Opposing effects of spatiotemporal variation in resources and temporal variation in climate on density dependent population growth in seabirds.

Understanding how ecological processes combine to shape population dynamics is crucial in a rapidly changing world. Evidence has been emerging for how fundamental drivers of density dependence in mobile species are related to two differing types of environmental variation-temporal variation in climate, and spatiotemporal variation in food resources. However, to date, tests of these hypotheses have been largely restricted to mid-trophic species in terrestrial environments and thus their general applicability remains unknown. We tested if these same processes can be identified in marine upper trophic level species. We assembled a multi-decadal data set on population abundance of 10 species of colonial seabirds comprising a large component of the UK breeding seabird biomass, and covering diverse phylogenies, life histories and foraging behaviours. We tested for evidence of density dependence in population growth rates using discrete time state-space population models fit to long time-series of observations of abundance at seabird breeding colonies. We then assessed if the strength of density dependence in population growth rates was exacerbated by temporal variation in climate (sea temperature and swell height), and attenuated by spatiotemporal variation in prey resources (productivity and tidal fronts). The majority of species showed patterns consistent with temporal variation in climate acting to strengthen density dependent feedbacks to population growth. However, fewer species showed evidence for a weakening of density dependence with increasing spatiotemporal variation in prey resources. Our findings extend this emerging theory for how different sources of environmental variation may shape the dynamics and regulation of animal populations, demonstrating its role in upper trophic marine species. We show that environmental variation leaves a signal in long-term population dynamics of seabirds with potentially important consequences for their demography and trophic interactions.

Integrating presence-only and occupancy data to model habitat use for the northernmost population of jaguars.

Species distribution models (SDMs) have become an essential tool for the management and conservation of imperiled species. However, many at-risk species are rare and characterized by limited data on their spatial distribution and habitat relationships. This has led to the development of SDMs that integrate multiple types and sources of data to leverage more information and provide improved predictions of habitat associations. We developed a novel integrated species distribution model to predict habitat suitability for jaguars (Panthera onca) in the border region between northern Mexico and the southwestern USA. Our model combined presence-only and occupancy data to identify key environmental correlates, and we used model results to develop a probability of use map. We adopted a logistic regression modeling framework, which we found to be more straightforward and less computationally intensive to fit than Poisson point process-based models. Model results suggested that high terrain ruggedness and the presence of riparian vegetation were most strongly related to habitat use by jaguars in our study region. Our best model, on average, predicted that there is currently 25,463 km2 of usable habitat in our study region. The United States portion of the study region, which makes up 38.6% of the total area, contained 40.6% of the total usable habitat. Even though there have been few detections of jaguars in the southwestern USA in recent decades, our results suggest that protection of currently suitable habitats, along with increased conservation efforts, could significantly contribute to the recovery of jaguars in the USA.

Harvest models of small populations of a large carnivore using Bayesian forecasting.

Harvesting large carnivores can be a management tool for meeting politically set goals for their desired abundance. However, harvesting carnivores creates its own set of conflicts in both society and among conservation professionals, where one consequence is a need to demonstrate that management is sustainable, evidence-based, and guided by science. Furthermore, because large carnivores often also have high degrees of legal protection, harvest quotas have to be carefully justified and constantly adjusted to avoid damaging their conservation status. We developed a Bayesian state-space model to support adaptive management of Eurasian lynx harvesting in Scandinavia. The model uses data from the annual monitoring of lynx abundance and results from long-term field research on lynx biology, which has provided detailed estimates of key demographic parameters. We used the model to predict the probability that the forecasted population size will be below or above the management objectives when subjected to different harvest quotas. The model presented here informs decision makers about the policy risks of alternative harvest levels. Earlier versions of the model have been available for wildlife managers in both Sweden and Norway to guide lynx harvest quotas and the model predictions showed good agreement with observations. We combined monitoring data with data on vital rates and were able to estimate unobserved additional mortality rates, which are most probably due to poaching. In both countries, the past quota setting strategy suggests that there has been a de facto threshold strategy with increasing proportion, which means that there is no harvest below a certain population size, but above this threshold there is an increasing proportion of the population harvested as the population size increases. The annual assessment of the monitoring results, the use of forecasting models, and a threshold harvest approach to quota setting will all reduce the risk of lynx population sizes moving outside the desired goals. The approach we illustrate could be adapted to other populations of mammals worldwide.

