[Aim] Climate change affects ecological communities via impacts on species. The community's response to climate change can be represented as the temporal trend in a climate-related functional property that is quantified using a relevant functional trait. Noteworthy, some species influence this response in the community more strongly than others., [Innovation] Leveraging on the concept of keystone species, we propose that species with a strong effect on the community's functional response to climate change beyond their relative abundance can be considered as ‘climate keystone species’. We develop a stepwise tool to determine species' effects on a community's climate response and identify climate keystone species. We quantify the species-specific effect by measuring the difference in the community's climate response with and without the species. Next, we identify climate keystone species as those with a strong residual effect after weighting with their relative abundances in the community., [Main Conclusions] To illustrate the use of the stepwise tool with empirical data, we identify climate keystone species that have a strong effect on the change in the average temperature niche in North American bird communities over time and find the identification tool ecologically relevant. Identification of climate keystone species can serve as an additional conservation method to efficiently protect ecological communities and, in turn, the ecosystem functions they provide., A.S., E.M. and A.L. were funded by the Academy of Finland (projects 307909, 323527 and 329251). In addition, the research has been funded through the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND programme, and with the funding organizations Academy of Finland (Helsinki: 326338) and the National Science Foundation (CLO, ICER-1927646). H.K. acknowledges funding from the Finnish Strategic Research Council's project IBC-Carbon (312559) and the EU Horizon project NaturaConnect (101060429). S.M. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 882221 and the Biodiversa+2021 project DarCo. A.S. was funded through the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101027534 and the Jane and Aatos Erkko Foundation., Peer reviewed

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce ‘novel community data’ as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning., This paper originated from several meetings of the sCom working group supported by sDiv, the Synthesis Centre of iDiv (DFG Forschungszentrum 118, 202548816). We thank the editor and two anonymous reviewers for their helpful comments on earlier drafts of this manuscript, and Andrew Buxton and Sarah Garratt from NatureSpace Partnership for comments on the newt offset market. W.T. and G.P. also acknowledge support from the European Union’s Horizon Europe under the project NaturaConnect (grant agreement No. 101060429) and OBSGESSION, as well as the French Agence Nationale de la Recherche (ANR) through the Gambas project (ANR-18-CE02-0025). S.S-M. and W.T. were funded by the French Biodiversity Office through the FrenchBiodiv project. O.O. was funded by Academy of Finland (grant No. 336212 and 345110) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 856506; ERC-synergy project LIFEPLAN). G.G-A. is currently supported by a ‘Ramón y Cajal’ grant (RYC2020-028826-I) funded by the Spanish Ministry of Science and Innovation, the Agencia Estatal de Investigación (10.13039/501100011033) and ‘ESF Investing in your future’. M.A.L. was funded by University of Delaware National Science Foundation (NSF) award 2025118. D.S.V is currently supported by a ‘Juan de la Cierva Incorporación’ postdoctoral grant (IJC2020-044545-I) funded by the Spanish Ministry of Science and Innovation. D.W.Y. was supported by the Chinese Academy of Sciences (CAS) Strategic Priority Research Program (XDA20050202) and the CAS Key Research Program of Frontier Sciences (QYZDY-SSW-SMC024)., Peer reviewed

Linear infrastructure represent a barrier to movement for many species, reducing the connectivity of the landscapes in which they reside. Of all linear infrastructure, roads and fences are two of the most ubiquitous, and are understood to reduce landscape connectivity for wildlife. However, what is often neglected consideration is a holistic approach of modelling the effects of multiple types of linear infrastructure simultaneously. Few studies have examined this, typically assessing the impacts of a singular kind of infrastructure on landscape connectivity. Therefore, the aim of this study is to address the relative importance of considering multiple kinds of linear infrastructure in landscape connectivity modelling. We utilised presence data of red deer Cervus elaphus and wild boar Sus scrofa in Doñana Biosphere Reserve (Spain) to generate a sequential approach of scenarios of landscape connectivity; firstly only with environmental variables, secondly with roads as the sole infrastructure, thirdly with the addition of fences, and finally with the further addition of fences and wildlife road-crossing structures. We found that the connectivity of the landscape was greatly affected by the addition of fences and wildlife road-crossing structures in both species, with fences in particular causing considerable alterations to estimated movement pathways. Our finding impresses a need to consider multiple different types of linear infrastructure when modelling landscape connectivity to enable a more realistic view of wildlife movement and inform mitigation and conservation measures more accurately., This study has been carried out within the framework of NaturaConnect (grant agreement no. 101060429, funded under the European Union's Horizon Europe Research and Innovation Programme). IB was supported by the UK Government Turing Scheme facilitated by DSC Regen, FA by Fundação para a Ciência e Tecnologia – FCT (contract 2022.01773.CEECIND), LMN and ZT by the SUMHAL Project funded by the Spanish Ministry of Science and Innovation (MICINN) through the European Regional Development Fund [LIFEWATCH-2019-09-CSIC-13, POPE2014-2020], MP by the Spanish Ministry of Economy and Competitiveness (MINECO) and by the European Social Fund through the Ramón y Cajal Program (RYC2021-033192-I), MD by the MICINN through the Juan de la Cierva Incorporación Program (IJC2019-039662-I)., Peer reviewed

