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The last few years have seen a surge of interest from field ecologists and evolutionary biologists to study neoplasia and cancer in wildlife. This contributes to the One Health Approach, which investigates health issues at the intersection of people, wild and domestic animals, together with their changing environments. Nonetheless, the emerging field of wildlife cancer is currently constrained by methodological limitations in detecting cancer using non-invasive sampling. In addition, the suspected differential susceptibility and resistance of species to cancer often make the choice of a unique model species difficult for field biologists. Here, we provide an overview of the importance of pursuing the study of cancer in non-model organisms and we review the currently available methods to detect, measure and quantify cancer in the wild, as well as the methodological limitations to be overcome to develop novel approaches inspired by diagnostic techniques used in human medicine. The methodology we propose here will help understand and hopefully fight this major disease by generating general knowledge about cancer, variation in its rates, tumour-suppressor mechanisms across species as well as its link to life history and physiological characters. Moreover, this is expected to provide key information about cancer in wildlife, which is a top priority due to the accelerated anthropogenic change in the past decades that might favour cancer progression in wild populations., (© 2024 The Author(s). Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

Theoretical and empirical research has shown that increased variability in demographic rates often results in a decline in the population growth rate. In order to reduce the adverse effects of increased variability, life-history theory predicts that demographic rates that contribute disproportionately to population growth should be buffered against environmental variation. To date, evidence of demographic buffering is still equivocal and limited to analyses on a reduced number of age classes (e.g. juveniles and adults), and on single sex models. Here we used Bayesian inference models for age-specific survival and fecundity on a long-term dataset of wild mountain gorillas. We used these estimates to parameterize two-sex, age-specific stochastic population projection models that accounted for the yearly covariation between demographic rates. We estimated the sensitivity of the long-run stochastic population growth rate to reductions in survival and fecundity on ages belonging to nine sex-age classes for survival and three age classes for female fecundity. We found a statistically significant negative linear relationship between the sensitivities and variances of demographic rates, with strong demographic buffering on young adult female survival and low buffering on older female and silverback survival and female fecundity. We found moderate buffering on all immature stages and on prime-age females. Previous research on long-lived slow species has found high buffering of prime-age female survival and low buffering on immature survival and fecundity. Our results suggest that the moderate buffering of the immature stages can be partially due to the mountain gorilla social system and the relative stability of their environment. Our results provide clear support for the demographic buffering hypothesis and its predicted effects on species at the slow end of the slow-fast life-history continuum, but with the surprising outcome of moderate social buffering on the survival of immature stages. We also demonstrate how increasing the number of sex-age classes can greatly improve the detection of demographic buffering in wild populations., (© 2021 British Ecological Society.)

Sex differences in mortality are pervasive in vertebrates, and usually result in shorter life spans in the larger sex, although the underlying mechanisms are still unclear. On the other hand, differences in frailty among individuals (i.e. individual heterogeneity), can play a major role in shaping demographic trajectories in wild populations. The link between these two processes has seldom been explored. We used Bayesian survival trajectory analysis to study age-specific mortality trajectories in the Eurasian sparrowhawk (Accipiter nisus), a monogamous raptor with reversed sexual size dimorphism. We tested the effect of individual heterogeneity on age-specific mortality, and the extent by which this heterogeneity was determined by average reproductive output and wing length as measures of an individual's frailty. We found that sex differences in age-specific mortality were primarily driven by the differences in individual heterogeneity between the two sexes. Females were more heterogeneous than males in their level of frailty. Thus, a larger number of females with low frailty are able to survive to older ages than males, with life expectancy for the least frail adult females reaching up to 4·23 years, while for the least frail adult males it was of 2·68 years. We found that 50% of this heterogeneity was determined by average reproductive output and wing length in both sexes. For both, individuals with high average reproductive output had also higher chances to survive. However, the effect of wing length was different between the two sexes. While larger females had higher survival, larger males had lower chances to survive. Our results contribute a novel perspective to the ongoing debate about the mechanisms that drive sex differences in vital rates in vertebrates. Although we found that variables that relate to the cost of reproduction and sexual dimorphism are at least partially involved in determining these sex differences, it is through their effect on the level of frailty that they affect age patterns of mortality. Therefore, our results raise the possibility that observed differences in age-specific demographic rates may in fact be driven by differences in individual heterogeneity., (© 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.)