A hierarchical Bayesian approach for handling missing classification data.

Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these "partial" observations must be modified to include the missing data mechanism to avoid spurious inference.We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in categories. In one model, we use an empirical Bayes approach, where a subset of data from one year serves as a prior for the missing data the next. In the other approach, we use a small random sample of data within a year to inform the distribution of the missing data.We performed a simulation to show the bias that occurs when partial observations were ignored and demonstrated the altered inference for the estimation of demographic ratios. We applied our models to demographic classifications of elk (Cervus elaphus nelsoni) to demonstrate improved inference for the proportions of sex and stage classes.We developed multiple modeling approaches using a generalizable nested multinomial structure to account for partially observed data that were missing not at random for classification counts. Accounting for classification uncertainty is important to accurately understand the composition of populations and communities in ecological studies.

Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements.

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground-based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark-recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.

Forecasting the Effects of Fertility Control on Overabundant Ungulates: White-Tailed Deer in the National Capital Region.

Overabundant populations of ungulates have caused environmental degradation and loss of biological diversity in ecosystems throughout the world. Culling or regulated harvest is often used to control overabundant species. These methods are difficult to implement in national parks, other types of conservation reserves, or in residential areas where public hunting may be forbidden by policy. As a result, fertility control has been recommended as a non-lethal alternative for regulating ungulate populations. We evaluate this alternative using white-tailed deer in national parks in the vicinity of Washington, D.C., USA as a model system. Managers seek to reduce densities of white-tailed deer from the current average (50 deer per km2) to decrease harm to native plant communities caused by deer. We present a Bayesian hierarchical model using 13 years of population estimates from 8 national parks in the National Capital Region Network. We offer a novel way to evaluate management actions relative to goals using short term forecasts. Our approach confirms past analyses that fertility control is incapable of rapidly reducing deer abundance. Fertility control can be combined with culling to maintain a population below carrying capacity with a high probability of success. This gives managers confronted with problematic overabundance a framework for implementing management actions with a realistic assessment of uncertainty.

Bayesian Modeling of Prion Disease Dynamics in Mule Deer Using Population Monitoring and Capture-Recapture Data.

Epidemics of chronic wasting disease (CWD) of North American Cervidae have potential to harm ecosystems and economies. We studied a migratory population of mule deer (Odocoileus hemionus) affected by CWD for at least three decades using a Bayesian framework to integrate matrix population and disease models with long-term monitoring data and detailed process-level studies. We hypothesized CWD prevalence would be stable or increase between two observation periods during the late 1990s and after 2010, with higher CWD prevalence making deer population decline more likely. The weight of evidence suggested a reduction in the CWD outbreak over time, perhaps in response to intervening harvest-mediated population reductions. Disease effects on deer population growth under current conditions were subtle with a 72% chance that CWD depressed population growth. With CWD, we forecasted a growth rate near one and largely stable deer population. Disease effects appear to be moderated by timing of infection, prolonged disease course, and locally variable infection. Long-term outcomes will depend heavily on whether current conditions hold and high prevalence remains a localized phenomenon.

AGE AND REPEATED BIOPSY INFLUENCE ANTEMORTEM PRP(CWD) TESTING IN MULE DEER (ODOCOILEUS HEMIONUS) IN COLORADO, USA.

