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Ecological filtering shapes the impacts of agricultural deforestation on biodiversity

Nature Ecology & Evolution volume 8, pages 251–266 (2024)Cite this article

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Abstract

The biodiversity impacts of agricultural deforestation vary widely across regions. Previous efforts to explain this variation have focused exclusively on the landscape features and management regimes of agricultural systems, neglecting the potentially critical role of ecological filtering in shaping deforestation tolerance of extant species assemblages at large geographical scales via selection for functional traits. Here we provide a large-scale test of this role using a global database of species abundance ratios between matched agricultural and native forest sites that comprises 71 avian assemblages reported in 44 primary studies, and a companion database of 10 functional traits for all 2,647 species involved. Using meta-analytic, phylogenetic and multivariate methods, we show that beyond agricultural features, filtering by the extent of natural environmental variability and the severity of historical anthropogenic deforestation shapes the varying deforestation impacts across species assemblages. For assemblages under greater environmental variability—proxied by drier and more seasonal climates under a greater disturbance regime—and longer deforestation histories, filtering has attenuated the negative impacts of current deforestation by selecting for functional traits linked to stronger deforestation tolerance. Our study provides a previously largely missing piece of knowledge in understanding and managing the biodiversity consequences of deforestation by agricultural deforestation.

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Fig. 1: How natural and anthropogenic filtering can shape the observed biodiversity responses to agricultural deforestation.
Fig. 2: The geographical distribution of paired abundance data in our database.
Fig. 3: Variation in assemblage-level impacts of agricultural deforestation across studies and their underlying factors.
Fig. 4: The trait signature of filtering by environmental variability and historical deforestation.
Fig. 5: How the ‘greater-tolerance shift’ of assemblage trait centroids may have occurred under natural and anthropogenic filtering.

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Data availability

All data used in this study have been uploaded to a public repository, and can be accessed at https://doi.org/10.5281/zenodo.10031327 (ref. 31).

Code availability

All code used in this study have been uploaded to a public repository, and can be accessed at https://doi.org/10.5281/zenodo.10031327 (ref. 31).

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Acknowledgements

We thank all co-authors of the primary studies that enabled the generation of original data included in this study, Y. Chen for providing advice on spatial data extraction, L. Roudart for granting permission to use their map on agricultural history (Extended Data Fig. 3c, cited from ref. 81), and the following primary-study authors for help with data compilation: X.-B. Gao, D. S. Karp, O. Norfolk, N. O’Dea, B. Phalan, and T. R. S. Raman. We thank members of the ConservationEE research group at Peking University for helpful discussions and support, and M. G. Betts for constructive comments that improved the quality of earlier versions of the article. This project was funded by the National Natural Science Foundation of China (grants 32122057 and 3198810 to F.H.) and the Ministry of Science and Technology of China (grant 2022YFF0802300 to F.H.), and received further support from the Tsinghua University Initiative Scientific Research Program (grant 20223080017 to L.Y.).

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Author notes

  1. These authors contributed equally: Fangyuan Hua, Weiyi Wang.

Authors and Affiliations

  1. Institute of Ecology and Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Fangyuan Hua, Weiyi Wang, Shuangqi Liu & Xinran Miao

  2. School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia

    Weiyi Wang

  3. Evolution and Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia

    Shinichi Nakagawa

  4. Fenner School of Environment and Society, Australian National University, Canberra, Australian Capital Territory, Australia

    Xinran Miao

  5. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Le Yu & Zhenrong Du

  6. Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing, China

    Le Yu

  7. Tsinghua University (Department of Earth System Science)–Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing, China

    Le Yu

  8. Department of Botany, State Museum of Natural History Stuttgart, Stuttgart, Germany

    Stefan Abrahamczyk

  9. Laboratorio de Ecología de Organismos (GEO-UPTC), Escuela de Ciencias Biológicas, Universidad Pedagógica y Tecnológica de Colombia, Tunja, Colombia

    Luis Alejandro Arias-Sosa

  10. Department of Behavioural Ecology, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland

    Kinga Buda & Michał Budka

  11. Institut de Recherche pour le Développement, UMR SENS, IRD, CIRAD, Université Paul Valéry Montpellier 3, Université de Montpellier, Montpellier, France

