Sampling design may obscure species–area relationships in landscape-scale field studies

We investigated 1) the role of area per se in explaining anuran species richness on reservoir forest islands, after controlling for several confounding factors. We also assessed 2) how sampling design affects the inferential power of island species–area relationships (ISARs) aiming to 3) provide guidelines to yield reliable estimates of area-induced species losses in patchy systems. We surveyed anurans with autonomous recording units at 151 plots located on 74 islands and four continuous forest sites at the Balbina Hydroelectric Reservoir landscape, central Brazilian Amazonia. We applied semi-log ISAR models to assess the effect of sampling design on the fit and slope of species–area curves. To do so, we subsampled our surveyed islands following both a 1) stratified and 2) non-stratified random selection of 5, 10, 15, 20 and 25 islands covering 1) the full range in island size (0.45–1699 ha) and 2) only islands smaller than 100 ha, respectively. We also compiled 25 datasets from the literature to assess the generality of our findings. Island size explained ca half of the variation in species richness. The fit and slope of species–area curves were affected mainly by the range in island size considered, and to a very small extent by the number of islands surveyed. In our literature review, all datasets covering a range of patch sizes larger than 300 ha yielded a positive ISAR, whereas the number of patches alone did not affect the detection of ISARs. We conclude that 1) area per se plays a major role in explaining anuran species richness on forest islands within an Amazonian anthropogenic archipelago; 2) the inferential power of island species–area relationships is severely degraded by sub-optimal sampling designs; 3) at least 10 habitat patches spanning three orders of magnitude in size should be surveyed to yield reliable species–area estimates in patchy systems.     

Ecological traits modulate bird species responses to forest fragmentation in an Amazonian anthropogenic archipelago

Aim

We assessed patterns of avian species loss and the role of morpho‐ecological traits in explaining species vulnerability to forest fragmentation in an anthropogenic island system. We also contrasted observed and detectability‐corrected estimates of island occupancy, which are often used to infer species vulnerability.

Location

Tucuruí Hydroelectric Reservoir, eastern Brazilian Amazonia.

Methods

We surveyed forest birds within 36 islands (3.4–2,551.5 ha) after 22 years of post‐isolation history. We applied species–area relationships to assess differential patterns of species loss among three data sets: all species, forest specialists and habitat generalists. After controlling for phylogenetic non‐independence, we used observed and detectability‐corrected estimates of island occupancy separately to build competing models as a function of species traits. The magnitude of the difference between these estimates of island occupancy was contrasted against species detectability.

Results

The rate of species loss as a function of island area reduction was higher for forest specialists than for habitat generalists. Accounting for the area effect, forest fragmentation did not affect the overall number of species regardless of the data set. Only the interactive model including natural abundance, habitat breadth and geographic range size was strongly supported for both estimates of island occupancy. For 30 species with detection probabilities below 30%, detectability‐corrected estimates were at least tenfold higher than those observed. Conversely, differences between estimates were negligible or non‐existent for all 31 species with detection probabilities exceeding 45.5%.

Main conclusions

Predicted decay of avian species richness induced by forest loss is affected by the degree of habitat specialisation of the species under consideration, and may be unrelated to forest fragmentation per se. Natural abundance was the main predictor of species island occupancy, although habitat breadth and geographic range size also played a role. We caution against using occupancy models for low‐detectability species, because overestimates of island occupancy reduce the power of species‐level predictions of vulnerability.