Do Mesocarnivores Respond to the Seasonality in Management Practices in an Agroforestry Landscape?

In the Mediterranean, we find a mosaic of natural and cultural landscapes, where a variety of forest management practices created intermediate disturbance regimes that potentially increased biodiversity values. Nonetheless, it is essential to understand the species' long-term response to the dynamic management in agroecosystems, since the species tolerance to disturbance can change throughout the life cycle. Mammalian carnivores can be sensitive to human disturbance and are an essential part of ecosystems due to their regulatory and community structuring effects. We investigated the spatial response of five mesocarnivores species to spatially- and temporally- varying management practices in an agroforestry landscape. More specifically, we assessed the mesocarnivores' temporal changes in space use by implementing multi-season occupancy models in a Bayesian framework, using seasonal camera-trapping surveys for a 2-year period. All species had a weak response of local extinction to forestry management and livestock grazing pressure. For forest-dwelling species, occupancy was higher where productivity of perennial vegetation was high, while colonization between seasons was positively associated with vegetation cover. For habitat generalist species, we found that occupancy in the wet season increased with the distance to cattle exclusion plots. Most of these plots are pine stands which are subject to forestry interventions during winter. During the 2-year period we found seasonal fluctuations in occupancy for all species, with an overall slight decrease for three mesocarnivore species, while for the two forest-dwelling species there was an increase in occupancy between years. The weak species response to management practices supports the importance of traditional management for upholding a diverse mesocarnivore community in agroforestry systems but could also reflect these species' ecological plasticity and resilience to disturbance.

Spatial patterns of breeding success of grizzly bears derived from hierarchical multistate models.

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low-elevation wetlands or mid-elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy-herbaceous alpine ecotones-were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection.

A note on investigating co-occurrence patterns and dynamics for many species, with imperfect detection and a log-linear modeling parameterization.

Patterns in, and the underlying dynamics of, species co-occurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterizations have been published that could be used with the same fundamental modeling framework that accounts for imperfect detection, although each parameterization has different advantages and disadvantages.We propose a parameterization based on log-linear modeling that does not require a species hierarchy to be defined (in terms of dominance) and enables a numerically robust approach for estimating covariate effects.Conceptually, the parameterization is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, "symmetric," interpretations of parameter estimates.The parameterization can be applied to many species, in either a maximum likelihood or Bayesian estimation framework. We illustrate the method using camera-trapping data collected on three mesocarnivore species in South Texas.

Seeking a second opinion: uncertainty in disease ecology.

Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy.

Simultaneous modeling of habitat suitability, occupancy, and relative abundance: African elephants in Zimbabwe.

The recent development of statistical models such as dynamic site occupancy models provides the opportunity to address fairly complex management and conservation problems with relatively simple models. However, surprisingly few empirical studies have simultaneously modeled habitat suitability and occupancy status of organisms over large landscapes for management purposes. Joint modeling of these components is particularly important in the context of management of wild populations, as it provides a more coherent framework to investigate the population dynamics of organisms in space and time for the application of management decision tools. We applied such an approach to the study of water hole use by African elephants in Hwange National Park, Zimbabwe. Here we show how such methodology may be implemented and derive estimates of annual transition probabilities among three dry-season states for water holes: (1) unsuitable state (dry water holes with no elephants); (2) suitable state (water hole with water) with low abundance of elephants; and (3) suitable state with high abundance of elephants. We found that annual rainfall and the number of neighboring water holes influenced the transition probabilities among these three states. Because of an increase in elephant densities in the park during the study period, we also found that transition probabilities from low abundance to high abundance states increased over time. The application of the joint habitat-occupancy models provides a coherent framework to examine how habitat suitability and factors that affect habitat suitability influence the distribution and abundance of organisms. We discuss how these simple models can further be used to apply structured decision-making tools in order to derive decisions that are optimal relative to specified management objectives. The modeling framework presented in this paper should be applicable to a wide range of existing data sets and should help to address important ecological, conservation, and management problems that deal with occupancy, relative abundance, and habitat suitability.