    Stéphanie M. Carrière

  12. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA

    Richard B. Chandler

  13. Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy

    Gianpasquale Chiatante

  14. Centre for Biodiversity Information Development, Strathmore University, Nairobi, Kenya

    David O. Chiawo

  15. Centre of Biological Diversity, University of St Andrews, St Andrews, Scotland

    Will Cresswell

  16. Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, CA, USA

    Alejandra Echeverri

  17. Department of Health and Environmental Science, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Eben Goodale

  18. School of Environment and Science, Griffith University, Brisbane, Queensland, Australia

    Guohualing Huang

  19. Department of Life Sciences, Faculty of Science and Technology, University of the West Indies, St Augustine, Trinidad and Tobago

    Mark F. Hulme

  20. British Trust for Ornithology, Norfolk, UK

    Mark F. Hulme

  21. Division of Biological Sciences, University of Montana, Missoula, MT, USA

    Richard L. Hutto

  22. Ornithology Section, Zoology Department, National Museums of Kenya, Nairobi, Kenya

    Titus S. Imboma

  23. Department of Bird Migration, Swiss Ornithological Institute, Sempach, Switzerland

    Crinan Jarrett

  24. Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Zhigang Jiang

  25. College of Life Science, University of Chinese Academy of Sciences, Beijing, China

    Zhigang Jiang

  26. Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece

    Vassiliki I. Kati

  27. Northern Research Station, USDA Forest Service, Amherst, MA, USA

    David I. King

  28. Group for Conservation Biology, DOPPS BirdLife Slovenia, Ljubljana, Slovenia

    Primož Kmecl

  29. Institute of Eastern-Himalaya Biodiversity Research, Dali University, Dali, China

    Na Li

  30. Institute of Applied Ecology, Fujian University of Agriculture and Forestry, Fuzhou, China

    Gábor L. Lövei

  31. HUN-REN-DE Anthropocene Ecology Research Group, University of Debrecen, Debrecen, Hungary

    Gábor L. Lövei

  32. Instituto de Ecología Regional (IER), CONICET, Universidad Nacional de Tucumán, Tucumán, Argentina

    Leandro Macchi

  33. Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Lahti, Finland

    Ian MacGregor-Fors

  34. Institute of Animal Ecology and Systematic Zoology, Justus Liebig University of Gießen, Giessen, Germany

    Emily A. Martin

  35. MED (Mediterranean Institute for Agriculture, Environment and Development), CHANGE (Global Change and Sustainability Institute) and UBC (Conservation Biology Lab), Department of Biology, School of Sciences and Technology, University of Évora, Évora, Portugal

    António Mira

  36. Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic

    Federico Morelli

  37. Department of Life and Environmental Sciences, Bournemouth University, Poole, UK

    Federico Morelli

  38. Investigadoras e Investigadores por México del Consejo Nacional de Ciencia y Tecnología (CONACYT), Dirección Regional Occidente, Mexico City, Mexico

    Rubén Ortega-Álvarez

  39. Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, China

    Rui-Chang Quan

  40. MED (Mediterranean Institute for Agriculture, Environment and Development), CHANGE (Global Change and Sustainability Institute), Institute for Advanced Studies and Research and UBC (Conservation Biology Lab), University of Évora, Évora, Portugal

    Pedro A. Salgueiro & Sara M. Santos

  41. Ashoka Trust for Research in Ecology and the Environment, Bengaluru, India

    Ghazala Shahabuddin

  42. NCX, San Francisco, CA, USA

    Jacob B. Socolar

  43. National Parks Board, Singapore, Singapore

    Malcolm C. K. Soh

  44. Centre for Nature-based Climate Solutions, National University of Singapore, Singapore, Singapore

    Rachakonda Sreekar

  45. Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India

    Umesh Srinivasan

  46. School of Public and International Affairs and Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

    David S. Wilcove

  47. Shikoku Research Center, Forestry and Forest Products Research Institute, Kochi, Japan

    Yuichi Yamaura

  48. Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China

    Liping Zhou

  49. Global Conservation Program, Wildlife Conservation Society, Bronx, NY, USA

    Paul R. Elsen

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  1. Fangyuan Hua
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Contributions

F.H. conceived the study and led the study design. W.W. compiled species-level abundance data and associated meta-data with assistance from all co-authors. W.W. compiled species trait data with assistance from S.L. and X.M. F.H. designed and coded data analysis with assistance from S.N. and P.R.E., and along with W.W. implemented all analyses. F.H. designed visualization of the results, and along with W.W. implemented visualization of the results. F.H. wrote the first draft of the article with assistance from W.W. and S.N., and all authors contributed to revisions of the article.

