Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.     

Socioeconomic development and ecological traits as predictors of human–bird conflicts

Because of the significant impacts on both human interests and bird conservation, it is imperative to identify patterns and anticipate drivers of human–bird conflicts (HBCs) worldwide. Through a global systematic review, following the PRISMA 2020 guidelines, we analyzed the socioeconomic factors and bird ecological traits driving the degree of knowledge and extent of HBCs. We included 166 articles published from 1971 to 2020 in our analyses through which we built a profile of the socioeconomic conditions of 52 countries with reported conflicts and the ecological traits of the 161 bird species involved in HBCs. Although HBC expanded worldwide, it had the greatest impact in less-developed countries (estimate 0. 66 [SE 0.13], p< 0.05), where agriculture is critical for rural livelihoods. Species with a relatively greater conflict extent had a relatively broader diet (estimate 0.80 [SE 0.22], p<0.05) and an increasing population trend (estimate 0.58 [SE 0.15], p<0.05) and affected human interests, such as agriculture and livestock raising. In countries with greater biodiversity, HBCs caused greater socioeconomic impacts than in more developed countries. Our results highlight the importance of understanding and addressing HBCs from multiple perspectives (ecological, sociocultural, and political) to effectively protect both biodiversity and local livelihoods.