Predictive modeling for allopatric Strix (Strigiformes: Strigidae) owls in South America: determinants of their distributions and ecological niche‐based processes

Strix (Strigidae) is a worldwide genus of 17 owl species typical of forested habitats, including Rusty‐barred Owls (S. hylophila), Chaco Owls (S. chacoensis), and Rufous‐legged Owls (S. rufipes) in South America. These species are distributed allopatrically, but the ecological traits that determine their distributions remain largely unknown and their phylogenetic relationships are unclear. We used species distribution models (SDMs) to identify variables explaining their distribution patterns and test hypotheses about ecological divergence and conservatism based on niche overlap analysis. For Rusty‐barred Owls and Chaco Owls, climatic factors related to temperature played a major role, whereas a rainfall variable was more important for Rufous‐legged Owls. When niche overlaps were compared, accounting for regional similarities in the habitat available to each species, an ecological niche divergence process was supported for Chaco Owl‐Rusty‐barred Owl and Chaco Owl‐Rufous‐legged Owl, whereas a niche conservatism process was supported for Rusty‐barred Owl‐Rufous‐legged Owl. Different ecological requirements support current species delimitation, but they are in disagreement with the two main hypotheses currently envisaged about their phylogenetic relationships (Chaco Owls as the sister taxa of either Rufous‐legged Owls or Rusty‐barred Owls) and support a new phylogenetic hypothesis (Rufous‐legged Owls as sister taxa of Rusty‐barred Owls). Our findings suggest that speciation of Rusty‐barred Owls and Rufous‐legged Owls was a vicariant event resulting from Atlantic marine transgressions in southern South America in the Miocene, but their niche was conserved because habitat changed little in their respective ranges. In contrast, Chaco Owls diverged ecologically from the other two species as a result of their adaptations to the habitat they currently occupy. Ecological and historical approaches in biogeography can be embedded to explain distribution patterns, and results provided by SDMs can be used to infer historical and ecological processes in an integrative way.     

Diet of Neotropical parrots is independent of phylogeny but correlates with body size and geographical range

Body mass and geographical range are two main drivers of diet in animals, yet how these factors influence diet in the morphologically and ecologically diverse avian group of Psittaciformes is little known. We reviewed current knowledge of the diet of Neotropical parrots and assessed the relation between diet (breadth and composition), phylogeny, body mass and geographical range. Diet has been documented for 98 of 165 species, but information is available only for 34 of 59 threatened species, and countries with high species diversity (> 20 species) had few studies (one to seven). Neotropical parrot species consumed 1293 plant species of 125 families. When assessing the relative frequency of different food items in the diet (seed, fruits, flowers, leaves, nectar, bark and stems), we found that parrots mostly exploited seeds (41.9%) and fruits (38.3%) of native species. Diet overlap was very low among genera (0.006–0.321). At the species level, geographical range and body size explained the variation in diet composition. In particular, small parrots of restricted distribution had a distinct diet composition relative to either large or widely distributed species. Although body size and geographical range showed phylogenetic inertia, diet was independent of phylogenetic history. Our review not only reveals ecological factors explaining diet in a generalist group but also exposes information gaps across the Neotropical region.     

Hybrid larch heterosis: for which traits and under which genetic control?

RCD1 is a member of the plant-specific SRO protein family. Several SRO genes have been functionally identified in the regulation of abiotic stresses in Arabidopsis and other plant species. However, the function of SROs is largely unknown in apple (Malus×domestica). In this study, six MdSRO-encoding genes were isolated, categorized into two types and mapped to six chromosomes. The phylogenetic analysis demonstrated that the sequences of the AtSRO and MdSRO proteins are highly conserved. Subsequently, expression analysis showed that MdSRO genes had different expression profiles in different tissues and in response to various stresses. Finally, MdRCD1 was isolated for functional identification. The results showed that resistance to oxidation stress in apple calli was enhanced by MdRCD1 overexpression and weakened by MdRCD1 suppression. MdRCD1 also played a crucial role in the regulation of ROS homeostasis in transgenic apple calli and Arabidopsis. Ectopic expression of MdRCD1 significantly enhanced resistance to salt and oxidative stresses in transgenic lines. In addition, MdRCD1 also enhanced drought tolerance due to its influence on stomatal opening. Based on these results, we conclude that MdRCD1 is an important regulator in abiotic stress response.

     

Energetics in Liolaemini lizards: implications of a small body size and ecological conservatism

Liolaemini lizards occur in southern South America in a variety of dietary habits across a broad latitudinal and altitudinal distribution. We studied standard metabolic rates of 19 Liolaemini species and analyzed these data using both conventional and phylogenetically informed statistics. Oxygen consumption showed a significant and positive relationship with body mass (SMR = 0.109 × body mass^0.876±0.023), with a higher slope than that expected on the basis of the three-quarter power law model. After phylogenetically informed and conventional analyses, no significant differences in metabolic rates were found to be related to diet or elevation. We hypothesize that small body size, ecological conservatism and physiological compensation may explain the lack of differences in metabolic rates observed among these lizards.     

Accounting for preferential sampling in species distribution models

Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as preferential sampling, which can lead to biased predictions of the distribution of the species. The aim of this study is to discuss a SDM that addresses this problem and that it is more computationally efficient than existing MCMC methods. From a statistical point of view, we interpret the data as a marked point pattern, where the sampling locations form a point pattern and the measurements taken in those locations (i.e., species abundance or occurrence) are the associated marks. Inference and prediction of species distribution is performed using a Bayesian approach, and integrated nested Laplace approximation (INLA) methodology and software are used for model fitting to minimize the computational burden. We show that abundance is highly overestimated at low abundance locations when preferential sampling effects not accounted for, in both a simulated example and a practical application using fishery data. This highlights that ecologists should be aware of the potential bias resulting from preferential sampling and account for it in a model when a survey is based on non‐randomized and/or non‐systematic sampling.