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The relationship between morphological traits and ecological performance in birds is an important area of research, as it can help us to understand how birds are able to adapt and how they are affected by changes in their environment. Many studies have investigated the relationship between morphological traits and certain aspects of the performance and ecological niche of these animals. However, the relationship between morphological traits and the primary lifestyle of birds has not previously been explored. This paper aims to evaluate the predictive potential of morphological data to determine the primary lifestyle of birds through a tree-based machine learning algorithm. By doing this, it is also possible to evaluate these artificial categories that we used to split up birds and know whether they are suitable for dividing them in function of shared morphological characteristics or need a redefinition under more discriminant criteria. Supplementary dataset 1 of the AVONET project was used, which comprises the 11 morphological predictors used in this work and the classification according to the primary lifestyle for more than 95% of the existing bird species. For all morphological traits used, statistically significant univariate differences were found between primary lifestyles. The three fitted machine learning models showed high accuracy, in all cases above 78% and superior to the ones achieved through traditional approaches used as contrasts. The results obtained provide evidence that primary lifestyle can be predicted in birds based on morphological traits, as well as more insights about the relevance of functional traits for ecological modeling. This is another step forward in our mechanistic understanding of bird ecology, while exploring how birds have adapted to their environments and how they interact with their surroundings. [ABSTRACT FROM AUTHOR]

The queen conch, Aliger gigas is a large marine gastropod mollusc that inhabits shallow seagrass meadows and sandplain habitats throughout the Caribbean. Owing to overfishing and degradation of the environment, it has become an endangered species in most of its habitat. Research on Cuban populations of A. gigas is scarce and mostly focused on conch exploitation. The present study assesses, for the first time, the genetic diversity and population structure of A. gigas from six locations on the Cuban platform, using five microsatellite loci. At most of the sampled locations, the observed heterozygosity was lower than expected and the F IS value was significantly positive, both of which suggest inbreeding. Also, we found evidence of bottlenecks for two of the locations with a deficit of heterozygotes. Although statistically significant, only 1.66% of the total variance was explained by genetic differentiation among populations, and discriminant analysis of principal components showed different degrees of overlapping between all locations. Accordingly, the results showed that there is an extensive and symmetrical exchange of genetic information. Sparse non-negative matrix factorization analysis determined the existence of three ancestral populations admixed across the sampled locations. Thus, our results suggest that A. gigas from the Cuban island platform could be a unique population, which is relevant for management and conservation strategies. [ABSTRACT FROM AUTHOR]