17 urn:lsid:arphahub.com:pub:8E638694-B4E0-570A-856A-746FF325BF6B Research Ideas and Outcomes RIO 2367-7163 Pensoft Publishers 10.3897/rio.8.e87143 87143 19794 Grant Proposal Climate action Life on land Illuminating biodiversity changes in the ‘Black Box’ Phillips Helen R P helen.phillips@smu.ca https://orcid.org/0000-0002-7435-5934 1 2 3 4 Cameron Erin K. 1 Eisenhauer Nico https://orcid.org/0000-0002-0371-6720 3 4 Saint Mary's University, Halifax, Canada Saint Mary's University Halifax Canada NIOO-KNAW, Wageningen, Netherlands NIOO-KNAW Wageningen Netherlands German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Leipzig University, Leipzig, Germany Leipzig University Leipzig Germany

Corresponding author: Helen R P Phillips (helen.phillips@smu.ca).

Academic editor:

2022 31 08 2022 8 e87143 C57B3519-7B7A-57B4-9C0E-76FB98008DE6 Helen R P Phillips, Erin K. Cameron, Nico Eisenhauer This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Soil is often described as a ‘black box’, as surprisingly little is known about the high levels of biodiversity that reside there. For aboveground organisms, we have good knowledge of the distribution of the species and how they might change under future human impacts. Yet despite the fact that soil organisms provide a wide variety of ecosystem functions, we have very limited knowledge of their distribution and how their diversity might change in the future. In order to create accurate and generalisable models of biodiversity, the underlying data need to be representative of the entire globe. Yet even with our recently compiled global earthworm dataset of over 11000 sites, there are gaps across large regions. These gaps are consistent across many other datasets of both above- and belowground diversity. In order to fill the gaps we propose a sampling network (SoilFaUNa), to create a comprehensive database of soil macrofauna diversity and soil functions (e.g. decomposition rates). Building on the existing dataset of earthworm diversity and early data from the SoilFaUNa project, we will investigate changes in earthworm diversity. From our current work, we know that both climate and land use are main drivers in predicting earthworm diversity, but both will change under future scenarios and may alter ecosystem functions. We will, using space-for-time substitution models, estimate how earthworm diversity and their functions might change in the future, modelling earthworm diversity as a function of climate, land use and soil properties and predicting based on future scenarios. Previous studies of aboveground diversity changes over time using time-series analysis have found no-net-loss in richness, but analyses have criticisms. We aim to use time-series data on earthworms to move this debate forward, by using data and statistical methods that would address the criticisms, whilst increasing our knowledge on this understudied soil group. Field experiments and micro-/mesocosm experiments have been used to investigate the link between a number of soil organisms and ecosystem functions under few environmental conditions. Meta-analyses, which can produce generalisable results can only answer questions for which there are data. Thus, we have been lacking on information on the link between the entire community of soil fauna and ecosystem functions and impact of changes to the soil fauna community across environmental contexts. Using data collected from the SoilFaUNa project, we will, for the first time, synthesise globally distributed specifically-sampled data to model how changes in the community composition of soil macrofauna (due to changes in land use, climate or soil properties) impact the ecosystem functions in the soil.

 earthworms soil macrofauna soil functions ecosystem functions global biodiversity monitoring Deutsche Forschungsgemeinschaft 501100001659 http://doi.org/10.13039/501100001659 List of participants

Dr. George Brown, Dr. Ika Djukic, Dr. Carlos Guerra, Dr. Patrick Lavelle

 State of the art and preliminary work

Loss of biodiversity

The diversity of life on Earth is declining through species extinctions (Butchart et al. 2010, Dirzo et al. 2014) and reductions in population abundances (Butchart et al. 2010, Collen et al. 2009). Current species extinction rates are estimated to be orders of magnitude higher than the background rate in geological history from the fossil record (Pimm et al. 2014). Land-use change is one of the main drivers of this loss (Sala et al. 2000, Pereira et al. 2010) and unless measures are taken to manage these changes, rapid biodiversity loss is predicted to continue (Pereira et al. 2010, Tittensor et al. 2014). Biodiversity is also changing at local scales (Vellend et al. 2013, Dornelas et al. 2014, Phillips et al. 2017a, Blowes et al. 2019), but the magnitude and direction of this change is still being debated (Gonzalez et al. 2016, Vellend et al. 2017, Cardinale et al. 2018). Investigating biodiversity changes at the local scale is important as local biodiversity can be linked directly to ecosystem services and functioning (Cardinale et al. 2012, Hooper et al. 2012, Eisenhauer 2019).

Recent large-scale synthesis analyses have shown how local biodiversity (i.e. a measure of the ecological assemblage within a sampled plot, which can vary in size from metres to kilometres, sensu Newbold et al. 2015) is being lost as a result of anthropogenic disturbance, in particular land-use change (Newbold et al. 2015, Newbold et al. 2016, Phillips et al. 2017a) and habitat loss (Phillips et al. 2018). However, the data used in these large-scale synthesis analyses (Dornelas et al. 2014, Newbold et al. 2015) often have biases, both geographically and taxonomically (Phillips et al. 2017b, Cameron et al. 2018) and, in particular, are often heavily biased towards certain taxa, mainly plants, birds and some marine organisms. Soil biodiversity is very rarely considered in such large-scale synthesis (Phillips et al. 2017b), despite its importance to ecosystem functions and services (Bardgett and van der Putten 2014, Wall et al. 2015). Thus, soil is still regarded as a ‘black box’ in the context of biodiversity change (Phillips et al. 2017b). The amalgamation of data from a wide-range of taxa also means that the often-hypothesised link between biodiversity loss and ecosystem services cannot be tested, as, for the majority of taxa included, there is no quantitative relationship. However, focussing on single taxa groups where there is a known link to an ecosystem function or service they provide (e.g. earthworms and decomposition; Coleman et al. 2004) allows the relationship between changing diversity and the provision of ecosystem functions to be quantitatively estimated.

Earthworms as a model organism for biodiversity research

Organisms that are ecosystem engineers, those that create, modify or maintain habitats (Jones et al. 1994), such as earthworms (Lavelle et al. 1997), can have especially large impacts on ecosystem functions. Earthworms provide a variety of ecosystem functions and services that are critical for human well-being (Blouin et al. 2013), such as increasing crop production and aboveground biomass (van Groenigen et al. 2014), as well as regulating the carbon sink of the soil (Zhang et al. 2013). They can be categorised into three main ecological functional groups; litter dwellers (epigeics), soil feeders (endogeics) and deep-burrowers (anecics) (Bouché 1977). The contribution to the provisioning of ecosystem functions and services varies depending on the functional group of the earthworm species (Brown 1995, Eisenhauer 2010, Lubbers et al. 2013, Craven et al. 2017). Epigeic species are typically found in the upper layers of the soil and litter and are important in the first stages of physical breakdown of the litter layer (Brown 1995), increasing the surface area of the litter for microbial decomposition (Hättenschwiler et al. 2005). Endogeic species live in the upper mineral soil layers, creating horizontal burrows (Bouché 1977, Brown 1995), with most species creating both above- and belowground casts. These casts alter the structure (porosity and aggregation) of the soil, changing the water infiltration, run-off and water holding capacity (Ernst et al. 2009). Anecic species are the deep burrowing species, moving litter from the surface into the deeper layers of the soil, whilst moving mineral soil from the depths to the surface via their cast production (Edwards and Bohlen 1996).

The relatively large body size of earthworms compared to other soil taxa (Veresoglou et al. 2015) means that they are, to some degree, easy to sample and identify to species level and, thus, often more studied than other soil taxa. Yet, despite the large amounts of data (e.g. Phillips et al. 2019 recently collated nearly 200 datasets including > 11,000 sampling locations from across the globe), surprisingly little is known about the diversity of earthworms across large regions or globally (Hendrix et al. 2008, Cameron et al. 2016). Whereas many global distribution maps and diversity maps have been produced using aboveground taxa (e.g. Orme et al. 2005, Kreft and Jetz 2007, Roll et al. 2017), biogeographic studies of earthworms have only occurred across smaller regions (Rutgers et al. 2016) or at coarse resolutions (either spatially or taxonomically; Hendrix et al. 2008). In order to fill this gap, we are currently analysing spatial patterns of earthworm communities across the globe, using our large dataset of sampled earthworm diversity (Phillips et al. 2019).

The work that we are currently undertaking will further our understanding of the current distribution of earthworm diversity and the importance of different environmental drivers in shaping the communities (Phillips et al. 2019). However, we know that most environmental drivers will change as a result of anthropogenic impacts (Pereira et al. 2010). For example, future predictions indicate that agricultural land will increase as a result of the increasing human population (Lambin and Meyfroidt 2011) and precipitation will change as a result of climate change (IPCC 2014). Due to the importance of earthworms for ecosystem services, we need to fully understand how earthworm diversity might change under these future scenarios of change and how the ecosystem functions they provide might be altered in response. However, until data gaps have been filled, confidence in future projections of earthworm diversity will be low and our ability to answer important questions across large scales will be substantially limited.

Gaps in global datasets

For decades, papers have commented on the lack of biogeographic studies for soil fauna (Brussaard 1997, Rusek 1998, Eisenhauer et al. 2017). Yet despite the repeated calls, only now are biogeographic studies of soil organisms starting to appear on specific taxa (Tedersoo et al. 2014, Rutgers et al. 2016, Delgado-Baquerizo et al. 2018, Ramirez et al. 2018). One of the reasons why biogeographic studies may not have appeared earlier (unlike those of aboveground organisms, such as birds: Orme et al. 2005, plants: Kreft and Jetz 2007 and reptiles and amphibians: Roll et al. 2017), may be due to soil ecologists’ primary focus on local-scale research and aboveground macro-ecologists’ underestimation of the amount of data available. Thus, one of the ways to facilitate the inclusion of soil biodiversity data into macro-ecological studies would be the creation of a soil biodiversity database (Ramirez et al. 2015, Phillips et al. 2017b), which has already been done for many individual groups of taxa globally (e.g. ants: Dunn et al. 2007, plants: Kreft and Jetz 2007, bacteria: Delgado-Baquerizo et al. 2018) or for regions (e.g. Edaphobase for earthworms in Germany: Burkhardt et al. 2014). Increasing the ease of access to soil biodiversity data will likely result in these taxa being included in further analyses. For example, the TRY database, which collates functional traits of plants from across the globe Kattge et al. 2011, has now been used in over 150 further publications (https://www.try-db.org).

However, in all of the globally assembled databases (e.g. PREDICTS: Hudson et al. 2017, BioTIME: Dornelas et al. 2014, the Global Ant Database: Dunn et al. 2007 and the global databases of bacteria: Delgado-Baquerizo et al. 2018, Ramirez et al. 2018), there are regional gaps in data availability, with large regions of the tropics and Russia represented by minimal data. It is possible that limited sampling has occurred in these regions and it is highly likely that any data generated from these regions is being published in languages other than English (Amano et al. 2016). These gaps will result in at least two issues; firstly, geographic biases in the underlying data will increase the risk that results are not transferable across the entirety of the globe, especially if responses of biodiversity vary across different regions (Phillips et al. 2017a). Secondly, if the data do not encompass a large enough disturbance gradient, any changes in response to that disturbance may be under- or over-estimated (Elahi et al. 2015). For example, analysis of the sampled regions from the largest collation of earthworm data (Phillips et al. 2019), show how the 11,009 sites do not encompass areas that will experience some of the largest changes in climate, despite being within travelling distances of major settlements (Fig. 1a). Failing to capture major threats faced by earthworms, or any other organism, may result in inaccurate estimates of how biodiversity changes under these threats.

Such gaps in global datasets could be filled by developing a global network of researchers who apply standardised protocols across sites. Previously, standardised networks of experiments have been useful in addressing typically local-scale ecological questions across a global scale (e.g. NutNet: Borer et al. 2014, TreeDivNet: Verheyen et al. 2016, dummy caterpillars: Roslin et al. 2017). A standardised protocol ensures that data are easily comparable, with similar assumptions and biases (Borer et al. 2014). Furthermore, networks are also able to address questions not possible using meta-analytical approaches due to a lack of data in the primary literature (Fraser et al. 2013). Such schemes have been successful in the past, answering questions, such as the relationship between plant diversity and productivity (Borer et al. 2017), as well as creating new collaborative networks of researchers. For example, the TeaComposition project (Djukic et al. 2018) (based on the methodology of the Tea Bag Index; Keuskamp et al. 2013) enlisted over 200 researchers from across the globe in just over a year. Within such experimental as well as monitoring networks, recruitment of researchers can be targeted to certain areas to ensure the best possible geographical coverage of sites, thereby reducing geographic biases and resulting in more accurate models (Maestre and Eisenhauer 2019).

Projecting changes in earthworm diversity under future scenarios of change

With predicted future changes in anthropogenic pressures, such as climate change (IPCC 2014) and land use change (Sala et al. 2000, Lambin and Meyfroidt 2011), it is important that we understand how local biodiversity might respond and how this might impact ecosystem functions and services. For example, Newbold et al. 2015) provided some of the first global projections of changes in local biodiversity due to future land use change (with changes in land use calculated from projections of climate change and their mitigation scenarios; Hurtt et al. 2011). They showed that under the ‘Business-As-Usual’ scenario (MESSAGE8.5), local biodiversity is predicted to decline by an additional 7% by 2100 compared to estimates of the current day biodiversity levels.

There are few studies that use future scenarios to project changes in local biodiversity at global scales. Those that do are often limited to only one driver of change, for example, land use (Newbold et al. 2015) or climate (Garciá Molinos et al. 2016). However, impacts of climate and land use are likely to have interactive effects on biodiversity (Frishkoff et al. 2016). None of the studies has included any soil biodiversity data (Phillips et al. 2017b) and there has been no specific assessment of the effect of future changes on soil biodiversity, even for more well-studied taxa, such as earthworms. Therefore, it is unclear whether earthworms may respond particularly negatively to the conditions that may be present in these scenarios, despite their importance for ecosystem function and services (Blouin et al. 2013, van Groenigen et al. 2014).

