Forest-type specialization strongly predicts avian responses to tropical agriculture

Abstract

Species' traits influence how populations respond to land-use change. However, even in well-characterized groups such as birds, widely studied traits explain only a modest proportion of the variance in response across species. Here, we show that associations with particular forest types strongly predict the sensitivity of forest-dwelling Amazonian birds to agriculture. Incorporating these fine-scale habitat associations into models of population response dramatically improves predictive performance and markedly outperforms the functional traits that commonly appear in similar analyses. Moreover, by identifying habitat features that support assemblages of unusually sensitive habitat-specialist species, our model furnishes straightforward conservation recommendations. In Amazonia, species that specialize on forests along a soil-nutrient gradient (i.e. both rich-soil specialists and poor-soil specialists) are exceptionally sensitive to agriculture, whereas species that specialize on floodplain forests are unusually insensitive. Thus, habitat specialization per se does not predict disturbance sensitivity, but particular habitat associations do. A focus on conserving specific habitats that harbour highly sensitive avifaunas (e.g. poor-soil forest) would protect a critically threatened component of regional biodiversity. We present a conceptual model to explain the divergent responses of habitat specialists in the different habitats, and we suggest that similar patterns and conservation opportunities probably exist for other taxa and regions.

Keywords: Amazon; birds; conservation; forest; habitat; traits.

Figure 1.

Habitat specializations used to predict population responses to agriculture. Regions of the diagram are not to scale but provide a rough idea of the relative size of each category. Forest presence and forest specialization are coarse-scale habitat associations, while the remaining habitat specializations are fine-scale. (Online version in colour.)

Figure 2.

Model performance in terms of (a) ELPD and (b) R². ELPD is expressed in terms of improvement over the null model, and significant differences are indicated above the bars: capitalization signifies a difference of at least 2 s.e. (95% confidence interval), while different letters signify a difference of at least 3 s.e. (99.7% confidence interval). R² is defined following the study by Nakagawa et al. [46]; bars display posterior mean estimates.

Figure 3.

Parameter estimates (posterior means and 95% credible intervals) for the effects of habitat associations and traits on abundance ratios in agriculture versus forest. Shading groups broad categories of predictors (labelled at right). (Online version in colour.)

Figure 4.

Modelled abundance ratios in agriculture versus forest, based on the global model. Species are ordered by their posterior means (points) and plotted with 95% credible intervals (lines). The first panel shows all 451 species analysed; subsequent panels highlight different subsets of the species pool. Forest-dwelling floodplain specialists are species that are both forest users and floodplain specialists.

Figure 5.

Mean pointwise richness and abundance (from raw data) of floodplain specialists across the landscape. Floodplain specialists proliferate in agricultural landscapes due to moderate increases in the floodplain itself as well as tremendous increases across the terra firme landscape. Error bars show ±1 s.e.