Co-occurrence of bobcats, coyotes, and ocelots in Texas.

Interspecific competition among carnivores has been linked to differences in behavior, morphology, and resource use. Insights into these interactions can enhance understanding of local ecological processes that can have impacts on the recovery of endangered species, such as the ocelot (Leopardus pardalis). Ocelots, bobcats (Lynx rufus), and coyotes (Canis latrans) share a small geographic range overlap from South Texas to south-central Mexico but relationships among the three are poorly understood. From May 2011 to March 2018, we conducted a camera trap study to examine co-occurrence patterns among ocelots, bobcats, and coyotes on the East Foundation's El Sauz Ranch in South Texas. We used a novel multiseason extension to multispecies occupancy models with ≥2 interacting species to conduct an exploratory analysis to examine interspecific interactions and examine the potential effects of patch-level and landscape-level metrics relative to the occurrence of these carnivores. We found strong evidence of seasonal mutual coexistence among all three species and observed a species-specific seasonal trend in detection. Seasonal coexistence patterns were also explained by increasing distance from a high-speed roadway. However, these results have important ecological implications for planning ocelot recovery in the rangelands of South Texas. This study suggests a coexistence among ocelots, bobcats, and coyotes under the environmental conditions on the El Sauz Ranch. Further research would provide a better understanding of the ecological mechanisms that facilitate coexistence within this community. As road networks in the region expand over the next few decades, large private working ranches will be needed to provide important habitat for ocelots and other carnivore species.

Modeling species occurrence dynamics with multiple states and imperfect detection.

Recent extensions of occupancy modeling have focused not only on the distribution of species over space, but also on additional state variables (e.g., reproducing or not, with or without disease organisms, relative abundance categories) that provide extra information about occupied sites. These biologist-driven extensions are characterized by ambiguity in both species presence and correct state classification, caused by imperfect detection. We first show the relationships between independently published approaches to the modeling of multistate occupancy. We then extend the pattern-based modeling to the case of sampling over multiple seasons or years in order to estimate state transition probabilities associated with system dynamics. The methodology and its potential for addressing relevant ecological questions are demonstrated using both maximum likelihood (occupancy and successful reproduction dynamics of California Spotted Owl) and Markov chain Monte Carlo estimation approaches (changes in relative abundance of green frogs in Maryland). Just as multistate capture-recapture modeling has revolutionized the study of individual marked animals, we believe that multistate occupancy modeling will dramatically increase our ability to address interesting questions about ecological processes underlying population-level dynamics.

How many to sample? Statistical guidelines for monitoring animal welfare outcomes.

There is increasing scrutiny of the animal welfare impacts of all animal use activities, including agriculture, the keeping of companion animals, racing and entertainment, research and laboratory use, and wildlife management programs. A common objective of animal welfare monitoring is to quantify the frequency of adverse animal events (e.g., injuries or mortalities). The frequency of such events can be used to provide pass/fail grades for animal use activities relative to a defined threshold and to identify areas for improvement through research. A critical question in these situations is how many animals should be sampled? There are, however, few guidelines available for data collection or analysis, and consequently sample sizes can be highly variable. To address this question, we first evaluated the effect of sample size on precision and statistical power in reporting the frequency of adverse animal welfare outcomes. We next used these findings to assess the precision of published animal welfare investigations for a range of contentious animal use activities, including livestock transport, horse racing, and wildlife harvesting and capture. Finally, we evaluated the sample sizes required for comparing observed outcomes with specified standards through hypothesis testing. Our simulations revealed that the sample sizes required for reasonable levels of precision (i.e., proportional distance to the upper confidence interval limit (δ) of ≤ 0.50) are greater than those that have been commonly used for animal welfare assessments (i.e., >300). Larger sample sizes are required for adverse events with low frequency (i.e., <5%). For comparison with a required threshold standard, even larger samples sizes are required. We present guidelines, and an online calculator, for minimum sample sizes for use in future animal welfare assessments of animal management and research programs.

