Indicator species for avian biodiversity hotspots: Combination of specialists and generalists is necessary in less natural environments

In this work, I tested the premise that the distribution of a group of few common bird species can be used to predict bird species hotspots in Central Italy. The data on bird observations were collected on 530 sampled sites (150 in cultivated, 150 in forest, 150 in grassland and 80 in urban and peri-urban environments). In each environment, sampled sites with values of bird species richness in the upper than third quartile were classified as high species richness spots (HSRS), while sites with lower bird species richness were classified as non-HSRS (binary classification system).Generalized Linear Models (GLM) were applied using HSRS or non-HSRS as binomial response variable and bird species occurrence was used as the predictor variable. All selected models showed “fair” or “good” capacities to predict the avian hotspots, using only few common birds (4–6) species. However, bird species selected as predictors were different on each environment. In more natural environments (grassland, forest), specialist species were selected, while in most disturbed environments (cultivated and urban) both generalist and specialist species were selected. The results are in agreement with other studies which show how homogenization of bird communities is strongly correlated to landscape disturbance. The findings supports the hypothesis that indicators have to incorporate both specialists and generalist’s species simultaneously. Furthermore, the groups of birds selected as surrogates are easy to detect and this makes it possible to involve citizen-science programmes in obtain data. This approach can be a cheap and efficient and can help to significantly speed up the process of assessing ecosystems that might be under threat.