Time series analysis

There is an ongoing debate surrounding the direction and magnitude of local biodiversity change, with some synthesis studies showing loss of local biodiversity (Newbold et al. 2015, Phillips et al. 2017a) and others showing ‘no-net-loss’ of diversity (Vellend et al. 2013, Dornelas et al. 2014, Hillebrand et al. 2018), but rather changes in community composition and structure (Dornelas et al. 2014, Hillebrand et al. 2018). However, the modelling approaches in these studies are very different. The ‘no-net-loss’ studies used time-series analysis (using data from sites that have been sampled more than once across multiple months or years), while the other studies used ‘space-for-time’ substitution (which assumes that spatially distributed sites differing in their disturbance will have the same biodiversity difference as sites that change their disturbance over time; e.g. Newbold et al. 2015, Phillips et al. 2017a). It is highly likely that these differing methodologies have led to the differences in the conclusions (De Palma et al. 2018).

Although analysis of time-series data is highly valuable for examining dynamic changes in local biodiversity, there have been criticisms of both the Vellend et al. 2013 and Dornelas et al. 2014 studies (Cardinale 2014, Gonzalez et al. 2016, Cardinale et al. 2018). These criticisms have focussed on several aspects of the modelling approach: 1) Spatial biases within the datasets used, with species-rich areas and areas under pressure from anthropogenic impacts being under-represented (Gonzalez et al. 2016). 2) Analysis of data without a reference (e.g. an undisturbed baseline; Eisenhauer et al. 2016). It is assumed that the start of the time-series contains diversity estimates similar to those found in the pristine conditions, which may not be true. This could explain the wide range of responses seen across the datasets, as over time, the diversity at some time-series could be recovering, whilst other time-series could be facing worsening pressures (Eisenhauer et al. 2016). 3) Investigating changes in temporal diversity outside of the context of external pressures, especially anthropogenic pressures (Cardinale 2014, Elahi et al. 2015, Cardinale et al. 2018). This may result in the average trends reported not being representative of biodiversity change across ecosystems globally (Elahi et al. 2015).

The synthesis of space-for-time data is often used as more data are available, as primary datasets require only one season of fieldwork (De Palma et al. 2018). Another advantage is that it is easy to relate the site level data to simultaneously occurring external pressures, such as land use or climate. Obtaining accurate data across multiple years can be problematic. However, there are criticisms of this approach. The underlying assumption is that spatial comparisons are a suitable substitute for temporal changes, which often is not true. Indeed, space-for-time studies may consistently underestimate biodiversity loss (França et al. 2016). Another issue with this approach is the inability to show dynamic changes in biodiversity, for example, recovery after a disturbance (Dunn 2004) or an extinction debt (Vellend et al. 2006).

Both techniques have downfalls and, consequently, an ideal solution is to combine the two approaches. Using time-series data to show dynamically how local biodiversity is changing, while addressing previous criticisms by incorporating baselines and information on external anthropogenic pressures, may move this debate forward. Given the amount of earthworm data available, and the fact that a time-series analysis has not been performed on any soil taxa, earthworms are an ideal study organism with which to combine these approaches.

Loss of ecosystem services

Soil biodiversity is a key driver of many vital ecosystem functions (Bardgett and van der Putten 2014). However, much of our understanding of the quantitative relationships between soil diversity and ecosystem functions comes from small-scale field studies or micro-/mesocosm experiments (Bradford et al. 2002, Heemsbergen et al. 2004, Wagg et al. 2014). Field (experimental and observational) studies typically focus on a single taxa group or trophic level (e.g. Eisenhauer et al. 2009) and ignore other groups. In contrast, micro-/mesocosm experiments often examine the effects of multiple taxonomic and functional groups on ecosystem functions (such as productivity; Bradford et al. 2002 and decomposition; Heemsbergen et al. 2004) and attempt to isolate the effects of individual taxa (Wagg et al. 2014). However, micro-/mesocosm studies may be unrealistic, as they often involve species-devoid communities, with manipulation treatments that are unlikely to occur in natural settings (Bradford et al. 2002). Therefore, results may not be generalisable from micro-/mesocosms and single, small-scale field studies to large scales.

As an alternative, meta-analysis can use globally distributed datasets to create more generalisable results (Koricheva et al. 2013). A disadvantage is that meta-analyses are limited by the number of available primary studies that investigate a similar question (Koricheva et al. 2013). This can often result in small sample sizes, whilst limiting the questions being asked. Previous meta-analyses have shown how earthworms can influence ecosystem services, such as increasing crop production and aboveground biomass (van Groenigen et al. 2014). However, due to the nature of the primary literature, these meta-analyses assume that changes in ecosystem functions are only caused by the presence or absence of earthworms and not other soil taxa. Moving the field forward may rely on establishing networks of researchers working towards common questions. For example, Wall et al. 2008 and de Vries et al. 2013 developed networks of researchers to investigate specific aspects of how soil community composition contributes to ecosystem functions.

Disturbances (a term used here to describe a site that differs from a ‘reference’ site, for example, a change in land use or soil properties and, therefore, not necessarily due to human impacts) are likely to change the composition of communities, but exactly how composition will change may vary. Groups of taxa may be lost or changes in abundance or biomass may occur (Hillebrand et al. 2008). These changes in the composition of the community will likely impact the level of the ecosystem functions provided: 1) As a reference, in an undisturbed area, the composition of the community supports a given level of ecosystem function (Fig. 2a; Fox and Kerr 2012). 2) Following a disturbance, all groups of taxa may decrease equally in abundance, reducing the total abundance of the community. Therefore, we might expect ecosystem function to decline (Fig. 2b; Fernández et al. 2015). 3) However, some taxa groups in the community may increase in abundance, rather than decrease. Thus, total community abundance remains equal, but the composition substantially changes (Cesarz et al. 2017). This could result in a reduction in ecosystem function if the dominant taxa group is unable to compensate for the lost abundance of the other taxa groups (Fig. 2c; Hunt and Wall 2002). 4) Alternatively, if the dominant taxa group can maintain the ecosystem function, no reduction would occur (Winfree et al. 2015, Abelho et al. 2016). This would indicate that the other groups were redundant in contributing towards the ecosystem function (Fig. 2d).

Despite its importance, very little is known about the biodiversity in the ‘black box’ (Phillips et al. 2017b). Although steps have been taken in attempts to show biogeographic patterns of some taxa, there are still gaps in our knowledge of how biodiversity might be changing due to the changing environment (Cameron et al. 2018). In addition, how changes in biodiversity might impact the provision of ecosystem functions that we heavily rely upon is unclear.

 Project-related publications

Articles published by outlets with scientific quality assurance, book publications and works accepted for publication, but not yet published.

Phillips HRP, Guerra CA,….and Eisenhauer N (2019) Global distribution of earthworm diversity. Science 366: 480–485

Phillips HRP, Cameron EK, Ferlian O, Türke M, Winter M, Eisenhauer N (2017) Red list of a black box. Nature Ecology and Evolution 1: article no 0103.

Cameron E, Martins IS, Lavelle P,… Phillips HRP,… Eisenhauer N (2018) Global gaps in soil biodiversity data. Nature Ecology and Evolution 2(7): 1042—1043.

Phillips HRP, Newbold T, Purvis A (2017) Land-use effects on local biodiversity in tropical forests vary between continents. Biodiversity and Conservation 26 :2251-2270.

Newbold T, Hudson LN, … Phillips HRP, …and Purvis A (2016) Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353: 288-291.

Newbold T, Hudson LN, … Phillips HRP, …and Purvis A (2015) Global effects of land use on local terrestrial biodiversity. Nature 520: 45-50.

Eisenhauer N, Dobies T, Cesarz S, Hobbie SE, Meyer RJ, Worm K, Reich PB (2013) Plant diversity effects on soil food webs are stronger than those of elevated CO2 and N deposition in a long-term grassland experiment. Proceedings of the National Academy of Sciences USA 110: 6889-6894.

Isbell F, Craven D, Connolly J, et al. and Eisenhauer N (2015) Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526: 574-577.

Reich PB, Tilman D, Isbell F, Mueller K, Hobbie SE, Flynn DFB and Eisenhauer N (2012) Impacts of biodiversity loss escalate through time as redundancy fades. Science 336: 589-592.

Schwarz B, Barnes AD, Thakur MP, Brose U, Ciobanu M, Reich PB, Rich RL, Rosenbaum B, Stefanski A and Eisenhauer N (2017) Warming alters energetic structure and function but not resilience of soil food webs. Nature Climate Change 7: 895-900.

 Objectives, concept and approach Objectives

Although there have been many small-scale studies on changes in soil biodiversity, there has been little progress in creating large-scale, generalisable results. This project aims to investigate changes in soil biodiversity (with a focus on earthworms, Order: Crassiclitellata), across large spatial scales using synthesis analyses. In addition, the project will also link soil biodiversity to soil functions and investigate how any changes in biodiversity might impact the ecosystem functions upon which we rely. Using previously collated data, as well as data collected specifically for this project and appropriate statistical methods that deal with the complexities of the data and the ecological questions, we will advance the field of global soil biodiversity.

Although soil biodiversity data are available across the globe the distribution is poor, especially within certain regions, and heavily biased towards certain realms and environmental conditions (Fig. 1). In order for any biodiversity model to be as transferable and accurate as possible, data need to be as representative of as much of the terrestrial realm, and all its environmental gradients, as possible. Therefore, we will be creating a network of researchers to collect data on soil biodiversity and soil ecosystem functions in a standardised, simple and cost-effective way (WP1). This collected data will be used in WP4, and if possible, WP2 and WP3.

WP2, 3 and 4 aim to further both our knowledge of the changes in soil biodiversity, particularly earthworms, whilst also furthering key, timely and highly relevant questions in the field of biodiversity change and ecosystem function. Previous synthesis analyses have shown how biodiversity is predicted to respond under future scenarios of change and have investigated whether biodiversity has been changing over time. However, these questions have been studied using datasets primarily composed of aboveground organisms. WP2 and 3 will modify the previously-used methods, to answer these questions in relation to earthworms. In addition, by modifying the methods previously used, we aim to further the field by addressing key questions that have not been addressed previously, such as whether local earthworm biodiversity is changing over time.

Providing this research is crucial, given that the understudied soil biodiversity is relied upon heavily for many ecosystem functions. In WP2, we aim to show how changes in earthworm diversity (as predicted based on future scenarios of global change) might affect the ecosystem functions that they provide. While, in WP4, we will further extend the biodiversity-ecosystem function field, by researching how multiple ecosystem functions provided by the soil might change when the soil community is altered, using specifically-collected datasets from non-manipulated biodiversity measurements in a globally-distributed network.

This project will be led by Dr Helen Phillips in the Experimental Interaction Ecology group of Prof. Nico Eisenhauer, at the University of Leipzig and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. Dr Helen Phillips was the lead post-doc (as part of a working group led by Prof. Nico Eisenhauer) collating data of earthworm diversity measures. This dataset now allows for a number of novel analyses.

 Anticipated total duration of the project

Current funding: none.

Duration: 3 years

 Work programme including proposed research methods

WP1 - Monitoring Scheme

Objectives

The number of research papers using synthesis approaches (i.e. collating raw data from previously published papers to conduct new analyses) to address patterns in biodiversity and questions on biodiversity change has recently increased (Dunn et al. 2007, Dornelas et al. 2014, Newbold et al. 2015, Phillips et al. 2017a, Delgado-Baquerizo et al. 2018, Ramirez et al. 2018, van den Hoogen et al. 2019). However, most have consistent gaps in where datasets are located (see Dunn et al. 2007, Dornelas et al. 2014, Hudson et al. 2017) -- usually lacking data from central Africa, boreal Asia and, to some extent, South America (Cameron et al. 2018). In addition, the datasets included in syntheses may not be equally distributed across the gradient of threats to biodiversity (Gonzalez et al. 2016) (Fig. 1A) or environmental conditions (Fig. 1B). For example, studies may be less common in regions that are facing large amounts of climate change (Fig. 1A). Until we have representative data, across regions and threats, our understanding of biodiversity change will remain limited.

We will create a network of researchers (the Soil biodiversity and Soil Function Network- SoilFaUNa) to collect soil biodiversity and soil functions data from regions that are typically understudied. The protocols used will be standardised and simple, where possible being based on protocols developed and agreed upon by soil experts within iSBio (https://home.uni-leipzig.de/idiv/isbio/; Heintz-Buschart et al. 2020). In addition, the protocol is cost-effective, enabling implementation with few resources (such as in the TeaComposition initiative; Djukic et al. 2018) in order to include geographic areas not included in previously established global sampling and monitoring networks. We will initially develop the network by re-contacting earthworm researchers we have previously worked with (~ 200 researchers, Phillips et al. 2019) to invite them to collect additional data. In a survey we previously conducted of 77 earthworm researchers, 89% indicated they would be willing to collect additional data for a global database. To find additional participants, we will locate universities within target regions and approach researchers with some expertise in soil ecology. Papers advertising the creation of the SoilFaUNa project will also be published at the start of the project, in continent-specific journals (e.g. African Journal of Ecology), through organisations (e.g. Global Soil Biodiversity Initiative) and other networks (e.g. TeaComposition network, ~ 200 researchers; Djukic et al. 2018), so that researchers can also get in contact with us. Data from this WP will feed into analyses in WP2, WP3 and WP4.

Methods

At the start of the project, we will re-contact the ~ 200 earthworm researchers we have previously worked with. We will ask researchers who have collected earthworm data in the past if they can resample the earthworm communities of their previously-sampled sites using their original methodology, in order to obtain more time-series datasets (suitable for analysis in WP3). They will also be informed about our new standardised sampling protocol (see below) and will be invited to start new sampling campaigns to collect data suitable for WP2 and WP4 (soil fauna and functions). We will also locate new soil ecologists to collaborate with, by searching through universities in target regions, for example across the tropics (i.e. Indonesia), central Africa (i.e. Tanzania), as well as boreal Asia (i.e. northern China). We believe that our advertising papers will also prompt soil ecologists to contact us. Nico Eisenhauer has successfully tested this approach recently in a global collection of soil microbial biomass data (Smith et al. 2020). Researchers who have not previously collected earthworm data will only be invited to use our standardised protocols.

