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Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

Nature Ecology & Evolution volume 7, pages 1079–1091 (2023)Cite this article

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Abstract

Species sensitivity to forest fragmentation varies latitudinally, peaking in the tropics. A prominent explanation for this pattern is that historical landscape disturbance at higher latitudes has removed fragmentation-sensitive species or promoted the evolution of more resilient survivors. However, it is unclear whether this so-called extinction filter is the dominant driver of geographic variation in fragmentation sensitivity, particularly because climatic factors may also cause latitudinal gradients in dispersal ability, a key trait mediating sensitivity to habitat fragmentation. Here we combine field survey data with a morphological proxy for avian dispersal ability (hand-wing index) to assess responses to forest fragmentation in 1,034 bird species worldwide. We find that fragmentation sensitivity is strongly predicted by dispersal limitation and that other factors—latitude, body mass and historical disturbance events—have relatively limited explanatory power after accounting for species differences in dispersal. We also show that variation in dispersal ability is only weakly predicted by historical disturbance and more strongly associated with intra-annual temperature fluctuations (seasonality). Our results suggest that climatic factors play a dominant role in driving global variation in the impacts of forest fragmentation, emphasizing the need for more nuanced environmental policies that take into account local context and associated species traits.

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Fig. 1: Hypotheses predicting the distribution of fragmentation-sensitive species.
Fig. 2: Global patterns of landscape disturbance and dispersal limitation.
Fig. 3: Fragmentation sensitivity increases with dispersal limitation in bird assemblages.
Fig. 4: Dispersal limitation (nHWI) explains variation in fragmentation sensitivity.
Fig. 5: Predictors of dispersal limitation in birds.

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Data availability

All data are available at https://github.com/tomlweeks1994/Dispersal_mediates_fragmentation_sensitivity.

Code availability

The code to conduct analyses and replicate figures is available at https://github.com/tomlweeks1994/Dispersal_mediates_fragmentation_sensitivity.

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Acknowledgements

We thank the BIOFRAG project for supplying data and numerous data contributors, including V. Arroyo-Rodriguez, D. Cleary, H. Jactel, J. Karubian, J. Lasky, S. Melles, J. C. Morante Filho, V. Proenca, S. Raman, P. Round and J. Terraube. Trait data collection was supported by Natural Environment Research Council grant NE/I028068/1 and UKRI Global Challenges Research Fund grant ES/P011306/1 (J.A.T.). Analysis was funded by the Natural Environment Research Council studentship through the Science and Solutions for a Changing Planet Doctoral Training Programme NE/S007415/1 (T.L.W.). Illustrations were reproduced with permission from the Cornell Lab of Ornithology. For the purpose of open access, T.L.W. has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising.

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Authors and Affiliations

  1. Department of Life Sciences, Imperial College London, Ascot, UK

    Thomas L. Weeks, Cristina Banks-Leite & Joseph A. Tobias

  2. Department of Life Sciences, Natural History Museum London, London, UK

    Thomas L. Weeks

  3. Forest Biodiversity Research Network, Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, USA

    Matthew G. Betts & Christopher Wolf

  4. School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne, UK

    Marion Pfeifer

  5. Dynafor, University of Toulouse, INRAE, Castanet-Tolosan, France

    Luc Barbaro

  6. CESCO, Museum National d’Histoire Naturelle, CNRS, Sorbonne-University, Paris, France

    Luc Barbaro

  7. Lancaster Environmental Centre, Lancaster University, Lancaster, UK

    Jos Barlow

  8. Foundation for Ecodevelopment and Conservation (FUNDAECO), Ciudad de Guatemala, Guatemala

    Alexis Cerezo

  9. Global Protect Oceans, Lands and Waters Program, The Nature Conservancy, Fort Collins, CO, USA

    Christina M. Kennedy

  10. Swiss Ornithological Institute, Sempach, Switzerland

    Urs G. Kormann

  11. Department of Ecology and Evolution, and Yale Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA

    Charles J. Marsh

  12. Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA

    Charles J. Marsh

  13. M.A.P Scientific Services, Pretoria, South Africa

    Pieter I. Olivier

  14. Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa

    Pieter I. Olivier

  15. Centre for Conservation of Atlantic Forest Birds, Parque das Aves, Foz do Iguaçu, Brazil

    Benjamin T. Phalan

  16. School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia

    Hugh P. Possingham

  17. Department of Biological Sciences, California State University Los Angeles, Los Angeles, CA, USA

    Eric M. Wood

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  1. Thomas L. Weeks
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  4. Christopher Wolf
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Contributions

T.L.W. and J.A.T. conceived and developed the study, with input from M.G.B., M.P. and C.W.; data from particular sites were contributed by M.B., C.B.-L., L.B., J.B., A.C., C.M.K., U.G.K., C.J.M., P.I.O., B.T.P., H.P.P. and E.M.W.; T.L.W. integrated datasets and ran all analyses with support from C.W. and M.G.B.; and T.L.W. wrote the first version of the manuscript and designed all figures with input from J.A.T. All authors contributed to subsequent drafts and gave final permission for publication.