Biopsy of rectal mucosa-associated lymphoid tissue provides a useful, but imperfect, live-animal test for chronic wasting disease (CWD) in mule deer (Odocoileus hemionus). It is difficult and expensive to complete these tests on free-ranging animals, and wildlife health managers will benefit from methods that can accommodate test results of varying quality. To this end, we developed a hierarchical Bayesian model to estimate the probability that an individual is infected based on test results. Our model was estimated with the use of data on 210 adult female mule deer repeatedly tested during 2010-14. The ability to identify infected individuals correctly declined with age and may have been influenced by repeated biopsy. Fewer isolated lymphoid follicles (where PrP(CWD) accumulates) were obtained in biopsies of older deer and the proportion of follicles showing PrP(CWD) was reduced. A deer's genotype in the prion gene (PRNP) also influenced detection. At least five follicles were needed in a biopsy to assure a 95% accurate test in PRNP genotype 225SS deer.

Efficacy of antemortem rectal biopsies to diagnose and estimate prevalence of chronic wasting disease in free-ranging cow elk (Cervus elaphus nelsoni).

A reliable antemortem test is needed to understand the ecology of chronic wasting disease (CWD) in elk (Cervus elaphus nelsoni). We measured the ability of antemortem biopsy samples from the rectal mucosa to detect the abnormal prion protein associated with CWD (PrP(CWD)), the relationship between test results from the obex and rectal biopsies at varying stages of CWD progression, and the prevalence of CWD in free-ranging elk from Rocky Mountain National Park, Colorado, USA. We sampled and placed radio collars on 136 adult female elk in the winter of 2007-08. Elk with biopsy samples found positive for PrP(CWD) by immunohistochemistry (IHC) were euthanized and the obex and retropharyngeal lymph nodes were examined with IHC. We resampled, euthanized, and necropsied 20, 25, and 34 of the remaining study elk in each of the three following winters, respectively. Sensitivity of rectal biopsy samples increased in an asymptotic fashion with follicle count and was maximized at 85% (95% credible limits [CL]=60, 98) in the beginning of the study, when a greater proportion of elk were in a detectable stage of prion infection. However, maximum sensitivity was reduced to 72% (CL=46, 94) when we included resampled elk, which included recently infected elk that were initially negative using rectal biopsies and IHC. Test results were similar between rectal biopsies and the obex, but the earliest stages of prion infection were only detected by using retropharyngeal lymph nodes. Minimum CWD prevalence was estimated to be 9.9% (CL=5.7, 15.7) using rectal biopsies, but this rose to 12.9% (CL=8.0, 19.1) when we included four elk that were likely misdiagnosed at initial capture. Our results indicate rectal biopsies can provide a useful research tool for CWD in elk populations, but should be used with caution because they can miss individuals in early stages of infection and underestimate prevalence. Prevalence estimates from this population are the highest reported to date in elk and indicate that under appropriate conditions, CWD may be able to affect the dynamics of high-density elk populations.

Stream hydrology limits recovery of riparian ecosystems after wolf reintroduction.

Efforts to restore ecosystems often focus on reintroducing apex predators to re-establish coevolved relationships among predators, herbivores and plants. The preponderance of evidence for indirect effects of predators on terrestrial plant communities comes from ecosystems where predators have been removed. Far less is known about the consequences of their restoration. The effects of removal and restoration are unlikely to be symmetrical because removing predators can create feedbacks that reinforce the effects of predator loss. Observational studies have suggested that the reintroduction of wolves to Yellowstone National Park initiated dramatic restoration of riparian ecosystems by releasing willows from excessive browsing by elk. Here, we present results from a decade-long experiment in Yellowstone showing that moderating browsing alone was not sufficient to restore riparian zones along small streams. Instead, restoration of willow communities depended on removing browsing and restoring hydrological conditions that prevailed before the removal of wolves. The 70-year absence of predators from the ecosystem changed the disturbance regime in a way that was not reversed by predator reintroduction. We conclude that predator restoration may not quickly repair effects of predator removal in ecosystems.

Contraception can lead to trophic asynchrony between birth pulse and resources.