To standardise sampling and assist researchers who may have limited experience in sampling soil fauna, an easily reproducible protocol has been drafted and will be distributed to all collaborating researchers (Suppl. material 1; SoilFaUNa Sampling Protocol). The protocol is based on methods suitable for both tropical and temperate regions (Anderson and Ingram 1993, ISO 2018) and will be tested and refined by the post doc and the international collaborators prior to be distributed to collaborating researchers. The remainder of this methods section refers only to our standardised protocols. To begin with, we are aiming for sampling at 200 new sites with our standardised protocols, which is manageable (in terms of lab processing) and achievable in the appropriate timeframes.

Collaborating researchers for the 200 new sites will be asked to sample earthworms, other soil macrofauna and soil functions at more than one site at least twice (at months 3 and 12 when teabags are collected; see below). Within each collaborating researcher’s sampling campaign, sites must be distributed across a range of current environmental conditions (e.g. land use, habitat cover, soil, climate), in the hope of increasing the environmental coverage of the database as a whole (Fig. 1b), and some information must be available on the history of the sites. Earthworm and soil macrofauna sampling will follow accepted protocols (Anderson and Ingram 1993, ISO 2018), using a handsorting approach (a block of 50 cm x 50 cm x 25 cm deep is excavated and soil macrofauna removed) followed by application of mustard solution into the created hole (full SoilFaUNa sampling protocol in Suppl. material 1). This combined approach has been shown to be the most accurate for obtaining a representative sample of earthworm communities at a site (Lawrence and Bowers 2002), whilst handsorting is also suitable for sampling a wide-range of soil macrofauna (ISO 2018). For each sample, the total abundance and fresh biomass of each soil fauna group (e.g. earthworms, carabid beetles, centipedes, millipedes etc.) will be measured. Counting and weighing organisms within fauna groups is similar to the approach used by the MacroFauna project (led by Patrick Lavelle), which successfully sampled soil macrofauna at over 2000 sites across the tropics, Europe and Australia (Orgiazzi et al. 2016, Cameron et al. 2018). Even when not identified to species level, earthworm data can still be incredibly useful, as density and biomass are critical indices (Bouché and Al-Addan 1997, Winfree et al. 2015) that will be useful to achieve our goals and advance the field. As further analysis will be focussed on earthworms (WP2, WP3, WP4), if the researcher has the experience (or access to region-specific taxonomic keys), earthworms will be identified to species or morphospecies. Where necessary, to help with differentiation of morpho-species, we hope to also create and distribute a simplified key of external characteristics (e.g. pigmentation, body size, clitellum type, setal pattern etc.) to collaborating researchers. The biomass and abundance of each earthworm (morpho-) species will then be calculated for each site, in addition to the total biomass and abundance.

Soil functions will also be measured at each site. To measure the belowground decomposition rate, the Tea Bag Index method will be used, which estimates decomposition rates by calculating weight differences in tea bags over time (see Keuskamp et al. 2013 for full protocol). In short, eight tea bags (4 x Lipton Green tea bags and 4 x Lipton Rooibos tea bags; provided by UNILEVER to ensure consistency) will be buried within the sampling site at the bottom the H horizon. The tea bags will be checked at 3, 12, 24 and 36 months. We will also ask for soil samples from each site to be sent to iDiv for analysis (100 g fresh weight). From each soil sample, we will measure soil pH, aggregate stability, microbial C, microbial respiration, metabolic quotient (qO2) (Eisenhauer et al. 2018), gravimetric soil water content and carbon:nitrogen ratio in the Eisenhauer laboratory that has a permit for processing and storing international soil samples. Following the iSBio protocols, we also hope to ensure consistency across samples by having the samples frozen prior to shipping (likely thawing during and post-flight) and by storing the samples for the same length of time between shipping and processing. This approach has been proven successful in previous global soil collections by the Eisenhauer lab (e.g. in Nutrient Network: Risch et al. 2019, in TreeDivNet: Cesarz et al. 2020).

All collaborating researchers will be sent a standardised data template, into which they will enter meta-data and biodiversity data and soil function data from each site sampled. This will ensure that all necessary data are collected and are stored in a standardised form, thereby decreasing the time needed to process it ready for analysis. In addition to our own global initiatives (e.g. Risch et al. 2019, Maestre and Eisenhauer 2019, Heintz-Buschart et al. 2020, Cesarz et al. 2020), we will build on the experience of Ika Djukic, who has coordinated the global TeaComposition project (Djukic et al. 2018) and has ample experience with obtaining relevant local data in a standardised way.

Mustard powder, teabags and containers for soil samples will be sent to all collaborating researchers, with the remaining costs of all fieldwork being covered by the collaborating researcher; however, protocols have been designed to use inexpensive methods/equipment. Collaborating researchers will be responsible for acquiring any permits needed for collecting and moving soil samples out of their country. We will obtain any permits that are needed to move soil samples into Germany (such as done in previous international, collaborative projects, e.g. Heintz-Buschart et al. 2020, Cesarz et al. 2020). We are aware that moving samples out of certain countries will not be possible. In these circumstances, we will work closely with and provide funding to soil scientists within the country, who would undertake all soil and biological processing. For example, Brazil has strong regulations regarding movement of material, but we have previously collaborated with Prof. Ademir Araújo who has agreed to undertake the processing when necessary, as done in previous collaborations (e.g. Araújo et al. 2014).

Data that are analysed at iDiv would be shared with the original collaborating researcher within the timeframe of the project. A subsample of 2 g of all soil samples would be stored at -80°C long term (at iDiv) to enable genetic analyses to be undertaken in the future. In addition, all biodiversity data would be entered into the Global Soil Biodiversity Database. We will ask all collaborating researchers to store biological samples for five years post sampling in 95% alcohol (see sampling protocol in Suppl. material 1). Stored samples will need to remain accessible, but can be at a place of the collaborating researchers choosing (i.e. in their lab collections or at local museums). Information about storage locations of reference samples will also enter the database. Collaborating researchers will be encouraged to publish analysis from their own biodiversity data, after they have collected and shared it with the SoilFaUNa project. In addition, they will be able to publish products using the analysed soil samples, once the data have been returned to them. In addition to being an author on other manuscripts when their data have been used in other WPs.

2.3.2 WP2 - Projecting changes in earthworm diversity under future scenarios of change

Objectives

From previous work, we are beginning to understand the large-scale spatial patterns of earthworm diversity in relation to current environmental conditions, such as soil properties, land use and climate (Phillips et al. 2019). However, human impacts are changing current conditions and, thus, the future state of many ecosystems will be altered (IPCC 2014, IPBES et al. 2016). It is vital that we understand how biodiversity will respond (Newbold et al. 2015) and especially how organisms that we rely upon for many ecosystem functions and services respond (e.g. earthworms, Blouin et al. 2013). Yet, we know surprisingly little about how soil organisms may respond to changing environmental conditions (Bardgett and van der Putten 2014).

We propose, using previously collected data (11,009 sites, Phillips et al. 2019), as well as data collected as part of WP1, to model how earthworm diversity may change in response to changing environmental variables using a space-for-time approach. Previous work has shown that earthworm diversity is low in agricultural land uses (over 20% reduction in species richness compared to the highest diversity land uses (Phillips et al. 2019) and with higher annual mean temperatures, both of which are projected to increase under future scenarios of change (Lambin and Meyfroidt 2011, IPCC 2014). Therefore, we hypothesise that local earthworm diversity will be reduced under these future scenarios of human impacts (WP2-H1). As earthworm functional groups vary in their contribution to ecosystem functions (Lubbers et al. 2013, van Groenigen et al. 2014) and may vary in their responses to a changing environment, we will also project how changes in earthworm community composition, with respect to functional group, may impact ecosystem functions.

Methods

For this WP, we will use previously-collected data (Fig. 1A, Phillips et al. 2019), as well as any data available from the early stages of WP1. We will also undertake a literature search and collate raw data from suitable recent studies. Once the dataset has been compiled, we will create models that predict earthworm diversity (biomass, abundance and diversity) using environmental variables and project how earthworm diversity may change under future scenarios of change.

For datasets to be suitable for this analysis, they will need to contain earthworm data from across more than two sites where current environmental conditions (e.g. land use, climate, soil properties) vary. The exact position of each site would also need to be known (from either GPS coordinates or by digitising and geolocation of available maps).

In order to create biodiversity projections using scenarios of future change (e.g. Hurtt et al. 2011, Riahi et al. 2017), mixed effects models will be constructed (using ‘lme4’ in R; Bates et al. 2015, R Core Team 2016) containing predictor variables encompassing environmental conditions considered in the future projections (e.g. temperature), but also environmental conditions that are important for earthworm diversity that have not been projected into the future (e.g. soil properties). For each site, some predictor variables will come from the original data collector (e.g. land use, with site descriptions classified into categories, based on the Representative Concentration Pathways harmonised land-uses; Hurtt et al. 2011) or from matching site coordinates to freely-available global data layers (e.g. annual mean temperature, available from CHELSA Climate; Karger et al. 2017). Models will be created for the three biodiversity metrics, species richness (the most commonly reported biodiversity measure), abundance (a metric increasingly being used in biodiversity studies; Winfree et al. 2015, Newbold et al. 2016) and biomass (which in earthworm communities can often be linked to ecosystem function: Bouché and Al-Addan 1997), as well as for the three main functional groups of earthworms (epigeics, endogeics and anecics). The models will then be used to predict earthworm diversity under future scenarios of change (e.g. RCPs, SSPs; Hurtt et al. 2011, Riahi et al. 2017) using variables from the global layers of future environmental conditions. In addition, we will use previously-published relationships between earthworm diversity and ecosystem functions (e.g. aboveground productivity; van Groenigen et al. 2014) to predict how the ecosystem function will change given the modelled reduction in earthworm diversity. This analysis will help us identify regions that may be particularly vulnerable to earthworm-induced change in ecosystem function, so they can receive more scientific attention and could be the focus of future monitoring campaigns.

2.3.3 WP3 - Time-series analysis

Objectives

Debate has continued over whether local biodiversity is declining (Gonzalez et al. 2016, Vellend et al. 2017, Cardinale et al. 2018). The debate has focused around two methodologies -- space-for-time and time-series analysis. However, both are not without their flaws (França et al. 2016, De Palma et al. 2018). To determine if local biodiversity is naturally changing over time or only as a direct result of human impacts and changing environmental conditions, long-term data are needed. Earthworms are an ideal study organism to help progress this debate. Although there are little data available on changes in soil biodiversity over time, some time-series data on earthworm biodiversity are available (currently 17 datasets compiled as part of Phillips et al. 2019, which encompass 457 sites, across 12 countries) and, due to being understudied, there is no prior expectation about how their diversity is changing.

In this WP3, we will use the time-series analysis approach similar to that used by Dornelas et al. 2014 but will incorporate changes into the statistical analysis proposed by Gonzalez et al. 2016 and De Palma et al. 2018. Specifically, we will investigate if local earthworm diversity (biomass, abundance and diversity) has changed over time and whether the response is consistent when land use (and other global change drivers) are accounted for. We hypothesise that any change in biodiversity will have been caused by environmental conditions and human impacts, and time will not explain any additional variation (WP3-H1).

Methods

Suitable datasets collected previously (Phillips et al. 2019) will be used in this analysis. In addition, a literature search will be undertaken to collate recent datasets. In order for data to be suitable for a time-series analysis, each dataset needs to contain multiple sites (which vary in their disturbance) that were sampled for earthworms on at least two occasions across different years, using a consistent methodology. As part of WP1, we will have contacted earthworm researchers asking them to resample their sites using their previous methodology. Any data from resampling will also be used in this analysis.

The earthworm samples from each sampled time will be temporally matched to both soil properties (measured either by researchers at the same time as sampling or from global data layers, such as SoilGrids; Hengl et al. 2017) and climate variables. Land use/habitat cover will be classified at each site, as it is known that earthworm diversity is affected by this (González et al. 1996, Didden 2001, Curry 2004, Feijoo et al. 2011).

Data will be analysed using a linear mixed effects model framework using ‘lme4’ in R. The random effect structure will account for differences between different datasets, such as sampling methodology, researcher error/biases, as well as differences from study location. Based on suggestions in De Palma et al. 2018 and approaches used by Soliveres et al. 2016, models will first be constructed with measures of diversity (species richness, biomass and abundance) as response variables, with all environmental variables (soil properties, climate) and human impacts (land use/habitat cover) as predictors. The residuals from the first model will then be modelled as a function of time, thus checking, after accounting for environment and human impact, whether the residuals are dependent on time (Freckleton 2002). If the residuals are dependent on time, then biodiversity is changing over time more than can be explained by human impacts and other environmental variables. Additional analysis could include modelling time with all other variables, to obtain parameter estimates, as well as focusing on how time-series length affects the results, as it is expected that time-series length will vary greatly, with most datasets sampling either twice over a couple of years or twice over many years.

We will also investigate alternative modelling approaches. One potential alternative will be the use of structural equation models (SEMs; Grace 2008, Eisenhauer et al. 2015, Shipley 2016), thereby investigating the direct effect of time on earthworm diversity as opposed to the direct and indirect effects of other variables (climate, soil properties and human impacts, for example, land use; Fig. 3). Model specification will depend on data availability, as SEMs will need to be fitted to each dataset and modelling implemented in the R package ‘piecewiseSEM’ (Lefcheck 2016) which is capable of handling complex SEM structures, including composite variables. As the direction of the pathways are not always clear (i.e. the relationship between climate and land use; Fig. 3), multiple structures will be tested.

2.3.4 WP4 - Ecosystem functions

Objectives

Soil biodiversity is important for ecosystem functions and, consequently, changes in soil biodiversity may impact functions (e.g. Bradford et al. 2002, Bardgett and van der Putten 2014). For example, reductions in decomposer diversity were associated with decreased decomposition and nutrient cycling (Heemsbergen et al. 2004, Handa et al. 2014). However, it is unclear how soil community composition changes with disturbances and consequently how any changes may impact ecosystem functions. Field and micro-/mesocosm experiments that examine changes in diversity and ecosystem function in response to disturbances often do not study the entire community. Therefore, the conclusions that can be drawn, in terms of how the soil responds to disturbance and how taxa contribute to ecosystem function, are limited to a subset of the community. The lack of primary literature means that we are unable to address this question across large scales or in a generalisable way.

