Opposing fitness consequences of habitat use in a harvested moose population

Geochemical landscapes as drivers of wildlife reproductive success: Insights from a high-Arctic ecosystem

The bioavailability of essential and non-essential elements in vegetation is expected to influence the performance of free-ranging terrestrial herbivores. However, attempts to relate the use of geochemical landscapes by animal populations directly to reproductive output are currently lacking. Here we measured concentrations of 14 essential and non-essential elements in soil and vegetation samples collected in the Zackenberg valley, northeast Greenland, and linked these to environmental conditions to spatially predict and map geochemical landscapes. We then used long-term (1996-2021) survey data of muskoxen (Ovibos moschatus) to quantify annual variation in the relative use of essential and non-essential elements in vegetated sites and their relationship to calf recruitment the following year. Results showed that the relative use of the geochemical landscape by muskoxen varied substantially between years and differed among elements. Selection for vegetated sites with higher levels of the essential elements N, Cu, Se, and Mo was positively linked to annual calf recruitment. In contrast, selection for vegetated sites with higher concentrations of the non-essential elements As and Pb was negatively correlated to annual calf recruitment. Based on the concentrations measured in our study, we found no apparent associations between annual calf recruitment and levels of C, Mn, Co, Zn, Cd, Ba, Hg, and C:N ratio in the vegetation. We conclude that the spatial distribution and access to essential and non-essential elements are important drivers of reproductive output in muskoxen, which may also apply to other wildlife populations. The value of geochemical landscapes to assess habitat-performance relationships is likely to increase under future environmental change.

Individual differences in habitat selection mediate landscape level predictions of a functional response

Predicting future space use by animals requires models that consider both habitat availability and individual differences in habitat selection. The functional response in habitat selection posits animals adjust their habitat selection to availability, but population-level responses to availability may differ from individual responses. Generalized functional response (GFR) models account for functional responses by including fixed effect interactions between habitat availability and selection. Population-level resource selection functions instead account for individual selection responses to availability with random effects. We compared predictive performance of both approaches using a functional response in elk (Cervus canadensis) selection for mixed forest in response to road proximity, and avoidance of roads in response to mixed forest availability. We also investigated how performance changed when individuals responded differently to availability from the rest of the population. Individual variation in road avoidance decreased performance of both models (random effects: β = 0.69, 95% CI 0.47, 0.91; GFR: β = 0.38, 95% CI 0.05, 0.71). Changes in individual road and forest availability affected performance of neither model, suggesting individual responses to availability different from the functional response mediated performance. We also found that overall, both models performed similarly for predicting mixed forest selection (F_{1, 58} = 0.14, p = 0.71) and road avoidance (F_{1, 58} = 0.28, p = 0.60). GFR estimates were slightly better, but its larger number of covariates produced greater variance than the random effects model. Given this bias-variance trade-off, we conclude that neither model performs better for future space use predictions.

Demystifying individual heterogeneity

Among-individual variation in vital rates, such as mortality and birth rates, exists in nearly all populations. Recent studies suggest that this individual heterogeneity produces substantial life-history and fitness differences among individuals, which in turn scale up to influence population dynamics. However, our ability to understand the consequences of individual heterogeneity is limited by inconsistencies across conceptual frameworks in the field. Studies of individual heterogeneity remain filled with contradicting and ambiguous terminology that introduces risks of misunderstandings, conflicting models and unreliable conclusions. Here, we synthesise the existing literature into a single and comparatively straightforward framework with explicit terminology and definitions. This work introduces a distinction between potential vital rates and realised vital rates to develop a coherent framework that maps directly onto mathematical models of individual heterogeneity. We suggest the terms "fixed condition" and "dynamic condition" be used to distinguish potential vital rates that are permanent from those that can change throughout an individual's life. To illustrate, we connect the framework to quantitative genetics models and to common classes of statistical models used to infer individual heterogeneity. We also develop a population projection matrix model that provides an example of how our definitions are translated into precise quantitative terms.