Corresponding author

Correspondence to Thomas L. Weeks.

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Nature Ecology & Evolution thanks Benjamin Zuckerberg, Don Driscoll and Jacob Socolar for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Correlation between disturbance and latitude.

Study landscapes exposed to high levels of historical disturbance (n = 16 landscapes; red) tend to be found at higher latitudes than landscapes exposed to lower levels of historical disturbance (n = 15 landscapes; blue). Disturbance level is estimated from global maps of major historical disturbance (for example fire, glaciation). Absolute latitude is the centroid latitude of all sampling points in each study landscape. Boxplots show the median, interquartile range and whiskers extending to extreme values. Statistics show results of two-sided Wilcoxon rank sum test indicating that disturbance and latitude are correlated (without accounting for spatial auto-correlation).

Extended Data Fig. 2 The latitudinal gradient in average dispersal limitation of bird assemblages.

Data points (coloured by level of historical disturbance) show the community mean values for avian assemblages sampled at 31 study landscapes mapped in Fig. 1. The overall gradient is not explained by landscape disturbance history. Absolute latitude is the centroid latitude of all sampling points in each study landscape. Mean dispersal limitation is the negative (that is inverse) hand-wing index (nHWI) averaged across all species in the assemblage; nHWI is logarithmically scaled (log(1/HWI)) for visualization. Statistics are from a linear model with Gaussian errors; purple line shows model fit (R2 = 0.44); shaded region shows the standard error of the regression coefficient.

Extended Data Fig. 3 Correlation between fragmentation sensitivity and latitude in birds.

Data points (coloured by level of historical disturbance) are community mean values for avian assemblages at 31 study landscapes mapped in Fig. 1. For each assemblage, fragmentation sensitivity is assigned to (a) Forest-specialist species with ‘Forest-core’ habitat preference (Restricted analysis), and (b) Forest-associated species with ‘Forest-core’ habitat preference (Expanded analysis). Absolute latitude is the absolute centroid latitude of all sampling points in each study landscape. Statistics are from generalized linear models with quasi-binomial errors; purple line shows model fit (Restricted analysis: R2 = 0.2559, Expanded analysis: R2 = 0.3208); shaded region shows the 95% confidence intervals.

Extended Data Fig. 4 Correlation between fragmentation sensitivity and dispersal limitation in birds.

Data points (coloured by level of historical disturbance) are community mean values for avian assemblages at 31 study landscapes mapped in Fig. 1. For each assemblage, fragmentation sensitivity is assigned to species with ‘Forest-core’ habitat preference and either a high or medium forest dependency (Expanded analysis). Mean dispersal limitation is the negative (that is inverse) hand-wing index (nHWI) averaged across all species in the assemblage; nHWI is logarithmically scaled (log(1/HWI)) for visualization. Statistics are from a generalized linear model with quasi-binomial errors; purple line shows model fit (R2 = 0.270); shaded region shows 95% confidence intervals. Adjacent boxplots show the same distribution with median value, interquartile range, and whiskers to extreme values (outliers are data points >1.5x quartiles).

Extended Data Fig. 5 Drivers of fragmentation sensitivity with Anthropogenic disturbances.

Results of Bayesian phylogenetic mixed effect models predicting fragmentation sensitivity for 1564 bird populations (n = 1034 species). Populations were classified as fragmentation sensitive if they were identified as ‘Forest-core’ by BIOFRAG. Restricted analysis assigned fragmentation sensitivity only to ‘Forest specialists’ (a); Expanded analysis assigned fragmentation sensitivity to both ‘Forest specialist’ and ‘Forest associated’ species (b; see Methods). Bayesian posterior distribution is shown above the line; effect size estimates with credible intervals (CI) below the line (68%: thick errorbars; 95%: thin errorbars). High effect sizes indicate a positive association with fragmentation sensitivity; low effect sizes indicate a negative association. Finch and hawk silhouettes indicate that both models were run on a complete sample. Historical disturbance is a binary variable (1/0) calculated using anthropogenic disturbance (forest loss) only.

Extended Data Fig. 6 Drivers of fragmentation sensitivity with natural disturbances.