In this WP, we will use specifically collected data on the soil community and ecosystem functions (from WP1) to investigate how soil communities change with disturbance and how ecosystem functions change as a result. We will investigate the following hypotheses (see Fig. 2): In a disturbed site, relative to an undisturbed site, all soil taxa groups are negatively impacted (decrease in community abundance) and ecosystem function is reduced (WP4-H1). Alternatively, not all soil taxa groups in a disturbed site will be negatively impacted (change in community composition) and ecosystem function is changed (WP4-H2). A third option is that in a disturbed site not all soil taxa groups are negatively impacted (change in community composition) but ecosystem function is maintained (WP4-H3).

Methods

Data will be collated on soil fauna and soil functions through the establishment of the global monitoring network in WP1. These data, collected using a standardised sampling protocol from sites distributed across the globe, will be analysed as part of this WP. A literature search will be performed to obtain recent published studies that meet all criteria. In addition, we are also aware of one large dataset (Biodiversity Exploratories) that has collected suitable data from across 150 sites in Germany. We will ask for access to this dataset to add to the analysis.

Suitable datasets will include data from two or more sites, which vary in their environmental conditions (e.g. land use, habitat cover, soil properties, climate). Each dataset will contain the sampled diversity (biomass and abundance) of soil macrofauna at the order-level (e.g. earthworms, beetles, centipedes/millipedes), as well as the biomass/abundance/diversity of the earthworm community. In addition, soil functions (decomposition, soil pH, aggregate stability, microbial C, microbial respiration, metabolic quotient (qO2)) at each site will also have been measured (discussed in WP1). As the coordinates of each site will be known, external data layers (such as SoilGrids and CHELSA climate data) can also be included in the analysis.

Although all the data will be standardised across the datasets, mixed effect models will be tested in case differences between datasets that may have arisen due to differences amongst multiple data collectors need to be accounted for. Models will be constructed for each ecosystem function. Predictor variables will include environmental variables that were collected in situ, such as land use and habitat cover, as well as those acquired from global layers that may explain additional variance within the data, such as altitude.

As little is known about how the diversity and composition of soil communities will change across a range of environmental conditions, such as different land uses, habitat covers and climates, we will first investigate this using the collated dataset. Diversity measures of the soil community, such as biomass, abundance and composition (discussed below), will be used as response variables in the models, with information on the environmental conditions at each site used as predictor variables.

To test whether changes in composition affect ecosystem functions, models will also contain the community composition of the soil fauna at the site as a predictor variable. The suitability of community composition metrics for the analysis will be investigated, to ensure that the abundance or biomass of each taxonomic order is clearly captured. For example, Simpson’s Evenness (Magurran 2004) would be a possibility, as it calculates the evenness in terms of abundance/biomass across all species or orders in a sample whilst removing biases created when samples have differing number of species. Sites from different regions will inevitably have different numbers of orders, so the diversity measures used will need to account for this.

The ecosystem functions that we are measuring may not be strongly impacted by all of the macrofauna groups sampled. Earthworms are known to impact some of the ecosystem functions measured, for example, decomposition, microbial activity, potentially more so than the other soil fauna groups that may be sampled. Therefore, models will also be created using earthworm diversity and the composition of the earthworm functional groups as the predictor variables (along with predictor variables mentioned previously) of the ecosystem functions. Further investigations into the effect of different soil fauna orders can then be done post hoc if needed.

Deliverables

WP1: At least one paper will be written to highlight and promote the creation of this monitoring network. In order to increase the participation in the under-represented countries, the paper will be submitted to a continent-specific, peer-reviewed journal. A more general paper, detailing the creation of the network and the subsequent database, will also be submitted on completion of the project to an international, peer-reviewed journal.

WP2: This synthesis analysis will allow us to write at least one paper in an international, peer-reviewed journal on the changes in earthworm communities as a response to human impacts.

WP3: This synthesis analysis will result in at least one paper in an international, peer-reviewed journal showing how earthworm communities are changing over time, adding to the existing debate on whether local diversity is changing over time.

WP4: This work package will result in at least one paper in an international, peer-reviewed journal. The paper will focus on using a collaborative network of researchers to address how soil biodiversity is changing in response to disturbance and the effects on the ecosystem functions provided. All collaborating researchers will be invited to be an author on the paper.

 Data handling

All data will be made publicly available with the respective publication using common data repositories, such as Dryad (http://datadryad.org/) and Pangaea (https://www.pangaea.de/), and assigned a DOI. Further, all data will be submitted to the Global Soil Biodiversity Database.

 Additional information

Protocols for WP1 (See Supplementary Materials)

 Project requirements Employment status information

Phillips, Helen, Post-Doctoral Researcher, (currently based at the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany, (DFG FZT 118))

Eisenhauer, Nico, Full Professor (W3), permanent

 First-time proposal data

Phillips, Helen

German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany

 Composition of the project group

Dr. Simone Cesarz, permanent post-doc in the Eisenhauer lab, specialist in soil chemical analyses. She will provide additional support and expertise for laboratory work. Anja Zeuner, technician within the Eisenhauer lab. She will conduct some of the laboratory work and provide assistance in the lab, if needed. Svenja Haenzel, Foreign Language Secretary in the Eisenhauer lab, will support the project by helping with contracting the student helper and with providing the necessary shipping documents and permits.

 Cooperation with other researchers

Researchers with whom you have agreed to cooperate on this project

Dr. Ika Djukic, leads the global TeaComposition project (Djukic et al. 2018) and has expertise in soil microbial diversity and functions. Dr. Carlos Guerra currently leading an iDiv project collecting global soil samples from researchers in the TeaComposition project. In addition, expertise in future scenarios of global change and global biodiversity policy. Dr. George Brown, expertise in earthworm ecology and taxonomy, large database on tropical earthworms and important contact point in South America (Brazil). Dr. Patrick Lavelle, expertise in soil biodiversity and ecosystem function, leads global database on soil macrofauna with > 2,000 sampling locations (Orgiazzi et al. 2016, Cameron et al. 2018).

Researchers with whom you have collaborated scientifically within the past three years

Prof. Andy Purvis and all members of the PREDICTS project (http://www.predicts.org.uk/pages/team.html). Dr. Nicholas Fisichelli, Dr. Lee Frelich, Prof. Sarah Hobbie, Dr. Forest Isbell, Prof. Dr. Edward Johnson, Dr. Pete Manning, Prof. Peter Reich, Prof. Matthias Rillig, Prof. Jacques Roy, Prof. David Tilman, Prof. Wim van der Putten, Prof. Alexandra Wright, all PIs of iDiv (https://www.idiv.de/groups_and_people/members.html) and the Jena Experiment (http://www.the-jena-experiment.de/Members.html) and all editors of Pedobiologia – Journal of Soil Ecology.

 Scientific equipment

A laboratory, with all necessary equipment for WP1, is available at iDiv (within the Experimental Interaction Ecology group led by Prof. Nico Eisenhauer). In addition, iDiv provides a high-performance computer cluster (HPC) and highly skilled IT support staff, that would be available should the analysis in WP2-4 require additional computational power. iDiv would provide the perfect infrastructure for the present project and no further equipment is requested.

