Using a large-scale biodiversity monitoring dataset to test the effectiveness of protected areas in conserving North-American breeding birds
Protected areas currently cover about 15% of the global land area and constitute one of the main tools in biodiversity conservation. Quantifying their effectiveness at protecting species from decline and local extinction involves comparing protected with counterfactual unprotected sites representing \"what would have happened to protected sites if they had not been protected\". Most studies are based on pairwise comparisons, using as counterfactuals neighbour sites to protected areas, but this choice is often subjective and may be prone to biases. An alternative is to use large-scale biodiversity monitoring datasets, which whereby the effect of protected areas is analysed statistically by controlling for landscape differences between protected and unprotected sites, allowing a more targeted and clearly defined measure of protected areas effect. Here we use the North American Breeding Bird Survey dataset as a case study to investigate protected areas effectiveness in conserving bird assemblages. We analysed the effect of protected areas on species richness, assemblage-level abundance and abundance of individual species by modelling how these metrics relate to the proportion of each site that is protected, while controlling for site habitat, altitude, productivity and spatial autocorrelation. At the assemblage level, we found no relationship between protection and species richness or overall abundance. At the species level, we found that species that avoid human activities tend to be favoured by protected areas are the one avoiding human activities. Moreover, we found that forest protected areas presented higher abundances of forest species, making the assemblage more typical of this habitat. We did not find that declining species were particularly favoured by protected areas. Our results highlight the complexity of answering the question of protected areas effectiveness, and the necessity to define clearly metrics measured and the controls used.