Most animals need to actively search for food to meet energetic requirements and live in heterogeneous environments where food resources have complex spatio-temporal patterns of availability. Consequently, foraging animals need to find a balance between effort and resource allocation while accounting for intrinsic and extrinsic factors, which are often overlooked when modelling foraging behaviour. We identified the decision rules for foraging in black howler monkeys (Alouatta pigra), according to food preferences, locations of high-quality patches and previously eaten trees, phenology of food resources and hunger state. We depicted foraging in two stages: (i) the choice of the immediate next tree and (ii) the time spent on this tree. We used a recently developed model for inference of movement processes, incorporating resource selection functions into a Markov chain framework. We found that monkeys tend to move to preferred tree species at each step. However, we did not find conclusively that, at each step, monkeys direct their movements to reach high-quality patches. In fact, they were using these patches intensively, thus limiting the possibility to move towards other high-quality patches. Time spent on a tree was positively and strongly affected by the presence of preferred food items, but not by its species. We also showed that time spent on trees increased as a function of satiation state. We suggest that the strategy adopted by black howlers tends to be efficient because choosing preferred trees at each step and spending spend more time where preferred resources are available should favour energy intake and restrain movement costs. This study showcases a modelling framework that can be widely used in ecology to describe movements as a combination of multiple attraction and repulsion sources, such as mates and competitors., (© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.)

1. Traditional estimation of age-specific survival and mortality rates in vertebrates is limited to individuals with known age. Although this subject has been studied extensively using effective capture-recapture and capture-recovery models, inference remains challenging because of large numbers of incomplete records (i.e. unknown age of many individuals) and because of the inadequate duration of the studies. 2. Here, we present a hierarchical model for capture-recapture/recovery (CRR) data sets with large proportions of unknown times of birth and death. The model uses a Bayesian framework to draw inference on population-level age-specific demographic rates using parametric survival functions and applies this information to reconstruct times of birth and death for individuals with unknown age. 3. We simulated a set of CRR data sets with varying study span and proportions of individuals with known age, and varying recapture and recovery probabilities. We used these data sets to compare our method to a traditional CRR model, which requires knowledge of individual ages. Subsequently, we applied our method to a subset of a long-term CRR data set on Soay sheep. 4. Our results show that this method performs better than the common CRR model when sample sizes are low. Still, our model is sensitive to the choice of priors with low recapture probability and short studies. In such cases, priors that overestimate survival perform better than those that underestimate it. Also, the model was able to estimate accurately ages at death for Soay sheep, with an average error of 0.94 years and to identify differences in mortality rate between sexes. 5. Although many of the problems in the estimation of age-specific survival can be reduced through more efficient sampling schemes, most ecological data sets are still sparse and with a large proportion of missing records. Thus, improved sampling needs still to be combined with statistical models capable of overcoming the unavoidable limitations of any fieldwork. We show that our approach provides reliable estimates of parameters and unknown times of birth and death even with the most incomplete data sets while being flexible enough to accommodate multiple recapture probabilities and covariates., (© 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.)

1. Our understanding of the interplay between density dependence, climatic perturbations, and conservation practices on the dynamics of small populations is still limited. This can result in uninformed strategies that put endangered populations at risk. Moreover, the data available for a large number of populations in such circumstances are sparse and mined with missing data. Under the current climate change scenarios, it is essential to develop appropriate inferential methods that can make use of such data sets. 2. We studied a population of desert bighorn sheep introduced to Tiburon Island, Mexico in 1975 and subjected to irregular extractions for the last 10 years. The unique attributes of this population are absence of predation and disease, thereby permitting us to explore the combined effect of density dependence, environmental variability and extraction in a 'controlled setting.' Using a combination of nonlinear discrete models with long-term field data, we constructed three basic Bayesian state space models with increasing density dependence (DD), and the same three models with the addition of summer drought effects. 3. We subsequently used Monte Carlo simulations to evaluate the combined effect of drought, DD, and increasing extractions on the probability of population survival under two climate change scenarios (based on the Intergovernmental Panel on Climate Change predictions): (i) increase in drought variability; and (ii) increase in mean drought severity. 4. The population grew from 16 individuals introduced in 1975 to close to 700 by 1993. Our results show that the population's growth was dominated by DD, with drought having a secondary but still relevant effect on its dynamics. 5. Our predictions suggest that under climate change scenario (i), extraction dominates the fate of the population, while for scenario (ii), an increase in mean drought affects the population's probability of survival in an equivalent magnitude as extractions. Thus, for the long-term survival of the population, our results stress that a more variable environment is less threatening than one in which the mean conditions become harsher. Current climate change scenarios and their underlying uncertainty make studies such as this one crucial for understanding the dynamics of ungulate populations and their conservation.