 References Abelho Manuela Martins Tiago Fortunato Shinn Cândida Moreira-Santos Matilde Ribeiro Rui 2016 Effects of the fungicide pyrimethanil on biofilm and organic matter processing in outdoor lentic mesocosms Ecotoxicology 25 1 121 131 10.1007/s10646-015-1574-x Amano Tatsuya González-Varo Juan P Sutherland William J 2016 Languages Are Still a Major Barrier to Global Science PLOS Biology 14 12 10.1371/journal.pbio.2000933 Anderson Jonathan Michael Ingram J. S. I. 1993 Tropical Soil Biology and Fertility: A handbook of methods Tropical Soil Biology and Fertility: A handbook of methods 2 Ed. 88 91 10.1017/S0014479700018354 Araújo Ademir Sérgio Ferreira Borges Clovis Daniel Tsai Siu Mui Cesarz Simone Eisenhauer Nico 2014 Soil bacterial diversity in degraded and restored lands of Northeast Brazil Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology 106 5 891 899 10.1007/s10482-014-0258-5 Bardgett R D van der Putten W H 2014 Belowground biodiversity and ecosystem functioning Nature 515 505 7528 505 511 10.1038/nature13855 Bates Douglas Mächler Martin Bolker Ben Walker Steve 2015 Fitting Linear Mixed-Effects Models Using lme4 Journal of Statistical Software 67 1 1 48 10.18637/jss.v067.i01 Blouin M Hodson M E Delgado E A Baker G Brussaard L Butt K R Dai J Dendooven L Peres G Tondoh J E Cluzeau D Brun J J 2013 A review of earthworm impact on soil function and ecosystem services European Journal of Soil Science 64 2 161 182 10.1111/ejss.12025 Blowes Shane A. Supp Sarah R. Antão Laura H. Bates Amanda Bruelheide Helge Chase Jonathan M. Moyes Faye Magurran Anne McGill Brian Myers-Smith Isla H. Winter Marten Bjorkman Anne D. Bowler Diana E. Byrnes Jarrett E. K. Gonzalez Andrew Hines Jes Isbell Forest Jones Holly P. Navarro Laetitia M. Thompson Patrick L. Vellend Mark Waldock Conor Dornelas Maria 2019 The geography of biodiversity change in marine and terrestrial assemblages Science 366 6463 339 345 10.1126/science.aaw1620 Borer Elizabeth T Harpole W Stanley Adler Peter B Lind Eric M Orrock John L Seabloom Eric W Smith Melinda D 2014 Finding generality in ecology: A model for globally distributed experiments Methods in Ecology and Evolution 5 1 65 73 10.1111/2041-210X.12125 Borer Elizabeth T Grace James B Harpole W Stanley MacDougall Andrew S Seabloom Eric W 2017 A decade of insights into grassland ecosystem responses to global environmental change Nature Ecology & Evolution 1 5 10.1038/s41559-017-0118 Bouché Marcel B. Al-Addan Fathel 1997 Earthworms, water infiltration and soil stability: Some new assessments Soil Biology and Biochemistry 29 3-4 441 452 10.1016/S0038-0717(96)00272-6 Bouché M B 1977 Strategies lombriciennes Ecological Bulletins 25 122 132 Bradford M A Jones T H Bardgett R D Black H I J Boag B Bonkowski M Cook R Eggers T Gange A C Grayston S J Kandeler E McCaig A E Newington J E Prosser J I Setala H Staddon P L Tordoff G M Tscherko D Lawton J H 2002 Impacts of Soil Faunal Community Composition on Model Grassland Ecosystems Science 298 615 618 10.1126/science.1075805 Brown George G. 1995 How do earthworms affect microfloral and faunal community diversity? Plant and Soil 170 1 209 231 10.1007/BF02183068 Brussaard L 1997 Biodiversity and ecosystem functioning in soil Ambio: A Journal of the Human Environment 26 8 563 570 10.2307/1313535 Burkhardt U. Russell D. J. Decker P. Döhler M. Höfer H. Lesch S. Rick S. Römbke J. Trog C. Vorwald J. Wurst E. Xylander W. E R 2014 The Edaphobase project of GBIF-Germany-A new online soil-zoological data warehouse Applied Soil Ecology 83 3 12 10.1016/j.apsoil.2014.03.021 Butchart Stuart H M Walpole Matt Collen Ben Van Strien Arco Scharlemann Jörn P W Almond Rosamunde E A Baillie Jonathan E M Bomhard Bastian Brown Claire Bruno John 2010 Global biodiversity: indicators of recent declines Science 328 5982 1164 1168 10.1126/science.1187512 Cameron Erin K. Martins Inês S. Lavelle Patrick Mathieu Jérôme Tedersoo Leho Gottschall Felix Guerra Carlos A. Hines Jes Patoine Guillaume Siebert Julia Winter Marten Cesarz Simone Delgado-Baquerizo Manuel Ferlian Olga Fierer Noah Kreft Holger Lovejoy Thomas E. Montanarella Luca Orgiazzi Alberto Pereira Henrique M. Phillips Helen R. P. Settele Josef Wall Diana H. Eisenhauer Nico 2018 Global gaps in soil biodiversity data Nature Ecology & Evolution in press 6 7 10.1038/s41559-018-0573-8 Cameron Erin K Decaens Thibaud Lapied Emmanuel Porco David Eisenhauer Nico 2016 Earthworm databases and ecological theory: Synthesis of current initiatives and main research directions Applied Soil Ecology 104 August 85 90 10.1016/j.apsoil.2015.11.012 Cardinale Bradley 2014 Overlooked local biodiversity loss Science 344 6188 Cardinale Bradley J. Gonzalez Andrew Allington Ginger R. H. Loreau Michel 2018 Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends Biological Conservation 219 175 183 10.1016/j.biocon.2017.12.021 Cardinale Bradley J Duffy J Emmett Gonzalez Andrew Hooper David U Perrings Charles Venail Patrick Narwani Anita Mace Georgina M Tilman David Wardle David A 2012 Biodiversity loss and its impact on humanity Nature 486 7401 59 67 10.1038/nature11148 Cesarz Simone Ciobanu Marcel Wright Alexandra J Ebeling Anne Vogel Anja Weisser Wolfgang W Eisenhauer Nico 2017 Plant species richness sustains higher trophic levels of soil nematode communities after consecutive environmental perturbations Oecologia 184 3 715 728 10.1007/s00442-017-3893-5 Cesarz Simone Craven Dylan Auge Harald Bruelheide Helge Castagneyrol Bastien Hector Andy Jactel Hervé Koricheva Julia Messier Christian Muys Bart O’Brien Michael J Paquette Alain Ponette Quentin Potvin Catherine Reich Peter B Scherer-Lorenzen Michael Smith Andrew R Verheyen Kris Eisenhauer Nico 2020 Biotic and abiotic drivers of soil microbial functions across tree diversity experiments bioRxiv 10.1101/2020.01.30.927277 Coleman David C. Crossley D. A. Hendrix Paul F. 2004 Fundamentals of Soil Ecology Elsevier 27 978-0-12-179726-3 10.1016/B978-012179726-3/50009-5 Collen Ben Loh Jonathan Whitmee Sarah McRae Louise Amin Rajan Baillie Jonathan E M 2009 Monitoring change in vertebrate abundance: the Living Planet Index Conservation Biology 23 2 317 327 10.1111/j.1523-1739.2008.01117.x Craven Dylan Thakur Madhav P. Cameron Erin K. Frelich Lee E. Beaus�jour Robin Blair Robert B. Blossey Bernd Burtis James Choi Amy D�valos Andrea Fahey Timothy J. Fisichelli Nicholas A. Gibson Kevin Handa I. Tanya Hopfensperger Kristine Loss Scott R. Nuzzo Victoria Maerz John C. Sackett Tara Scharenbroch Bryant C. Smith Sandy M. Vellend Mark Umek Lauren G. Eisenhauer Nico 2017 The unseen invaders: introduced earthworms as drivers of change in plant communities in North American forests (a meta-analysis) Global Change Biology 23 3 1065 1074 10.1111/gcb.13446 Curry J P 2004 Factors affecting the abundance of earthworms in soils Edwards Clive A. Earthworm Ecology 22 1420039717 10.1201/9781420039719.pt3 Delgado-Baquerizo Manuel Oliverio Angela M Brewer Tess E Benavent-González Alberto Eldridge David J Bardgett Richard D Maestre Fernando T Singh Brajesh K Fierer Noah 2018 A global atlas of the dominant bacteria found in soil Science 359 6373 320 325 10.1126/science.aap9516 De Palma Adriana Sanchez-Ortiz Katia Martin Philip A Chadwick Amy Gilbert Guillermo Bates Amanda E Börger Luca Contu Sara Hill Samantha L L Purvis Andy 2018 Challenges With Inferring How Land-Use Affects Terrestrial Biodiversity: Study Design, Time, Space and Synthesis - Next Generation Biomonitoring: Part 1 Bohan David A. Next Generation Biomonitoring: Part 1 36 10.1016/bs.aecr.2017.12.004 de Vries Franciska T Thébault Elisa Liiri Mira Birkhofer Klaus Tsiafouli Maria A Bjørnlund Lisa 2013 Soil food web properties explain ecosystem services across European land use systems PNAS 110 35 14296 14301 10.1073/pnas.1305198110 Didden W. A.M. 2001 Earthworm communities in grasslands and horticultural soils Biology and Fertility of Soils 33 2 111 117 10.1007/s003740000297 Dirzo Rodolfo Young Hillary S Galetti Mauro Ceballos Gerardo Isaac Nick J B Collen Ben 2014 Defaunation in the Anthropocene Science 345 6195 401 406 10.1126/science.1251817 Djukic Ika Kepfer-Rojas Sebastian Schmidt Inger Kappel Larsen Klaus Steenberg Beier Claus Berg Björn Verheyen Kris 2018 Early stage litter decomposition across biomes Science of Total Environment 629 1369 1394 10.1016/j.scitotenv.2018.01.012 Dornelas Maria Gotelli Nicholas J McGill Brian Shimadzu Hideyasu Moyes Faye Sievers Caya Magurran Anne E 2014 Assemblage time series reveal biodiversity change but not systematic loss Science 344 6181 296 299 10.1126/science.1248484 Dunn Robert R 2004 Recovery of faunal communities during tropical forest regeneration Conservation Biology 18 2 302 309 10.1111/j.1523-1739.2004.00151.x Dunn R R Sanders Nathan J Fitzpatrick Matthew C Laurent Ed Lessard Jean-Philippe Agosti Donat A Ndersen Alan N A Ruhl Carsten B Erda Xim C Llison Aaron M E Isher Brian L F Ibb Heloise G Otelli Nicholas J G Ove Aaron G Uenard Benoit G Anda Milan J Aspari Michael K Ongino John T L Ajer Jonathan M Lynn Terrence P M C G Enke Sean B M Arr Catherine L P Hilpott M P Feiffer Martin P Etana Javier R Uarez Andrew V S Asconcelos V Heraldo L 2007 Global ant (Hymenoptera : Formicidae) biodiversity and biogeography – a new database and its possibilities Myrmecological News 10 September 77 83 Edwards C. A. Bohlen P J 1996 Biology and ecology of earthworms. Third edition Springer 0412561603 Eisenhauer Nico Milcu Alexandru Nitschke Norma Sabais Alexander C. W. Scherber Christoph Scheu Stefan 2009 Earthworm and belowground competition effects on plant productivity in a plant diversity gradient Oecologia 161 2 291 301 10.1007/s00442-009-1374-1 Eisenhauer Nico 2010 The action of an animal ecosystem engineer: Identification of the main mechanisms of earthworm impacts on soil microarthropods Pedobiologia 53 6 343 352 10.1016/j.pedobi.2010.04.003 Eisenhauer Nico Bowker Matthew A Grace James B Powell Jeff R 2015 From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology Pedobiologia 58 2-3 65 72 10.1016/j.pedobi.2015.03.002 Eisenhauer Nico Barnes Andrew D Cesarz Simone Craven Dylan Ferlian Olga Gottschall Felix Hines Jes Sendek Agnieszka Siebert Julia Thakur Madhav P Türke Manfred 2016 Biodiversity-ecosystem function experiments reveal the mechanisms underlying the consequences of biodiversity change in real world ecosystems Journal of Vegetation Science 27 1061 1070 10.1111/jvs.12435 Eisenhauer Nico Antunes Pedro M. Bennett Alison E. Birkhofer Klaus Bissett Andrew Bowker Matthew A. Caruso Tancredi Chen Baodong Coleman David C. Boer Wietse de Ruiter Peter de DeLuca Thomas H. Frati Francesco Griffiths Bryan S. Hart Miranda M. Hättenschwiler Stephan Haimi Jari Heethoff Michael Kaneko Nobuhiro Kelly Laura C. Leinaas Hans Petter Lindo Zoë Macdonald Catriona Rillig Matthias C. Ruess Liliane Scheu Stefan Schmidt Olaf Seastedt Timothy R. Straalen Nico M. van Tiunov Alexei V. Zimmer Martin Powell Jeff R. 2017 Priorities for research in soil ecology Pedobiologia 63 1 7 10.1016/j.pedobi.2017.05.003 Eisenhauer Nico Hines Jes Isbell Forest van der Plas Fons Hobbie Sarah E. Kazanski Clare E. Lehmann Anika Liu Mengyun Lochner Alfred Rillig Matthias C. Vogel Anja Worm Kally Reich Peter B. 2018 Plant diversity maintains multiple soil functions in future environments eLife 10.7554/eLife.41228 Eisenhauer Nico 2019 Mechanisms Underlying the Relationship Between Biodiversity and Ecosystem Function Academic Press 10.1016/S0065-2504(19)30044-3 Elahi Robin O'Connor Mary I. Byrnes Jarrett E. K. Dunic Jillian Eriksson Britas Klemens Hensel Marc J. S. Kearns Patrick J. 2015 Recent Trends in Local-Scale Marine Biodiversity Reflect Community Structure and Human Impacts Current Biology 25 14 1938 1943 10.1016/j.cub.2015.05.030 Ernst Gregor Felten Daniel Vohland Michael Emmerling Christoph 2009 European Journal of Soil Biology Impact of ecologically different earthworm species on soil water characteristics European Journal of Soil Biology 45 3 207 213 10.1016/j.ejsobi.2009.01.001 Feijoo Alexander Carvajal Andrés F. Zúñiga María C. Quintero Heimar Fragoso Carlos 2011 Diversity and abundance of earthworms in land use systems in central-western Colombia Pedobiologia 54 SUPPL. 10.1016/j.pedobi.2011.09.016 Fernández Diego Voss Katharina Bundschuh Mirco Zubrod Jochen P Schäfer Ralf B 2015 Effects of fungicides on decomposer communities and litter decomposition in vineyard streams Science of the Total Environment 533 40 48 10.1016/j.scitotenv.2015.06.090 Fox Jeremy W. Kerr Benjamin 2012 Analyzing the effects of species gain and loss on ecosystem function using the extended Price equation partition Oikos 121 2 290 298 10.1111/j.1600-0706.2011.19656.x França Filipe Louzada Julio Korasaki Vanesca Griffiths Hannah Silveira Juliana M Barlow Jos 2016 Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles Journal of Applied Ecology 53 4 1098 1105 10.1111/1365-2664.12657 Fraser Lauchlan H Henry Al Hugh Carlyle Cameron N White Shannon R Beierkuhnlein Carl Cahill James F Casper Brenda B Cleland Elsa Collins Scott L Dukes Jeffrey S Knapp Alan K Lind Eric Long Ruijun Luo Yiqi Reich Peter B Smith Melinda D Sternberg Marcelo Turkington Roy 2013 Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science Frontiers in Ecology and the Environment 11 3 147 155 10.1890/110279 Freckleton Robert P 2002 On the misuse of residuals in ecology: regression of residuals vs. multiple regression Journal of Animal Ecology 71 3 542 545 10.1046/j.1365-2656.2002.00618.x Frishkoff Luke O. Karp Daniel S. Flanders Jon R. Zook Jim Hadly Elizabeth A. Daily Gretchen C. M'Gonigle Leithen K. 2016 Climate change and habitat conversion favour the same species Ecology letters 19 9 1081 1090 10.1111/ele.12645 Garciá Molinos Jorge Halpern Benjamin S. Schoeman David S. Brown Christopher J. Kiessling Wolfgang Moore Pippa J. Pandolfi John M. Poloczanska Elvira S. Richardson Anthony J. Burrows Michael T. 2016 Climate velocity and the future global redistribution of marine biodiversity Nature Climate Change 6 1 83 88 10.1038/nclimate2769 Gonzalez Andrew Cardinale Bradley J Allington Ginger R H Byrnes Jarrett Arthur Endsley K Brown Daniel G Hooper David U Isbell Forest O'Connor Mary I Loreau Michel 2016 Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity Ecology 97 8 1949 1960 10.1890/15-1759.1 González Grizelle Zou Xiaoming Borges Sonia 1996 Earthworm abundance and species composition in abandoned tropical croplands: comparisons of tree plantations and secondary forests Pedobiologia 40 385 391 10.1007/s10530-006-9023-7 Grace James B 2008 Structural Equation Modeling and Natural Systems Cambridge University Press 10.1086/586991 Grace James B Schoolmaster Donald R Guntenspergen Glenn R Little Amanda M Mitchell Brian R Miller Kathryn M Schweiger E William 2012 Guidelines for a graph-theoretic implementation of structural equation modeling Ecosphere 3 8 1 44 10.1890/ES12-00048.1 Handa I Tanya Aerts Rien Berendse Frank Berg Matty P Bruder Andreas Butenschoen Olaf Chauvet Eric Gessner Mark O Jabiol Jérémy Makkonen Marika McKie Brendan G Malmqvist Björn Peeters Edwin T H M Scheu Stefan Schmid Bernhard van Ruijven Jasper Vos Veronique C A Hättenschwiler Stephan 2014 Consequences of biodiversity loss for litter decomposition across biomes Nature 509 7499 218 221 10.1038/nature13247 Hättenschwiler Stephan Tiunov Alexei V. Scheu Stefan 2005 Biodiversity and Litter Decomposition in Terrestrial Ecosystems Annual Review of Ecology, Evolution, and Systematics 36 1 191 218 10.1146/annurev.ecolsys.36.112904.151932 Heemsbergen D. A. Berg M. P. Loreau M. Hal J. R. van Faber J. H. Verhoef H. A. 2004 Biodiversity Effects on Soil Processes Explained by Interspecific Functional Dissimilarity Science 306 10 8 10 10.1126/science.1101865 Heintz-Buschart Anna Guerra Carlos Djukic Ika Cesarz Simone Chatzinotas Antonis Patoine Guillaume Sikorski Johannes Buscot Francois Küsel Kirsten Wegner Carl-Eric Eisenhauer Nico 2020 Microbial diversity-ecosystem function relationships across environmental gradients Research Ideas and Outcomes 6 10.3897/rio.6.e52217 Hendrix Paul F. Callaham Mac A. Drake John M. Huang Ching-Yu James Sam W. Snyder Bruce A. Zhang Weixin 2008 Pandora's Box