Results of Bayesian phylogenetic mixed effect models predicting fragmentation sensitivity for 1564 bird populations (n = 1034 species). Populations were classified as fragmentation sensitive if they were identified as ‘Forest-core’ by BIOFRAG. Restricted analysis assigned fragmentation sensitivity only to ‘Forest specialists’ (a); Expanded analysis assigned fragmentation sensitivity to both ‘Forest specialist’ and ‘Forest associated’ species (b; see Methods). Bayesian posterior distribution is shown above the line; effect size estimates with credible intervals (CI) below the line (68%: thick errorbars; 95%: thin errorbars). High effect sizes indicate a positive association with fragmentation sensitivity; low effect sizes indicate a negative association. Finch and hawk silhouettes indicate that both models were run on a complete sample. Historical disturbance is a binary variable (1/0) calculated using natural disturbance (for example fires, storms & glaciation) layers only.

Extended Data Fig. 7 Predictors of dispersal limitation in birds.

Results shown are outputs of phylogenetic least squares model predicting dispersal limitation (nHWI) across all bird species sampled, including long-distance migrants (swallow image, dark bars; n = 1034); only resident species and short distance/partial migrants (thrush image, medium bars; n = 921); or resident species only (pitta image, pale bars; n = 858). Panels present three sets of models with increasing complexity: a univariate model with single predictor (a,d), and multivariate models with two (b,e) and three (c,f) predictors. Each predictor is calculated at the species level by averaging across landscapes where each species is present. Disturbance (red) is calculated as the proportion of species breeding range which overlaps areas of high natural (e.g. storms, glaciers, fires) or anthropogenic (e.g. forest loss) disturbance. Absolute latitude (yellow) is calculated as the centroid latitude of the species breeding range. Seasonality (blue) is calculated as the standard deviation of mean monthly temperature values throughout the year, averaged across all grid cells in the breeding range. a–c, Effect size estimates are given with 95% confidence intervals; a negative effect indicates reduced dispersal limitation (that is increased dispersal ability). R2 and AIC values are calculated for full sample models only. d–f, Proportion of independent variation explained by each model covariate, calculated using hierarchical partitioning.

Extended Data Fig. 8 Relationship between dispersal limitation (nHWI) and diet.

Data shown for (a) 276 bird species sampled across 18 temperate study landscapes, and (b) 817 bird species sampled across 13 tropical study landscapes. Dietary classes with <5 species were removed from the analysis. Diet classifications are from Tobias and Pigot110. F-statistic and P-value are calculated with a two-way ANOVA. Boxplots show median, interquartile range, and whiskers to extreme values (outliers are data points >1.5x quartiles).

Extended Data Fig. 9 Correlation between seasonality and disturbance.

At the local landscape level (a), seasonality is calculated as the standard deviation of mean monthly temperature values throughout the year at the landscape centroid (n = 31). High disturbance means 50% of the study landscape area overlaps areas of high natural (for example storms, glaciers, fires) or Anthropogenic (for example forest loss). Boxplots show median, interquartile range, and whiskers to extreme values (outliers are data points >1.5x quartiles). Statistics are from a two-sided Wilcoxon test. At the species level (b), community mean values (n = 31), are calculated using species’ distributional seasonality and disturbance scores. Disturbance is calculated as the proportion of the species breeding range which overlaps areas of high natural (for example storms, glaciers, fires) or anthropogenic (for example forest loss) disturbance. Seasonality is calculated as the standard deviation of mean monthly temperature values throughout the year, averaged across all grid cells in the species’ breeding range. Statistics are from a linear regression with Gaussian errors; purple line shows model fit; shaded area is 95% confidence intervals.

Extended Data Table 1 Predictors of fragmentation sensitivity using all disturbance layers
Full size table

Supplementary information

Supplementary Information

Supplementary Methods, Analyses, Tables 1–7, Figs. 1–4 and References.

Reporting Summary

Supplementary Data 1

Supplementary dataset containing all data to recreate analyses and figures. Sheet 1. All species data (n = 1,034), with corresponding taxonomic, life-history, morphological and distributional data. Sheet 2. Landscape data (n = 31), with sample period, sample method and sample structure, geographical data, local seasonality and disturbance values, biome and matrix descriptions. Sheet 3. Species list per landscape (n = 1,564), with habitat preference classifications for each population within each landscape.

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Weeks, T.L., Betts, M.G., Pfeifer, M. et al. Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds. Nat Ecol Evol 7, 1079–1091 (2023). https://doi.org/10.1038/s41559-023-02077-x

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