Abiotic inputs such as photoperiod and temperature can regulate reproductive cyclicity in many species. When humans perturb this process by intervening in reproductive cycles, the ecological consequences may be profound. Trophic mismatches between birth pulse and resources in wildlife species may cascade toward decreased survival and threaten the viability of small populations. We followed feral horses (Equus caballus) in three populations for a longitudinal study of the transient immunocontraceptive porcine zona pellucida (PZP), and found that repeated vaccinations extended the duration of infertility far beyond the targeted period. After the targeted years of infertility, the probability of parturition from post-treated females was 25.6% compared to 64.1% for untreated females, when the data were constrained only to females that had demonstrated fertility prior to the study. Estimated time to parturition increased 411.3 days per year of consecutive historical treatment. Births from untreated females in these temperate latitude populations were observed to peak in the middle of May, indicating peak conception occurred around the previous summer solstice. When the post-treated females did conceive and give birth, parturition was an estimated 31.5 days later than births from untreated females, resulting in asynchrony with peak forage availability. The latest neonate born to a post-treated female arrived 7.5 months after the peak in births from untreated females, indicating conception occurred within 24-31 days of the winter solstice. These results demonstrate surprising physiological plasticity for temperate latitude horses, and indicate that while photoperiod and temperature are powerful inputs driving the biological rhythms of conception and birth in horses, these inputs may not limit their ability to conceive under perturbed conditions. The protracted infertility observed in PZP-treated horses may be of benefit for managing overabundant wildlife, but also suggests caution for use in small refugia or rare species.

Native predators reduce harvest of reindeer by Sámi pastoralists.

Contemporary efforts to protect biological diversity recognize the importance of sustaining traditional human livelihoods, particularly uses of the land that are compatible with intact landscapes and ecologically complete food webs. However, these efforts often confront conflicting goals. For example, conserving native predators may harm pastoralist economies because predators consume domestic livestock that sustain people. This potential conflict must be reconciled by policy, but such reconciliation requires a firm understanding of the effects of predators on the prey used by people. We used a long-term, large-scale database and Bayesian models to estimate the impacts of lynx (Lynx lynx), wolverine (Gulo gulo), and brown bear (Ursus arctos) on harvest of semi-domesticated reindeer (Rangifer tarandus) by Sami pastoralists in Sweden. The average annual harvest of reindeer averaged 25% of the population (95% credible interval = 19, 31). Annual harvest declined by 96.6 (31, 155) reindeer for each lynx family group (the surveyed segment of the lynx population) in a management unit and by 94.3 (20, 160) for each wolverine reproduction (the surveyed segment of the wolverine population). We failed to detect effects of predation by brown bear. The mechanism for effects of predation on harvest was reduced population growth rate. The rate of increase of reindeer populations declined with increasing abundance of lynx and wolverine. The density of reindeer, latitude, and weather indexed by the North Atlantic Oscillation also influenced reindeer population growth rate. We conclude that there is a biological basis for compensating the Sámi reindeer herders for predation on reindeer.

Shoot, shovel and shut up: cryptic poaching slows restoration of a large carnivore in Europe.

Poaching is a widespread and well-appreciated problem for the conservation of many threatened species. Because poaching is illegal, there is strong incentive for poachers to conceal their activities, and consequently, little data on the effects of poaching on population dynamics are available. Quantifying poaching mortality should be a required knowledge when developing conservation plans for endangered species but is hampered by methodological challenges. We show that rigorous estimates of the effects of poaching relative to other sources of mortality can be obtained with a hierarchical state-space model combined with multiple sources of data. Using the Scandinavian wolf (Canis lupus) population as an illustrative example, we show that poaching accounted for approximately half of total mortality and more than two-thirds of total poaching remained undetected by conventional methods, a source of mortality we term as 'cryptic poaching'. Our simulations suggest that without poaching during the past decade, the population would have been almost four times as large in 2009. Such a severe impact of poaching on population recovery may be widespread among large carnivores. We believe that conservation strategies for large carnivores considering only observed data may not be adequate and should be revised by including and quantifying cryptic poaching.

Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting.

Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector-borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data-model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state-space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.

Introducing data-model assimilation to students of ecology.