21 pages, 4 figures, supporting information https://doi.org/10.1111/csp2.70293.-- Data Availability statement: A list of articles on global change drivers in Doñana Protected Area has been provided as supporting information. No other data has been used in this article, Historically, Mediterranean systems, particularly the Mediterranean Basin, have been substantially impacted by multiple regional-scale disturbances resulting from complex interactive effects of global-change drivers. However, such effects are typically studied on isolated groups of organisms, often disregarding how ecological processes such as biotic interactions affect ecosystem responses to global change. We use the Doñana Protected Area, one of the most important wetlands and shrublands in Europe, as a well-documented case study to highlight how regional anthropogenic pressures simultaneously affect various interacting species, creating cascading impacts across trophic webs on different ecosystems. Using two examples representing the role of key habitat-structuring species on ecosystem processes, the cork oak (Quercus suber) and European rabbit (Oryctolagus cuniculus), we show how decreases in abundance of such key species due to interlinked direct and indirect anthropogenic pressures can alter multitrophic communities—but not always negatively, as other species can adapt to the loss of key species. We also use two examples of species that have flourished under human pressures, the native wild boar (Sus scrofa) and invasive red swamp crayfish (Procambarus clarkii), and how increases in their abundance have had complex impacts on ecosystems. We then discuss, based on the outcomes of actual conservation actions, how management targeted at single species or taxa is ineffective for ecosystem functioning, as it ignores complex interlinks with other components of the system. Instead, the ecosystem-wide impacts of gains and losses of interacting species serve as an excellent empirical example for the need for conservation management and research agenda that account for the complexity of global change in the Mediterranean, Maria Paniw was supported by the grants RYC2021-033192-I and PID-2022-141004OA-100; Irene Mendoza by the grants PID2020-115129RJI00 and TED2021-129871A-I00 by MCIN/AEI/10.13039/501100011033 by “European Union NextGenerationEU/PRTR”; This project has received funding from the European Union's Horizon 2021 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101064340 to Virginia Domínguez-García. María José Ruiz-López was supported by the grant PID2020-118921RJ-I00; Javier Puy by grant FJC2020-042954-I by MCIN/AEI/10.13039/501100011033 by “European Union NextGenerationEU/PRTR”; Laetitia M. Navarro, Alberto García-Rodríguez, Elena Tena, José M. Reyes-González, Simone Guareschi, Pablo Homet, Pablo Villalva and Eva Moracho were funded by the Spanish Ministry of Science and Innovation project through European Regional Development Fund (SUMHAL, LIFEWATCH-2019-09-CSIC-4, POPE 2014–2020). Cosme López-Calderón was supported by Juan de la Cierva fellowship (FJC2019-042541-I). Elena Velado-Alonso received funding from the project SHOWCASE (SHOWCASing synergies between agriculture, biodiversity and ecosystem services to help farmers capitalizing on native biodiversity) within the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 862480. Cristina Coccia acknowledges the support of NBFC to Roma Tre University, under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4—Call for Tender No. 3138 of 16 December 2021, rectified by Decree No. 3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union—NextGenerationEU. Project Code CN00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP F83C22000730006. Alberto García-Rodríguez, Elena Tena, and Iñigo Donázar-Aramendía were additionally supported by the EU-NextGenerationEU funds through the 2021–2023 Margarita Salas call for the requalification of the Spanish university system, convened by the Universidad de León, Universidad de Salamanca, and Universidad de Sevilla, respectively. Sebastiano de Franciscis was funded by the Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020) of Junta de Andalucía (PY20\_00765) granted to M.A.F. Zulima Tablado was supported by the NaturaConnect project within the Horizon Europe funding scheme (Grant agreement ID: 101060429). Marcello D'Amico was supported by the Juan de la Cierva Incorporación Program from the Spanish Ministry of Science, Innovation and Universities (IJC2019-039662-I), and the project “Plan Complementario de I+D+i en el área de Biodiversidad (PCBIO)” funded by the European Union within the framework of the Recovery, Transformation and Resilience Plan—NextGenerationEU and by the Regional Government of Andalusia, This work contributes to the Institut de Ciències del Mar "Severo Ochoa Centre of Excellence" accreditation CEX2024-001494-S funded by AEI 10.13039/501100011033 of the Spanish Ministry of Science and Innovation, Peer reviewed

Earthworms are key drivers of soil function, influencing organic matter turnover, nutrient cycling, and soil structure. Understanding the environmental controls on their distribution is essential for predicting the impacts of land use and climate change on soil ecosystems. While local studies have identified abiotic drivers of earthworm communities, broad-scale spatial patterns remain underexplored. We developed a multi-species, multi-task deep learning model to jointly predict the distribution of 77 earthworm species across metropolitan France, using historical (1960–1970) and contemporary (1990–2020) records. The model integrates climate, soil, and land cover variables to estimate habitat suitability. We applied SHapley Additive exPlanations (SHAP) to identify key environmental drivers and used species clustering to reveal ecological response groups. The joint model achieved high predictive performance (TSS >0.7) and improved predictions for rare species compared to traditional species distribution models. Shared feature extraction across species allowed for more robust identification of common and contrasting environmental responses. Precipitation variability, temperature seasonality, and land cover emerged as dominant predictors of earthworm distribution but differed in ranking across species and functional groups. Species clustering into response groups to climatic, land use and soil revealed distinct ecological strategies including a gradient of sensitivity to precipitation seasonality, differential habitat preferences in terms of vegetation cover and wetness and trade-offs between soil acidity and organic matter quality. Our study advances both the methodological and ecological understanding of soil biodiversity. We demonstrate the utility of interpretable deep learning approaches for large-scale soil fauna modeling and provide new insights into earthworm habitat specialization. These findings highlight land cover and seasonal climate variability as efficient proxies for soil biodiversity, providing actionable indicators for global monitoring initiatives and helping to identify habitat requirements of earthworm species to guide emerging earthworm conservation strategies in the face of global environmental change.