Contained Bait: The Global Problem of Introduced Earthworms Annual Review of Ecology, Evolution, and Systematics 39 1 593 613 10.1146/annurev.ecolsys.39.110707.173426 Hengl Tomislav Mendes de Jesus Jorge Heuvelink Gerard B. M. Ruiperez Gonzalez Maria Kilibarda Milan Blagotić Aleksandar Shangguan Wei Wright Marvin N. Geng Xiaoyuan Bauer-Marschallinger Bernhard Guevara Mario Antonio Vargas Rodrigo MacMillan Robert A. Batjes Niels H. Leenaars Johan G. B. Ribeiro Eloi Wheeler Ichsani Mantel Stephan Kempen Bas 2017 SoilGrids250m: Global gridded soil information based on machine learning PLOS ONE 12 2 10.1371/journal.pone.0169748 Hillebrand Helmut Bennett Danuta M Cadotte Marc W 2008 Consequences of dominance: a review of evenness effects on local and regional ecosystem processes Ecology 89 6 1510 1520 10.1890/07-1053.1 Hillebrand Helmut Blasius Bernd Borer Elizabeth T Chase Jonathan M Downing John A Eriksson Britas Klemens Filstrup Christopher T Harpole W Stanley Hodapp Dorothee Larsen Stefano Lewandowska Aleksandra M Seabloom Eric W Van de Waal Dedmer B Ryabov Alexey B 2018 Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring Journal of Applied Ecology 55 1 169 184 10.1111/1365-2664.12959 Hooper David U Adair E Carol Cardinale Bradley J Byrnes Jarrett E K Hungate Bruce A Matulich Kristin L Gonzalez Andrew Duffy J Emmett Gamfeldt Lars O’Connor Mary I 2012 A global synthesis reveals biodiversity loss as a major driver of ecosystem change Nature 486 7401 105 108 10.1038/nature11118 Hudson Lawrence N. Newbold Tim Contu Sara Hill Samantha L. L. Lysenko Igor De Palma Adriana Phillips Helen R. P. Alhusseini Tamera I. Bedford Felicity E. Bennett Dominic J. Booth Hollie Burton Victoria J. Chng Charlotte W. T. Choimes Argyrios Correia David L. P. Day Julie Echeverría-Londoño Susy Emerson Susan R. Gao Di Garon Morgan Harrison Michelle L. K. Ingram Daniel J. Jung Martin Kemp Victoria Kirkpatrick Lucinda Martin Callum D. Pan Yuan Pask-Hale Gwilym D. Pynegar Edwin L. Robinson Alexandra N. Sanchez-Ortiz Katia Senior Rebecca A. Simmons Benno I. White Hannah J. Zhang Hanbin Aben Job Abrahamczyk Stefan Adum Gilbert B. Aguilar-Barquero Virginia Aizen Marcelo A. Albertos Belén Alcala E. L. del Mar Alguacil Maria Alignier Audrey Ancrenaz Marc Andersen Alan N. Arbeláez-Cortés Enrique Armbrecht Inge Arroyo-Rodríguez Víctor Aumann Tom Axmacher Jan C. Azhar Badrul Azpiroz Adrián B. Baeten Lander Bakayoko Adama Báldi András Banks John E. Baral Sharad K. Barlow Jos Barratt Barbara I. P. Barrico Lurdes Bartolommei Paola Barton Diane M. Basset Yves Batáry Péter Bates Adam J. Baur Bruno Bayne Erin M. Beja Pedro Benedick Suzan Berg Åke Bernard Henry Berry Nicholas J. Bhatt Dinesh Bicknell Jake E. Bihn Jochen H. Blake Robin J. Bobo Kadiri S. Bóçon Roberto Boekhout Teun Böhning-Gaese Katrin Bonham Kevin J. Borges Paulo A. V. Borges Sérgio H. Boutin Céline Bouyer Jérémy Bragagnolo Cibele Brandt Jodi S. Brearley Francis Q. Brito Isabel Bros Vicenç Brunet Jörg Buczkowski Grzegorz Buddle Christopher M. Bugter Rob Buscardo Erika Buse Jörn Cabra-García Jimmy Cáceres Nilton C. Cagle Nicolette L. Calviño-Cancela María Cameron Sydney A. Cancello Eliana M. Caparrós Rut Cardoso Pedro Carpenter Dan Carrijo Tiago F. Carvalho Anelena L. Cassano Camila R. Castro Helena Castro-Luna Alejandro A. Rolando Cerda B. Cerezo Alexis Chapman Kim Alan Chauvat Matthieu Christensen Morten Clarke Francis M. Cleary Daniel F. R. Colombo Giorgio Connop Stuart P. Craig Michael D. Cruz-López Leopoldo Cunningham Saul A. D'Aniello Biagio D'Cruze Neil da Silva Pedro Giovâni Dallimer Martin Danquah Emmanuel Darvill Ben Dauber Jens Davis Adrian L. V. Dawson Jeff de Sassi Claudio de Thoisy Benoit Deheuvels Olivier Dejean Alain Devineau Jean-Louis Diekötter Tim Dolia Jignasu V. Domínguez Erwin Dominguez-Haydar Yamileth Dorn Silvia Draper Isabel Dreber Niels Dumont Bertrand Dures Simon G. Dynesius Mats Edenius Lars Eggleton Paul Eigenbrod Felix Elek Zoltán Entling Martin H. Esler Karen J. de Lima Ricardo F. Faruk Aisyah Farwig Nina Fayle Tom M. Felicioli Antonio Felton Annika M. Fensham Roderick J. Fernandez Ignacio C. Ferreira Catarina C. Ficetola Gentile F. Fiera Cristina Filgueiras Bruno K. C. Fırıncıoğlu Hüseyin K. Flaspohler David Floren Andreas Fonte Steven J. Fournier Anne Fowler Robert E. Franzén Markus Fraser Lauchlan H. Fredriksson Gabriella M. Freire Geraldo B. Frizzo Tiago L. M. Fukuda Daisuke Furlani Dario Gaigher René Ganzhorn Jörg U. García Karla P. Garcia-R Juan C. Garden Jenni G. Garilleti Ricardo Ge Bao-Ming Gendreau-Berthiaume Benoit Gerard Philippa J. Gheler-Costa Carla Gilbert Benjamin Giordani Paolo Giordano Simonetta Golodets Carly Gomes Laurens G. L. Gould Rachelle K. Goulson Dave Gove Aaron D. Granjon Laurent Grass Ingo Gray Claudia L. Grogan James Gu Weibin Guardiola Moisès Gunawardene Nihara R. Gutierrez Alvaro G. Gutiérrez-Lamus Doris L. Haarmeyer Daniela H. Hanley Mick E. Hanson Thor Hashim Nor R. Hassan Shombe N. Hatfield Richard G. Hawes Joseph E. Hayward Matt W. Hébert Christian Helden Alvin J. Henden John-André Henschel Philipp Hernández Lionel Herrera James P. Herrmann Farina Herzog Felix Higuera-Diaz Diego Hilje Branko Höfer Hubert Hoffmann Anke Horgan Finbarr G. Hornung Elisabeth Horváth Roland Hylander Kristoffer Isaacs-Cubides Paola Ishida Hiroaki Ishitani Masahiro Jacobs Carmen T. Jaramillo Víctor J. Jauker Birgit Hernández F. Jiménez Johnson McKenzie F. Jolli Virat Jonsell Mats Juliani S. Nur Jung Thomas S. Kapoor Vena Kappes Heike Kati Vassiliki Katovai Eric Kellner Klaus Kessler Michael Kirby Kathryn R. Kittle Andrew M. Knight Mairi E. Knop Eva Kohler Florian Koivula Matti Kolb Annette Kone Mouhamadou Kőrösi Ádám Krauss Jochen Kumar Ajith Kumar Raman Kurz David J. Kutt Alex S. Lachat Thibault Lantschner Victoria Lara Francisco Lasky Jesse R. Latta Steven C. Laurance William F. Lavelle Patrick Le Féon Violette LeBuhn Gretchen Légaré Jean-Philippe Lehouck Valérie Lencinas María V. Lentini Pia E. Letcher Susan G. Li Qi Litchwark Simon A. Littlewood Nick A. Liu Yunhui Lo-Man-Hung Nancy López-Quintero Carlos A. Louhaichi Mounir Lövei Gabor L. Lucas-Borja Manuel Esteban Luja Victor H. Luskin Matthew S. MacSwiney G M Cristina Maeto Kaoru Magura Tibor Mallari Neil Aldrin Malone Louise A. Malonza Patrick K. Malumbres-Olarte Jagoba Mandujano Salvador Måren Inger E. Marin-Spiotta Erika Marsh Charles J. Marshall E. J. P. Martínez Eliana Martínez Pastur Guillermo Moreno Mateos David Mayfield Margaret M. Mazimpaka Vicente McCarthy Jennifer L. McCarthy Kyle P. McFrederick Quinn S. McNamara Sean Medina Nagore G. Medina Rafael Mena Jose L. Mico Estefania Mikusinski Grzegorz Milder Jeffrey C. Miller James R. Miranda-Esquivel Daniel R. Moir Melinda L. Morales Carolina L. Muchane Mary N. Muchai Muchane Mary N. Muchai Mudri-Stojnic Sonja Munira A. Nur Muoñz-Alonso Antonio Munyekenye B. F. Naidoo Robin Naithani A. Nakagawa Michiko Nakamura Akihiro Nakashima Yoshihiro Naoe Shoji Nates-Parra Guiomar Navarrete Gutierrez Dario A. Navarro-Iriarte Luis Ndang'ang'a Paul K. Neuschulz Eike L. Ngai Jacqueline T. Nicolas Violaine Nilsson Sven G. Noreika Norbertas Norfolk Olivia Noriega Jorge Ari Norton David A. Nöske Nicole M. Nowakowski A. Justin Numa Catherine O'Dea Niall O'Farrell Patrick J. Oduro William Oertli Sabine Ofori-Boateng Caleb Oke Christopher Omamoke Oostra Vicencio Osgathorpe Lynne M. Otavo Samuel Eduardo Page Navendu V. Paritsis Juan Parra-H Alejandro Parry Luke Pe'er Guy Pearman Peter B. Pelegrin Nicolás Pélissier Raphaël Peres Carlos A. Peri Pablo L. Persson Anna S. Petanidou Theodora Peters Marcell K. Pethiyagoda Rohan S. Phalan Ben Philips T. Keith Pillsbury Finn C. Pincheira-Ulbrich Jimmy Pineda Eduardo Pino Joan Pizarro-Araya Jaime Plumptre A. J. Poggio Santiago L. Politi Natalia Pons Pere Poveda Katja Power Eileen F. Presley Steven J. Proença Vânia Quaranta Marino Quintero Carolina Rader Romina Ramesh B. R. Ramirez-Pinilla Martha P. Ranganathan Jai Rasmussen Claus Redpath-Downing Nicola A. Reid J. Leighton Reis Yana T. Rey Benayas José M. Rey-Velasco Juan Carlos Reynolds Chevonne Ribeiro Danilo Bandini Richards Miriam H. Richardson Barbara A. Richardson Michael J. Ríos Rodrigo Macip Robinson Richard Robles Carolina A. Römbke Jörg Romero-Duque Luz Piedad Rös Matthias Rosselli Loreta Rossiter Stephen J. Roth Dana S. Roulston T'ai H. Rousseau Laurent Rubio André V. Ruel Jean-Claude Sadler Jonathan P. Sáfián Szabolcs Saldaña-Vázquez Romeo A. Sam Katerina Samnegård Ulrika Santana Joana Santos Xavier Savage Jade Schellhorn Nancy A. Schilthuizen Menno Schmiedel Ute Schmitt Christine B. Schon Nicole L. Schüepp Christof Schumann Katharina Schweiger Oliver Scott Dawn M. Scott Kenneth A. Sedlock Jodi L. Seefeldt Steven S. Shahabuddin Ghazala Shannon Graeme Sheil Douglas Sheldon Frederick H. Shochat Eyal Siebert Stefan J. Silva Fernando A. B. Simonetti Javier A. Slade Eleanor M. Smith Jo Smith-Pardo Allan H. Sodhi Navjot S. Somarriba Eduardo J. Sosa Ramón A. Soto Quiroga Grimaldo St-Laurent Martin-Hugues Starzomski Brian M. Stefanescu Constanti Steffan-Dewenter Ingolf Stouffer Philip C. Stout Jane C. Strauch Ayron M. Struebig Matthew J. Su Zhimin Suarez-Rubio Marcela Sugiura Shinji Summerville Keith S. Sung Yik-Hei Sutrisno Hari Svenning Jens-Christian Teder Tiit Threlfall Caragh G. Tiitsaar Anu Todd Jacqui H. Tonietto Rebecca K. Torre Ignasi Tóthmérész Béla Tscharntke Teja Turner Edgar C. Tylianakis Jason M. Uehara-Prado Marcio Urbina-Cardona Nicolas Vallan Denis Vanbergen Adam J. Vasconcelos Heraldo L. Vassilev Kiril Verboven Hans A. F. Verdasca Maria João Verdú José R. Vergara Carlos H. Vergara Pablo M. Verhulst Jort Virgilio Massimiliano Vu Lien Van Waite Edward M. Walker Tony R. Wang Hua-Feng Wang Yanping Watling James I. Weller Britta Wells Konstans Westphal Catrin Wiafe Edward D. Williams Christopher D. Willig Michael R. Woinarski John C. Z. Wolf Jan H. D. Wolters Volkmar Woodcock Ben A. Wu Jihua Wunderle Joseph M. Yamaura Yuichi Yoshikura Satoko Yu Douglas W. Zaitsev Andrey S. Zeidler Juliane Zou Fasheng Collen Ben Ewers Rob M. Mace Georgina M. Purves Drew W. Scharlemann Jörn P. W. Purvis Andy 2017 The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project Ecology and Evolution 7 1 145 188 10.1002/ece3.2579 Hunt William H Wall Diana H 2002 Modelling the effects of loss of soil biodiversity on ecosystem function Global change biology 8 33 50 10.1046/j.1365-2486.2002.00425.x Hurtt George C Chini Louise Parsons Frolking Steve Betts R A Feddema Johannes Fischer G Fisk J P Hibbard K Houghton R A Janetos A 2011 Harmonization of land-use scenarios for the period 1500--2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands Climatic change 109 1-2 117 161 10.1007/s10584-011-0153-2 IPBES Ferrier S. Ninan K. N. Leadley P. Alkemade R. Acosta L. A. Akçakaya H. R. Brotons L. Cheung W. Christensen V. Harhash K. A. Kabubo-Mariara J. Lundquist C. Obersteiner M. Pereira H. Peterson G. Pichs-Madruga R. Ravindranath N. H. Rondinini C. Wintle B. 2016 Summary for policymakers of the methodological assessment of scenarios and models of biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem IPCC 2014 Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press 9789291691432 10.1017/CBO9781107415324.004 ISO 2018 Soil quality - Sampling of soil invertebrates - Part 1: Hand-sorting and extraction of earthworms (ISO/FDIS 23611-1:2018) Jones Clive G Lawton John H Shachak Moshe 1994 Organisms as Ecosystem Engineers Oikos 69 3 10.2307/3545850 Karger Dirk Nikolaus Conrad Olaf Böhner Jürgen Kawohl Tobias Kreft Holger Soria-Auza Rodrigo Wilber Zimmermann Niklaus E. Linder H. Peter Kessler Michael 2017 Climatologies at high resolution for the earth’s land surface areas Scientific Data 4 10.1038/sdata.2017.122 Kattge Jens D\'\iaz Sandra Lavorel Sandra Prentice I C Leadley Paul Bönisch Gerhard Garnier Eric Westoby Mark Reich Peter B Wright I J 2011 TRY -- a global database of plant traits Global change biology 17 9 2905 2935 10.1111/j.1365-2486.2011.02451.x Keuskamp Joost A. Dingemans Bas J J Lehtinen Taru Sarneel Judith M. Hefting Mariet M. 2013 Tea Bag Index: A novel approach to collect uniform decomposition data across ecosystems Methods in Ecology and Evolution 4 11 1070 1075 10.1111/2041-210X.12097 Koricheva Julia Gurevitch Jessica Mengersen Kerrie 2013 Handbook of meta-analysis in ecology and evolution Princeton University Press 9780691137292 10.2307/j.ctt24hq6n Kreft Holger Jetz Walter 2007 Global patterns and determinants of vascular plant diversity Proceedings of the National Academy of Sciences 104 14 5925 5930 10.1073/pnas.0608361104 Lambin Eric F Meyfroidt Patrick 2011 Global land use change, economic globalization, and the looming land scarcity Proceedings of the National Academy of Sciences 108 9 3465 3472 10.1073/pnas.1100480108 Lavelle P Bignell D Lepage M Wolters W Roger P Ineson P Heal O W Dhillion S 1997 Soil function in a changing world: the role of invertebrate ecosystem engineers European Journal of Soil Biology 33 May 1996 159 193 Lawrence Amy Paulson Bowers Michael A. 2002 A test of the 'hot' mustard extraction method of sampling earthworms Soil Biology and Biochemistry 34 4 549 552 10.1016/S0038-0717(01)00211-5 Lefcheck Jonathan S 2016 piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics Methods in Ecology and Evolution 7 5 573 579 10.1111/2041-210X.12512 Lubbers Ingrid M Van Groenigen Kees Jan Fonte Steven J Six Johan Brussaard Lijbert Van Groenigen Jan Willem 2013 Greenhouse-gas emissions from soils increased by earthworms Nature Climate Change 3 3 187 194 10.1038/nclimate1692 Maestre Fernando T Eisenhauer Nico 2019 Recommendations for establishing global collaborative networks in soil ecology SOIL ORGANISMS 91 3 SE - ARTICLES 10.25674/so91iss3pp73 Magurran Anne E 2004 Measuring biological diversity John Wiley & Sons Newbold Tim Hudson Lawrence N. Hill Samantha L. L. Contu Sara Lysenko Igor Senior Rebecca A. Börger Luca Bennett Dominic J. Choimes Argyrios Collen Ben Day Julie De Palma Adriana Díaz Sandra Echeverria-Londoño Susy Edgar Melanie J. Feldman Anat Garon Morgan Harrison Michelle L. K. Alhusseini Tamera Ingram Daniel J. Itescu Yuval Kattge Jens Kemp Victoria Kirkpatrick Lucinda Kleyer Michael Correia David Laginha Pinto Martin Callum D. Meiri Shai Novosolov Maria Pan Yuan Phillips Helen R. P. Purves Drew W. Robinson Alexandra Simpson Jake Tuck Sean L. Weiher Evan White Hannah J. Ewers Robert M. Mace Georgina M. Scharlemann Jörn P. W. Purvis Andy 2015 Global effects of land use on local terrestrial biodiversity Nature 520 7545 45 50 10.1038/nature14324 Newbold Tim Hudson Lawrence N. Arnell Andrew P. Contu Sara De Palma Adriana Ferrier Simon Hill Samantha L. L. Hoskins Andrew J. Lysenko Igor Phillips Helen R. P. Burton Victoria J. Chng Charlotte W. T. Emerson Susan Gao Di Pask-Hale Gwilym Hutton Jon Jung Martin Sanchez-Ortiz Katia Simmons Benno I. Whitmee Sarah Zhang Hanbin Scharlemann Jörn P. W. Purvis Andy 2016 Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment Science 353 6296 288 291 10.1126/science.aaf2201 Orgiazzi A. Bardgett R. D. Barrios E. Behan-Pelletier V. Briones M. J.I. Chotte J-L. De Deyn G. B. Eggleton P. Fierer N. Fraser T. Hedlund K. Jeffery S. Johnson N. C. Jones A. Kandeler E. Kaneko N. Lavelle P. Lemanceau P. Miko L. , . Montanarella L. Moreira F. M.S. Ramirez K. S. Scheu S. Singh B. K. Six J. van der Putten W. H. Wall D. H. 2016 Global Soil Biodiversity Atlas European Commission 978-92-79-48168-0 10.2788/2613 Orme C David L Davies Richard G Burgess Malcolm Eigenbrod Felix Pickup Nicola Olson Valerie A Webster Andrea J Ding Tzung-Su Rasmussen Pamela C Ridgely Robert S Stattersfield Ali J. Bennett Peter M. Blackburn Tim M. Gaston Kevin J. Owens Ian P. F. 2005 Global hotspots of species richness are not congruent with endemism or threat Nature 436 7053 1016 1019 10.1038/nature03850 Pereira H. M. Leadley P. W. Proenca V. Alkemade R. Scharlemann J. P. W. Fernandez-Manjarres J. F. Araujo M. B. Balvanera P. Biggs R. Cheung W. W. L. Chini L. Cooper H. D. Gilman E. L. Guenette S. Hurtt G. C. Huntington H. P. Mace G. M. Oberdorff T. Revenga C. Rodrigues P. Scholes R. J. Sumaila U. R. Walpole M. 2010 Scenarios for Global Biodiversity in the 