Corresponding author

Correspondence to Fangyuan Hua.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 PRISMA plot for data compilation.

Reference information for the two databases and reviews consulted is provided in Extended Data Table 1.

Extended Data Fig. 2 Additional information on the range of data covered by our database.

As with Fig. 2, circles represent datasets of entire avian assemblages for agriculture-forest pairs contributed by each primary study, sized proportional to the number of avian assemblages and colored by (a) MAP, (b) seasonality, and (c) disturbance regime of the study system, as well as (d) remnant forest cover, distance to (e) the nearest continuous forest and (f) native forest surveyed for the agricultural sites in each primary study.

Extended Data Fig. 3 Geographical patterns of three filtering factors across the world.

(a) MAP, (b) temperature seasonality, and (c) agricultural history. Data for temperature seasonality and MAP are from WorldClim 2.139. Map boundaries in c show the centres of origin and areas of expansion of the Neolithic Agricultural Revolution, as reproduced with permission from ref. 81, Monthly Review Press.

Extended Data Fig. 4 Evidence that the influence of filtering on the observed impacts of agricultural deforestation was not an artefact of confounding variables or publication bias.

(a–d) The relationship between the four filtering variables and comparison type (left part) or study season (right part). While none of the filtering factors had no strong collinearity with comparison type, for at least MAP and agricultural history, there appeared to be some difference between breeding/all-year versus non-breeding seasons. (e, f) We therefore focused on a subset of data concerning the breeding season only (80% of all data) to visually assess the relationship between assemblage RR with MAP and agricultural history, using the same graph format as in Fig. 3. This subset of data also showed the negative effect of MAP (Fig. 3d) and the positive effect of agricultural history (Fig. 3e) on assemblage RR that were found by formal meta-regressions, suggesting that these effects were not spuriously driven by possible collinearity between filtering variables and study season. (g) Funnel plot for meta-analysis, based on effect size (RR) and study size (sampling effort; measured as the study duration in months). The dotted vertical line represents the mean effect size indicated by meta-analysis (that is corresponding to the mean of Fig. 3A, upper row). (h) The relationship between assemblage RR and the distance of agricultural sites to their matching native forests. We represented distance by the smallest distance from any sampling unit of the agricultural sites to matching native forest sites, with ‘close’ representing distances ≤1 km and ‘far’ those >1 km. This visual assessment showed that greater assemblage RR was not associated with shorter distances between agricultural sites and native forests, corroborating our main findings based on the FGP map data that distance to nearest continuous forest did not drive variation in biodiversity responses to agricultural deforestation.

Extended Data Fig. 5 Diagnostic plots for meta-analyses and meta-regressions corresponding to (a) Fig. 3a, (b) Fig. 3b, and (c) Fig. 3c–e.

For (a) and (b), residual plots (upper) and Q-Q plots (lower) are displayed for each of the meta-analyses concerning all agricultural types (left column), agroforestry (middle column), and open agricultural systems (right column) displayed in Fig. 3a, b. For (c), the residual plot (upper) and Q-Q plot (lower) correspond to the meta-regression global model.

Extended Data Fig. 6 Diagnostic plots for the phylogenetically controlled mixed-effect model on the relationship between species-level RR and predictor variables, run on one randomly drawn phylogenetic tree.

Plots for all variables other than generation length were from a model that dropped generation length, while the plot for generation length was from a model that dropped body mass. Pairs of plots on the trace (left) and density (right) of posterior estimates are displayed for each fixed factor and random factor including residual variance, or ‘Units’ (in dashed box).

Extended Data Table 1 List of syntheses, databases, and other studies consulted for identifying suitable primary studies. These studies are listed by type82,83,84,85,86,87,88,89,90,91,92,93
Full size table
Extended Data Table 2 Full list of primary studies included in our database. These studies are listed in alphabetical order94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130
Full size table
Extended Data Table 3 List of species functional traits considered in this study. Traits are listed by type (morphological, life history, and ecological), along with information on their definition, rationale for consideration in our study, and data sources131,132,133,134,135
Full size table
Extended Data Table 4 Two criteria that must be met simultaneously for classifying species into forest association categories
Full size table

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Hua, F., Wang, W., Nakagawa, S. et al. Ecological filtering shapes the impacts of agricultural deforestation on biodiversity. Nat Ecol Evol 8, 251–266 (2024). https://doi.org/10.1038/s41559-023-02280-w

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