Beauty before age: landscape factors influence bird functional diversity in naturally regenerating fragments, but regeneration age does not
Effective ecological restoration actions should be able to recover ecosystem processes that influence community development in the long term. However, there is scarce information on how landscape factors promote or accelerate fauna recovery. We used a landscape framework to evaluate how functional g...
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| Published in | Restoration ecology Vol. 24; no. 2; pp. 259 - 270 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Wiley Periodicals, Inc
01.03.2016
Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
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| Summary: | Effective ecological restoration actions should be able to recover ecosystem processes that influence community development in the long term. However, there is scarce information on how landscape factors promote or accelerate fauna recovery. We used a landscape framework to evaluate how functional groups respond to natural regeneration in a highly fragmented region of Atlantic Forest. Using bird functional groups sampled in 15 regenerating forest fragments, we built and ranked models using a model selection approach to test the relative effect of landscape variables on each group. Our results showed that bird community recovery is not determined by the duration of the regeneration process (i.e. forest age), but by how the species responds to the landscape context. Functional diversity and the abundance of the functional groups were mainly related to composition metrics, whereas the functional metric affected only specific groups. Our findings highlight the importance of considering the landscape level to ensure both the colonization of fauna and the restoration of ecological functions. |
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| Bibliography: | http://dx.doi.org/10.1111/rec.12293 Table S1. Six biological traits used in the analyses characterizing the "Eltonian" niches of bird species (diet, feeding guild, foraging strata, and body size), associated with the persistence of species in fragmented landscape (dependence on forested habitat), and linked to species dispersion and, consequently, the recolonizing processes in naturally regenerating forest fragments (movement through the matrix). Table S2. Bird species classified according to the functional groups obtained by regression tree analysis. Trait data were used to calculate the functional diversity and the regression tree to sort bird species into functional groups. Table S3. Number of birds sampled by point counts in 15 regenerating forest fragments (R) and in two control sites (C) within old growth forest in Minas Gerais, Brazil. Bird species were assigned to functional group based on trait data described in Table S2. Table S4. Highest ranked generalized linear models (GLM) and generalized additive models (GAM) using AICc-based model selection for bird functional diversity and the abundance of birds in each functional group. The ΔAICc is the difference in AICc values compared with the estimated best model (lowest AICc), which allows the ranking of models from best (top of the table) to worst. AICc weight is the estimated probability that a model is the best model in the set. Only functional groups that have models with ΔAIC < 2 and a null model >2 are included in the table. Significant coefficients obtained in the Wald test are highlighted with *. Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - No. 312045/2013-1 ArticleID:REC12293 istex:99ABDC1A9FB41F5A97CF18806EC88DDA2C4A8640 ark:/67375/WNG-28SNWRZW-X São Paulo Research Foundation - No. 2013/50421-2 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 1061-2971 1526-100X |
| DOI: | 10.1111/rec.12293 |