21st Century Science 330 6010 1496 1501 10.1126/science.1196624 Phillips Helen R P Newbold Tim Purvis Andy 2017 Land-use effects on local biodiversity in tropical forests vary between continents Biodiversity and Conservation 26 9 2251 2270 10.1007/s10531-017-1356-2 Phillips Helen R P Cameron Erin K Ferlian Olga Türke Manfred Winter Marten Eisenhauer Nico 2017 Red list of a black box Nature Ecology & Evolution 1 4 10.1038/s41559-017-0103 Phillips Helen R P Halley John M. Urbina-Cardona J Nicolas Purvis Andy 2018 The effect of fragment area on site-level biodiversity Ecography 41 7 1220 1231 10.1111/ecog.02956 Phillips Helen R P Guerra Carlos A Bartz Marie L C Briones Marie J I Brown George Crowther Thomas W. Ferlian Olga Gongalsky Konstantin B Hoogen Johan van den Krebs Julia Orgiazzi Alberto Routh Devin Schwarz Benjamin Bach Elizabeth M Bennett Joanne Brose Ulrich Decaëns Thibaud König-Ries Birgitta Loreau Michel Mathieu Jérôme Mulder Christian Putten Wim van der Ramirez Kelly S Rillig Matthias C Russell David Rutgers Michiel Thakur Madhav P Vries Franciska T. de Wall Diana Wardle David Arai Miwa Ayuke Fredrick O. Baker Geoff H. Beauséjour Robin Bedano José C. Birkhofer Klaus Blanchart Eric Blossey Bernd Bolger Thomas Bradley Robert L. Callaham Mac A. Capowiez Yvan Caulfield Mark E. Choi Amy Crotty Felicity V. Dávalos Andrea Cosin Darío J. Diaz Dominguez Anahí Duhour Andrés Esteban Eekeren Nick van Emmerling Christoph Falco Liliana B. Fernández Rosa Fonte Steven J. Fragoso Carlos Franco André L. C. Fugère Martine Fusilero Abegail T. Gholami Shaieste Gundale Michael J. López Mónica Gutiérrez Hackenberger Davorka K. Hernández Luis M. Hishi Takuo Holdsworth Andrew R. Holmstrup Martin Hopfensperger Kristine N. Lwanga Esperanza Huerta Huhta Veikko Hurisso Tunsisa T. Iannone III Basil V. Iordache Madalina Joschko Monika Kaneko Nobuhiro Kanianska Radoslava Keith Aidan M. Kelly Courtland A. Kernecker Maria L. Klaminder Jonatan Koné Armand W. Kooch Yahya Kukkonen Sanna T. Lalthanzara H. Lammel Daniel R. Lebedev Iurii M. Li Yiqing Lidon Juan B. Jesus Lincoln Noa K. Loss Scott R. Marichal Raphael Matula Radim Moos Jan Hendrik Moreno Gerardo Morón-Ríos Alejandro Muys Bart Neirynck Johan Norgrove Lindsey Novo Marta Nuutinen Visa Nuzzo Victoria P Mujeeb Rahman Pansu Johan Paudel Shishir Pérès Guénola Pérez-Camacho Lorenzo Piñeiro Raúl Ponge Jean-François Rashid Muhammad Imtiaz Rebollo Salvador Rodeiro-Iglesias Javier Rodríguez Miguel Á. Roth Alexander M. Rousseau Guillaume X. Rozen Anna Sayad Ehsan Schaik Loes van Scharenbroch Bryant C. Schirrmann Michael Schmidt Olaf Schröder Boris Seeber Julia Shashkov Maxim P. Singh Jaswinder Smith Sandy M. Steinwandter Michael Talavera José A. Trigo Dolores Tsukamoto Jiro Valença Anne W. de Vanek Steven J. Virto Iñigo Wackett Adrian A. Warren Matthew W. Wehr Nathaniel H. Whalen Joann K. Wironen Michael B. Wolters Volkmar Zenkova Irina V. Zhang Weixin Cameron Erin K Eisenhauer Nico 2019 Global distribution of earthworm diversity Science 366 6464 480 485 10.1101/587394 Pimm Stuart L Jenkins Clinton N Abell Robin Brooks Thomas M Gittleman John L Joppa Lucas N Raven Peter H Roberts Callum M Sexton Joseph O 2014 The biodiversity of species and their rates of extinction, distribution, and protection Science 344 6187 10.1126/science.1246752 Ramirez Kelly S. Knight Christopher G. De Hollander Mattias Brearley Francis Q. Constantinides Bede Cotton Anne Creer Si Crowther Thomas W. Davison John Delgado-Baquerizo Manuel Dorrepaal Ellen Elliott David R. Fox Graeme Griffiths Robert I. Hale Chris Hartman Kyle Houlden Ashley Jones David L. Krab Eveline J. Maestre Fernando T. McGuire Krista L. Monteux Sylvain Orr Caroline H. Van Der Putten Wim H. Roberts Ian S. Robinson David A. Rocca Jennifer D. Rowntree Jennifer Schlaeppi Klaus Shepherd Matthew Singh Brajesh K. Straathof Angela L. Bhatnagar Jennifer M. Thion Cécile Van Der Heijden Marcel G. A. De Vries Franciska T. 2018 Detecting macroecological patterns in bacterial communities across independent studies of global soils Nature Microbiology 3 2 189 196 10.1038/s41564-017-0062-x Ramirez Kelly S Döring Markus Eisenhauer Nico Gardi Ciro Ladau Josh Leff Jonathan W Lentendu Guillaume Lindo Zoë Rillig Matthias C Russell David 2015 Toward a global platform for linking soil biodiversity data Frontiers in Ecology and Evolution 3 10.3389/fevo.2015.00091 Team R Core 2016 R: A language and environment for statistical computing R Foundation for Statistical Computing Vienna, Austria Riahi Keywan van Vuuren Detlef P Kriegler Elmar Edmonds Jae O'Neill Brian C Fujimori Shinichiro Bauer Nico Calvin Katherine Dellink Rob Fricko Oliver Lutz Wolfgang Popp Alexander Cuaresma Jesus Crespo KC Samir Leimbach Marian Jiang Leiwen Kram Tom Rao Shilpa Emmerling Johannes Ebi Kristie Hasegawa Tomoko Havlik Petr Humpenöder Florian Da Silva Lara Aleluia Smith Steve Stehfest Elke Bosetti Valentina Eom Jiyong Gernaat David Masui Toshihiko Rogelj Joeri Strefler Jessica Drouet Laurent Krey Volker Luderer Gunnar Harmsen Mathijs Takahashi Kiyoshi Baumstark Lavinia Doelman Jonathan C Kainuma Mikiko Klimont Zbigniew Marangoni Giacomo Lotze-Campen Hermann Obersteiner Michael Tabeau Andrzej Tavoni Massimo 2017 The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview Global Environmental Change 42 153 168 10.1016/j.gloenvcha.2016.05.009 Risch A. C. Zimmermann S. Ochoa-Hueso R. Schütz M. Frey B. Firn J. L. Fay P. A. Hagedorn F. Borer E. T. Seabloom E. W. Harpole W. S. Knops J. M.H. McCulley R. L. Broadbent A. A.D. Stevens C. J. Silveira M. L. Adler P. B. Báez S. Biederman L. A. Blair J. M. Brown C. S. Caldeira M. C. Collins S. L. Daleo P. di Virgilio A. Ebeling A. Eisenhauer N. Esch E. Eskelinen A. Hagenah N. Hautier Y. Kirkman K. P. MacDougall A. S. Moore J. L. Power S. A. Prober S. M. Roscher C. Sankaran M. Siebert J. Speziale K. L. Tognetti P. M. Virtanen R. Yahdjian L. Moser B. 2019 Soil net nitrogen mineralisation across global grasslands Nature Communications 10.1038/s41467-019-12948-2 Roll Uri Feldman Anat Novosolov Maria Allison Allen Bauer Aaron M Bernard Rodolphe Böhm Monika Castro-Herrera Fernando Chirio Laurent Collen Ben Colli Guarino R Dabool Lital Das Indraneil Doan Tiffany M Grismer Lee L Hoogmoed Marinus Itescu Yuval Kraus Fred LeBreton Matthew Lewin Amir Martins Marcio Maza Erez Meirte Danny Nagy Zoltán T Nogueira Cristiano, de C. Pauwels Olivier S G Pincheira-Donoso Daniel Powney Gary D Sindaco Roberto Tallowin Oliver J S Torres-Carvajal Omar Trape Jean-François Vidan Enav Uetz Peter Wagner Philipp Wang Yuezhao Orme C David L Grenyer Richard Meiri Shai 2017 The global distribution of tetrapods reveals a need for targeted reptile conservation Nature Ecology & Evolution 1 11 1677 1682 10.1038/s41559-017-0332-2 Roslin Tomas Hardwick Bess Novotny Vojtech Petry William K. Andrew Nigel R. Asmus Ashley Barrio Isabel C. Basset Yves Boesing Andrea Larissa Bonebrake Timothy C. Cameron Erin K. Dáttilo Wesley Donoso David A. Drozd Pavel Gray Claudia L. Hik David S. Hill Sarah J. Hopkins Tapani Huang Shuyin Koane Bonny Laird-Hopkins Benita Laukkanen Liisa Lewis Owen T. Milne Sol Mwesige Isaiah Nakamura Akihiro Nell Colleen S. Nichols Elizabeth Prokurat Alena Sam Katerina Schmidt Niels M. Slade Alison Slade Victor Suchanková Alžběta Teder Tiit Van Nouhuys Saskya Vandvik Vigdis Weissflog Anita Zhukovich Vital Slade Eleanor M. 2017 Latitudinal gradients: Higher predation risk for insect prey at low latitudes and elevations Science 356 6339 742 744 10.1126/science.aaj1631 Rusek Josef 1998 Biodiversity of Collembola and their functional role in the ecosystem Biodiversity and Conservation 7 9 1207 1219 10.1023/A:1008887817883 Rutgers Michiel Orgiazzi Alberto Gardi Ciro Römbke Jörg Jänsch Stephan Keith Aidan M. Neilson Roy Boag Brian Schmidt Olaf Murchie Archie K. Blackshaw Rod P. Pérès Guénola Cluzeau Daniel Guernion Muriel Briones Maria J. I. Rodeiro Javier Piñeiro Raúl Cosín Darío J. Díaz Sousa J. Paulo Suhadolc Marjetka Kos Ivan Krogh Paul Henning Faber Jack H. Mulder Christian Bogte Jaap J. Wijnen Harm J. van Schouten Anton J. Zwart Dick de 2016 Mapping earthworm communities in Europe Applied Soil Ecology 97 98 111 10.1016/j.apsoil.2015.08.015 Sala O E Chapin F S Armesto J J Berlow E Bloomfield J Dirzo R Huber-Sanwald E Huenneke L F Jackson R B Kinzig A Leemans R Lodge D M Mooney H A Oesterheld M Poff N L Sykes M T Walker B H Walker M Wall D H 2000 Global biodiversity scenarios for the year 2100 Science 287 5459 1770 1774 10.1126/science.287.5459.1770 Shipley Bill 2016 Cause and Correlation in Biology A User's Guide to Path Analysis, Structural Equations and Causal Inference 20 ambridge University Press 0511017723 10.1017/CBO9780511605949 Smith Gabriel R Crowther Thomas W Eisenhauer Nico Hoogen Johan van den 2020 Building a global database of soil microbial biomass and function: a call for collaboration SOIL ORGANISMS 91 3 SE - ARTICLES 10.25674/so91iss3pp140 Soliveres Santiago Van Der Plas Fons Manning Peter Prati Daniel Gossner Martin M. Renner Swen C. Alt Fabian Arndt Hartmut Baumgartner Vanessa Binkenstein Julia Birkhofer Klaus Blaser Stefan Bl�thgen Nico Boch Steffen B�hm Stefan B�rschig Carmen Buscot Francois Diek�tter Tim Heinze Johannes H�lzel Norbert Jung Kirsten Klaus Valentin H. Kleinebecker Till Klemmer Sandra Krauss Jochen Lange Markus Morris E. Kathryn M�ller J�rg Oelmann Yvonne Overmann J�rg Pašalić Esther Rillig Matthias C. Schaefer H. Martin Schloter Michael Schmitt Barbara Sch�ning Ingo Schrumpf Marion Sikorski Johannes Socher Stephanie A. Solly Emily F. Sonnemann Ilja Sorkau Elisabeth Steckel Juliane Steffan-Dewenter Ingolf Stempfhuber Barbara Tschapka Marco T�rke Manfred Venter Paul C. Weiner Christiane N. Weisser Wolfgang W. Werner Michael Westphal Catrin Wilcke Wolfgang Wolters Volkmar Wubet Tesfaye Wurst Susanne Fischer Markus Allan Eric 2016 Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality Nature 536 7617 456 459 10.1038/nature19092 Tedersoo L. Bahram M. Polme S. Koljalg U. Yorou N. S. Wijesundera R. Ruiz L. V. Vasco-Palacios A. M. Thu P. Q. Suija A. Smith M. E. Sharp C. Saluveer E. Saitta A. Rosas M. Riit T. Ratkowsky D. Pritsch K. Poldmaa K. Piepenbring M. Phosri C. Peterson M. Parts K. Partel K. Otsing E. Nouhra E. Njouonkou A. L. Nilsson R. H. Morgado L. N. Mayor J. May T. W. Majuakim L. Lodge D. J. Lee S. S. Larsson K. -H. Kohout P. Hosaka K. Hiiesalu I. Henkel T. W. Harend H. Guo L. -d. Greslebin A. Grelet G. Geml J. Gates G. Dunstan W. Dunk C. Drenkhan R. Dearnaley J. De Kesel A. Dang T. Chen X. Buegger F. Brearley F. Q. Bonito G. Anslan S. Abell S. Abarenkov K. 2014 Global diversity and geography of soil fungi Science 346 6213 1256688 1256688 10.1126/science.1256688 Tittensor Derek P Walpole Matt Hill Samantha L L Boyce Daniel G Britten Gregory L Burgess Neil D Butchart Stuart H M Leadley Paul W Regan Eugenie C Alkemade Rob 2014 A mid-term analysis of progress toward international biodiversity targets Science 346 6206 241 244 10.1126/science.1257484 van den Hoogen Johan Geisen Stefan Routh Devin Ferris Howard Traunspurger Walter Wardle David A. de Goede Ron G. M. Adams Byron J. Ahmad Wasim Andriuzzi Walter S. Bardgett Richard D. Bonkowski Michael Campos-Herrera Raquel Cares Juvenil E. Caruso Tancredi de Brito Caixeta Larissa Chen Xiaoyun Costa Sofia R. Creamer Rachel Mauro da Cunha Castro José Dam Marie Djigal Djibril Escuer Miguel Griffiths Bryan S. Gutiérrez Carmen Hohberg Karin Kalinkina Daria Kardol Paul Kergunteuil Alan Korthals Gerard Krashevska Valentyna Kudrin Alexey A. Li Qi Liang Wenju Magilton Matthew Marais Mariette Martín José Antonio Rodríguez Matveeva Elizaveta Mayad El Hassan Mulder Christian Mullin Peter Neilson Roy Nguyen T. A. Duong Nielsen Uffe N Okada Hiroaki Rius Juan Emilio Palomares Pan Kaiwen Peneva Vlada Pellissier Loïc Carlos Pereira da Silva Julio Pitteloud Camille Powers Thomas O. Powers Kirsten Quist Casper W. Rasmann Sergio Moreno Sara Sánchez Scheu Stefan Setälä Heikki Sushchuk Anna Tiunov Alexei V. Trap Jean van der Putten Wim Vestergård Mette Villenave Cecile Waeyenberge Lieven Wall Diana H. Wilschut Rutger Wright Daniel G. Yang Jiue-in Crowther Thomas Ward 2019 Soil nematode abundance and functional group composition at a global scale Nature 572 7768 194 198 10.1038/s41586-019-1418-6 van Groenigen Jan Willem Lubbers Ingrid M Vos Hannah M J Brown George G De Deyn Gerlinde B van Groenigen Kees Jan 2014 Earthworms increase plant production: a meta-analysis Scientific Reports 4 2 10.1038/srep06365 Vellend Mark Verheyen Kris Jacquemyn Hans Kolb Annette Van Calster Hans Peterken George Hermy Martin 2006 Extinction debt of forest plants persists for more than a century following habitat fragmentation Ecology 87 3 542 548 10.1890/05-1182 Vellend Mark Baeten Lander Myers-Smith Isla H Elmendorf Sarah C Beauséjour Robin Brown Carissa D De Frenne Pieter Verheyen Kris Wipf Sonja 2013 Global meta-analysis reveals no net change in local-scale plant biodiversity over time Proceedings of the National Academy of Sciences 110 48 19456 19459 10.1073/pnas.1312779110 Vellend Mark Dornelas Maria Baeten Lander Beauséjour Robin Brown Carissa D De Frenne Pieter Elmendorf Sarah C Gotelli Nicholas J Moyes Faye Myers-Smith Isla H Magurran Anne E McGill Brian J Shimadzu Hideyasu Sievers Caya 2017 Estimates of local biodiversity change over time stand up to scrutiny Ecology 98 2 583 590 10.1002/ecy.1660 Veresoglou Stavros D Halley John M Rillig Matthias C 2015 Extinction risk of soil biota Nature Communications 6 10.1038/ncomms9862 Verheyen Kris Vanhellemont Margot Auge Harald Baeten Lander Baraloto Christopher Barsoum Nadia Bilodeau-Gauthier Simon Bruelheide Helge Castagneyrol Bastien Godbold Douglas Haase Josephine Hector Andy Jactel Hervé Koricheva Julia Loreau Michel Mereu Simone Messier Christian Muys Bart Nolet Philippe Paquette Alain Parker John Perring Mike Ponette Quentin Potvin Catherine Reich Peter Smith Andy Weih Martin Scherer-Lorenzen Michael 2016 Contributions of a global network of tree diversity experiments to sustainable forest plantations Ambio 45 1 29 41 10.1007/s13280-015-0685-1 Wagg Cameron Bender S Franz Widmer Franco van der Heijden Marcel G A 2014 Soil biodiversity and soil community composition determine ecosystem multifunctionality Proceedings of the National Academy of Sciences 111 14 5266 5270 10.1073/pnas.1320054111 Wall Diana H. Bradford Mark A. Mark G. St. John, Trofymow John A. Behan-Pelletier Valerie Bignell David E. Dangerfield J. Mark Parton William J. Rusek Josef Voigt Winfried Wolters Volkmar Gardel Holley Zadeh Ayuke Fredo O. Bashford Richard Beljakova Olga I. Bohlen Patrick J. Brauman Alain Flemming Stephen Henschel Joh R. Johnson Dan L. Jones T. Helfin Kovarova Marcela Kranabetter J. Marty Kutny Les Lin Kuo Chuan Maryati Mohamed Masse Dominique Pokarzhevskii Andrei Rahman Homathevi Sabará Millor G. Salamon Joerg Alfred Swift Michael J. Varela Amanda Vasconcelos Heraldo L. White Don Zou Xiaoming 2008 Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent Global Change Biology 14 11 2661 2677 10.1111/j.1365-2486.2008.01672.x Wall Diana H Nielsen Uffe N Six Johan 2015 Perspective Soil biodiversity and human health Nature 528 69 76 10.1038/nature15744 Weiss D. J. Nelson A. Gibson H. S. Temperley W. Peedell S. Lieber A. Hancher M. Poyart E. Belchior S. Fullman N. Mappin B. Dalrymple U. Rozier J. Lucas T. C.D. Howes R. E. Tusting L. S. Kang S. Y. Cameron E. Bisanzio D. Battle K. E. Bhatt S. Gething P. W. 2018 A global map of travel time to cities to assess inequalities in accessibility in 2015 Nature 553 7688 333 336 10.1038/nature25181 Winfree Rachael W Fox Jeremy Williams Neal M Reilly James R Cariveau Daniel P 2015 Abundance of common species, not species richness, drives delivery of a real-world ecosystem service Ecology Letters 18 7 626 635 10.1111/ele.12424 Zhang Weixin Hendrix Paul F. Dame Lauren E. Burke Roger A. Wu Jianping Neher Deborah A. Li Jianxiong Shao Yuanhu Fu Shenglei 2013 Earthworms facilitate carbon sequestration through unequal amplification of carbon stabilization compared with mineralization Nature Communications 4 1 10.1038/ncomms3576 8BBECF52-C60F-5BD9-B668-65B773CB0810 10.3897/rio.8.e87143.figure1

