A new approach to modelling the relationship between annual population abundance indices and weather data

Weather has often been associated with fluctuations in population sizes of species; however, it can be difficult to estimate the effects satisfactorily because population size is naturally measured by annual abundance indices whilst weather varies on much shorter timescales. We describe a novel method for estimating the effects of a temporal sequence of a weather variable (such as mean temperatures from successive months) on annual species abundance indices. The model we use has a separate regression coefficient for each covariate in the temporal sequence, and over-fitting is avoided by constraining the regression coefficients to lie on a curve defined by a small number of parameters. The constrained curve is the product of a periodic function, reflecting assumptions that associations with weather will vary smoothly throughout the year and tend to be repetitive across years, and an exponentially decaying term, reflecting an assumption that the weather from the most recent year will tend to have the greatest effect on the current population and that the effect of weather in previous years tends to diminish as the time lag increases. We have used this approach to model 501 species abundance indices from Great Britain and present detailed results for two contrasting species alongside an overall impression of the results across all species. We believe this approach provides an important advance to the challenge of robustly modelling relationships between weather and species population size

Wave-like patterns of plant phenology determine ungulate movement tactics

Animals exhibit a diversity of movement tactics [1]. Tracking resources that change across space and time is predicted to be a fundamental driver of animal movement [2]. For example, some migratory ungulates (i.e., hooved mammals) closely track the progression of highly nutritious plant green-up, a phenomenon called ‘‘green-wave surfing’’ [3–5]. Yet general principles describing how the dynamic nature of resources determine movement tactics are lacking [6]. We tested an emerging theory that predicts surfing and the existence of migratory behavior will be favored in environments where green-up is fleeting and moves sequentially across large landscapes (i.e., wave-like green-up) [7]. Landscapes exhibiting wave-like patterns of greenup facilitated surfing and explained the existence of migratory behavior across 61 populations of four ungulate species on two continents (n = 1,696 individuals). At the species level, foraging benefits were equivalent between tactics, suggesting that each movement tactic is fine-tuned to local patterns of plant phenology. For decades, ecologists have sought to understand how animals move to select habitat, commonly defining habitat as a set of static patches [8, 9]. Our findings indicate that animal movement tactics emerge as a function of the flux of resources across space and time, underscoring the need to redefine habitat to include its dynamic attributes. As global habitats continue to be modified by anthropogenic disturbance and climate change [10], our synthesis provides a generalizable framework to understand how animal movement will be influenced by altered patterns of resource phenology

Efeitos da temperatura e da movimentação do ar sobre o isolamento térmico do velo de ovinos em câmara climática Effect of temperature and air velocity on the thermal insulation of the fleece of sheep in climatic chamber

Foram utilizados dez ovinos da raça Corriedale - cinco machos e cinco fêmeas com pesos entre 52,2 e 87,6 kg - com o objetivo de avaliar o efeito da combinação da movimentação do ar (0 e 5,0 m/s) com a temperatura do ar (25, 30 e 40ºC) sobre a temperatura retal (T R, ºC), da epiderme (T E, ºC), da superfície do velo (T V, ºC) e do interior do velo (T I, ºC) e a espessura do velo (E V, cm) e suas relações com o isolamento térmico do velo. A presença de vento não teve efeito sobre as variáveis estudadas, o que sugere que fluxo de ar (<5,0 m/s) paralelo ao eixo corporal do animal tem pouco efeito sobre o isolamento térmico do velo, independentemente da temperatura do ar, que se mostrou altamente correlacionada, de forma positiva, com as temperaturas retal, do velo, do interior do velo e da epiderme. Sob temperaturas inferiores a 30ºC, a transferência de calor através do velo ocorreu via condução e convecção livre, enquanto sob altas temperaturas (>40ºC) o fluxo de calor sensível não foi significativa.<br>Ten Corriedale sheep were evaluated, five males and five females with 52.2 to 87.6 kg of body weight, to evaluate the effect of air velocity (0 and 5 m/s) and temperature (25, 30 and 40ºC) on rectal temperature (T R, ºC), skin temperature (T E, ºC), fleece surface temperature (T V, ºC), temperature inside the fleece (T I, ºC), and fleece thickness (E V, cm) and their relationship with fleece thermal insulation. The results showed that air velocity did not affect the studied traits, suggesting that airflow <5.0 m/s parallel to the fleece had little effect on fleece thermal insulation, regardless the air temperature. Air temperature was highly correlated with T R, T E, T I and T V. Under air temperatures below 30°C, the heat transfer through the fleece was dominated by conduction and free convection, while under high temperatures (>40°C), the sensible heat flow was not significant

Solving the sample size problem for resource selection functions

Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals urn:x-wiley:2041210X:media:mee313701:mee313701-math-0001 and as many relocations per animal N as possible. These thresholds render many RSF-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than urn:x-wiley:2041210X:media:mee313701:mee313701-math-0002 animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies