RESUMEN

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point-count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out-performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species-specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision-making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.

RESUMEN

Home ranges provide a conceptual and quantitative representation of animal-habitat associations over time. Methods to estimate home ranges have swiftly progressed by dynamically accounting for various sources of bias. Across that period of growth, one potentially influential source of bias has yet to be robustly scrutinized. Animals inhabiting the terrestrial spatial domain make movement decisions in environments with variable landscape complexity. Despite that reality, home range estimation methods tend to be informed by two-dimensional (2D) data (i.e., x and y coordinates), which analytically presume that these landscapes are flat. This analytical tendency potentially misrepresents the configuration and size of animal home range estimates. To examine the prevalence of this bias, we reviewed literature of terrestrial animal home range estimation published between 2000 and 2019. We recorded the proportion of studies that (1) recognized and (2) incorporated landscape complexity. Over 22.0% (n = 271) of the 1,203 studies recognized the importance of landscape complexity for animal movement. Interestingly, just 0.7% (n = 8) incorporated landscape complexity into the home range estimation. We infer then that landscape complexity represents an important source of bias resulting in the underestimation of terrestrial animal home range size. Given the influence of landscape complexity on terrestrial animal decision making, energetics, and fitness, our analysis highlights an important gap in current home range methodologies. We discuss the implications of our analysis for biased understandings of terrestrial animal spatial ecology with subsequent impacts on management and conservation practices built upon these estimates.

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RESUMEN

Developing techniques to quantify the spread and severity of diseases afflicting wildlife populations is important for disease ecology, animal ecology, and conservation. Giraffes (Giraffa camelopardalis) are in the midst of a dramatic decline, but it is not known whether disease is playing an important role in the broad-scale population reductions. A skin disorder referred to as giraffe skin disease (GSD) was recorded in 1995 in one giraffe population in Uganda. Since then, GSD has been detected in 13 populations in seven African countries, but good descriptions of the severity of this disease are not available. We photogrammetrically analyzed camera trap images from both Ruaha and Serengeti National parks in Tanzania to quantify GSD severity. Giraffe skin disease afflicts the limbs of giraffes in Tanzania, and we quantified severity by measuring the vertical length of the GSD lesion in relation to the total leg length. Applying the Jenks natural breaks algorithm to the lesion proportions that we derived, we classified individual giraffes into disease categories (none, mild, moderate, and severe). Scaling up to the population level, we predicted the proportion of the Ruaha and Serengeti giraffe populations with mild, moderate, and severe GSD. This study serves to demonstrate that camera traps presented an informative platform for examinations of skin disease ecology.

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RESUMEN

Examining the ways in which animals use habitat and select resources to satisfy their life history requirements has important implications for ecology, evolution, and conservation. The advent of radio-tracking in the mid-20th century greatly expanded the scope of animal-habitat modeling. Thereafter, it became common practice to aggregate telemetry data collected on a number of tagged individuals and fit one model describing resource selection at the population level. This convention, however, runs the risk of masking important individuality in the nature of associations between animals and their environment. Here, we investigated the importance of individual variation in animal-habitat relationships via the study of a highly gregarious species. We modeled elk (Cervus elaphus) location data, collected from Global Positioning System (GPS) collars, using Bayesian discrete choice resource selection function (RSF) models. Using a high-performance computing cluster, we batch-processed these models at the level of each individual elk (n = 88) and evaluated the output with respect to: (a) the composition of parameters in the most supported models, (b) the estimates of the parameters featured in the global models, and (c) spatial maps of the predicted relative probabilities of use. We detected considerable individual variation across all three metrics. For instance, the most supported models varied with respect to parameter composition with a range of seven to 17 and an average of 14.4 parameters per individual elk. The estimates of the parameters featured in the global models also varied greatly across individual elk with little conformity detected across age or sex classes. Finally, spatial mapping illustrated stark differences in the predicted relative probabilities of use across individual elk. Our analysis identifies that animal-habitat relationships, even among the most gregarious of species, can be highly variable. We discuss the implications of our results for ecology and present some guiding principles for the development of RSF models at the individual-animal level.