Figure 1: A) Black dots show sample locations of the 11,009 sites in the Phillips et al. 2019) data. Yellow indicates areas where climate change is predicted to be greatest (> 3 degrees C change in temperature or a change of absolute 100 mm in rainfall; using Karger et al. 2017 and are relatively accessible from settlements (< 12 hours travel time; using Weiss et al. 2018). Apart from western Europe, most regions with a lot of climate change are not sampled representatively. B) Environmental representation of the current database across 10 environmental variables. Coverage is typically well spread, but under-representing certain values.

https://binary.pensoft.net/fig/682388 5B20BE62-6F14-5409-B269-1E8EAABCC560 10.3897/rio.8.e87143.figure2

Figure 2: The hypothetical changes in community composition as a result of changes in the disturbance across sites and their potential impact on ecosystem function. The X-axis shows two states of disturbance, ‘low’ (a baseline/reference) and ‘high’ (a change in the disturbance, such as a non-natural land use). Y-axis shows the potential amount or rate of an unspecified ecosystem function provided by the community. Each large circle is a community, composed of multiple smaller circles of different taxa groups (e.g. earthworms, carabid beetles, millipedes etc.) The size of the smaller circles indicates abundance or biomass of that group. The community changes from the reference state (a) when the disturbance changes (b, c, d). The community could change in equal proportions (b) or in dominance structure (c and d). The changes could result in a reduction in the ecosystem function that the community provides (b and c) or the ecosystem function could be maintained (d), especially if the dominant group is the main contributor to the ecosystem function measured.

https://binary.pensoft.net/fig/682422 89AB4CE6-46A5-5768-BBA2-75363C1A72CF 10.3897/rio.8.e87143.figure3

Figure 3: Structural Equation “meta-“ model (Grace et al. 2012). We will investigate the exact structure of the SEM and variables. For example, composite variables or PCA analysis may be used for the ‘Climate’ and ‘Soil Properties’ as they are comprised of multiple variables. ‘Land use’, as a categorical variable, would likely be a composite variable. Analysis would compare the direct effect of time on biodiversity (red arrow) with other indirect pathways.

https://binary.pensoft.net/fig/682665 49A89183-9FFC-57BE-ACBD-794E65F7E950 10.3897/rio.8.e87143.suppl1

SoilFaUNa Sampling Protocol

Sampling Protocol

Sampling protocol to ensure standardised methods for researchers sampling soil macrofauna (and ecosystem functions) in a new site.

File: oo_682557.pdf

https://binary.pensoft.net/file/682557 Helen RP Phillips