Occupancy estimation and modeling with multiple states and state uncertainty.

The distribution of a species over space is of central interest in ecology, but species occurrence does not provide all of the information needed to characterize either the well-being of a population or the suitability of occupied habitat. Recent methodological development has focused on drawing inferences about species occurrence in the face of imperfect detection. Here we extend those methods by characterizing occupied locations by some additional state variable (e.g., as producing young or not). Our modeling approach deals with both detection probabilities <1 and uncertainty in state classification. We then use the approach with occupancy and reproductive rate data from California Spotted Owls (Strix occidentalis occidentalis) collected in the central Sierra Nevada during the breeding season of 2004 to illustrate the utility of the modeling approach. Estimates of owl reproductive rate were larger than naïve estimates, indicating the importance of appropriately accounting for uncertainty in detection and state classification.

Sampling design trade-offs in occupancy studies with imperfect detection: examples and software.

Researchers have used occupancy, or probability of occupancy, as a response or state variable in a variety of studies (e.g., habitat modeling), and occupancy is increasingly favored by numerous state, federal, and international agencies engaged in monitoring programs. Recent advances in estimation methods have emphasized that reliable inferences can be made from these types of studies if detection and occupancy probabilities are simultaneously estimated. The need for temporal replication at sampled sites to estimate detection probability creates a trade-off between spatial replication (number of sample sites distributed within the area of interest/inference) and temporal replication (number of repeated surveys at each site). Here, we discuss a suite of questions commonly encountered during the design phase of occupancy studies, and we describe software (program GENPRES) developed to allow investigators to easily explore design trade-offs focused on particularities of their study system and sampling limitations. We illustrate the utility of program GENPRES using an amphibian example from Greater Yellowstone National Park, U.S.A.

Solar Radiation Determines Site Occupancy of Coexisting Tropical and Temperate Deer Species Introduced to New Zealand Forests.

Assemblages of introduced taxa provide an opportunity to understand how abiotic and biotic factors shape habitat use by coexisting species. We tested hypotheses about habitat selection by two deer species recently introduced to New Zealand's temperate rainforests. We hypothesised that, due to different thermoregulatory abilities, rusa deer (Cervus timorensis; a tropical species) would prefer warmer locations in winter than red deer (Cervus elaphus scoticus; a temperate species). Since adult male rusa deer are aggressive in winter (the rut), we also hypothesised that rusa deer and red deer would not use the same winter locations. Finally, we hypothesised that in summer both species would prefer locations with fertile soils that supported more plant species preferred as food. We used a 250 × 250 m grid of 25 remote cameras to collect images in a 100-ha montane study area over two winters and summers. Plant composition, solar radiation, and soil fertility were also determined for each camera location. Multiseason occupancy models revealed that direct solar radiation was the best predictor of occupancy and detection probabilities for rusa deer in winter. Multistate, multiseason occupancy models provided strong evidence that the detection probability of adult male rusa deer was greater in winter and when other rusa deer were present at a location. Red deer mostly vacated the study area in winter. For the one season that had sufficient camera images of both species (summer 2011) to allow two-species occupancy models to be fitted, the detection probability of rusa deer also increased with solar radiation. Detection probability also varied with plant composition for both deer species. We conclude that habitat use by coexisting tropical and temperate deer species in New Zealand likely depends on the interplay between the thermoregulatory and behavioural traits of the deer and the abiotic and biotic features of the habitat.

Ignoring imperfect detection in biological surveys is dangerous: a response to 'fitting and interpreting occupancy models'.

In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD's claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.

Getting the biggest bang for our conservation buck.

Most conservation programs operate with limited resources and budgets. Optimal decision-making tools can be used to indicate the best allocation of such resources to achieve conservation goals. Recently, Chades et al. suggested an approach for doing so, with one of the management decisions being to halt conservation efforts. Here I discuss the results of Chades et al. and suggest formal adaptive resource management as an alternative tool when there is uncertainty in how populations respond to management actions.