Data from: An environmental habitat gradient and within-habitat segregation enable co-existence of ecologically similar bird species
Data files
Jul 14, 2023 version files 315.61 MB
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Data_HCDSM5sp.zip
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Data_HCDSM63sp.zip
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Data_indices_figures.zip
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Data_null_model_body_size.zip
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Data_null_models_figures_elev_diet_strata.zip
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Data_Rank_abundance_plots.zip
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Data_spatial_site_index.zip
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README.md
Abstract
Niche theory predicts that ecologically similar species can co-exist through multidimensional niche partitioning. However, due to the challenges of accounting for both abiotic and biotic processes in ecological niche modelling, the underlying mechanisms that facilitate co-existence of competing species are poorly understood. In this study, we evaluated potential mechanisms underlying the co-existence of ecologically similar bird species in a biodiversity-rich transboundary montane forest in east-central Africa by computing niche overlap indices along an environmental elevation gradient, diet, forest strata, activity patterns, and within-habitat segregation across horizontal space. We found strong support for abiotic environmental habitat niche partitioning, with 55% of species pairs having separate elevation niches. For the remaining species pairs that exhibited similar elevation niches, we found that within-habitat segregation across horizontal space and to a lesser extent vertical forest strata provided the most likely mechanisms of species co-existence. Co-existence of ecologically similar species within a highly diverse montane forest was determined primarily by abiotic factors (e.g., environmental elevation gradient) that characterize the Grinnellian niche and secondarily by biotic factors (e.g., vertical and horizontal segregation within habitats) that describe the Eltonian niche. Thus, partitioning across multiple levels of spatial organization is a key mechanism of co-existence in diverse communities.
Usage notes
All the datasets are in .csv format and .Rdata format.
Accessable using R programing software (open-source) for statistical analysis.