Quantitative training for students of ecology has traditionally emphasized two sets of topics: mathematical modeling and statistical analysis. Until recently, these topics were taught separately, modeling courses emphasizing mathematical techniques for symbolic analysis and statistics courses emphasizing procedures for analyzing data. We advocate the merger of these traditions in ecological education by outlining a curriculum for an introductory course in data-model assimilation. This course replaces the procedural emphasis of traditional introductory material in statistics with an emphasis on principles needed to develop hierarchical models of ecological systems, fusing models of data with models of ecological processes. We sketch nine elements of such a course: (1) models as routes to insight, (2) uncertainty, (3) basic probability theory, (4) hierarchical models, (5) data simulation, (6) likelihood and Bayes, (7) computational methods, (8) research design, and (9) problem solving. The outcome of teaching these combined elements can be the fundamental understanding and quantitative confidence needed by students to create revealing analyses for a broad array of research problems.

Nonlinear responses to food availability shape effects of habitat fragmentation on consumers.

Fragmentation of landscapes is a pervasive source of environmental change. Although understanding the effects of fragmentation has occupied ecologists for decades, there remain important gaps in our understanding of the way that fragmentation influences mobile organisms. In particular, there is little tested theory explaining the way that fragmentation shapes interactions between consumers and resources. We propose a simple model that explains why fragmentation may harm consumers even when the total amount of resources on the landscape they use remains unchanged. In particular, we show that nonlinearity in the relationship between resource availability and benefits acquired by consumers from resources can cause a decrease in benefits to consumers when landscapes are subdivided into isolated parts and when the distribution of consumers in fragments is not matched to the distribution of resources. We tested predictions of the model using a laboratory system of cabbage looper (Trichoplusia ni) larvae on artificial landscapes. Consistent with the model's predictions, survivorship of larvae decreased when landscapes with heterogeneous resources were fragmented into isolated parts. However, average mass of surviving larvae did not change in response to fragmentation. With basic knowledge of consumer resource use patterns and landscape structure, our model, supported by our experiment, contributes new understanding of the resource-mediated effects of fragmentation on consumers.

The role of predation in disease control: a comparison of selective and nonselective removal on prion disease dynamics in deer.

Effective measures for controlling chronic wasting disease (CWD), a contagious prion disease of cervids, remain elusive. We review theoretic relationships between predation and host-parasite dynamics and describe a mathematical model to evaluate the potential influence of random removal through harvest or culling and selective predation by wolves (Canis lupus) upon CWD dynamics in deer (Odocoileus spp.) populations. Imposing nonselective mortality representing a 15% annual harvest or cull 51 yr after CWD introduction lowered both deer population size and steady state CWD. Selective (4×) mortality at the same 15% predation rate caused a more modest reduction in deer population size accompanied by a relatively rapid decline in CWD prevalence and elimination of the disease from a closed population. The impacts of selective predation on epidemic dynamics were sensitive to assumptions on parameter estimates; however, within expected ranges, the results of selective predation were consistent and robust. We suggest that as CWD distribution and wolf range overlap in the future, wolf predation may suppress disease emergence or limit prevalence.

Estimating chronic wasting disease effects on mule deer recruitment and population growth.

Chronic wasting disease (CWD), a prion disease of mule deer (Odocoileus hemionus), accelerates mortality and in so doing has the potential to influence population dynamics. Although effects on mule deer survival are clear, how CWD affects recruitment is less certain. We studied how prion infection influenced the number of offspring raised to weaning per adult (≥2 yr old) female mule deer and subsequently the estimated growth rate (λ) of an infected deer herd. Infected and presumably uninfected radio-collared female deer were observed with their fawns in late summer (August-September) during three consecutive years (2006-2008) in the Table Mesa area of Boulder, Colorado, USA. We counted the number of fawns accompanying each female, then used a fully Bayesian model to estimate recruitment by infected and uninfected females and the effect of the disease on λ. On average, infected females weaned 0.95 fawns (95% credible interval=0.56-1.43) whereas uninfected females weaned 1.34 fawns (95% credible interval=1.09-1.61); the probability that uninfected females weaned more fawns than infected females was 0.93). We used estimates of prevalence to weight recruitment and survival parameters in the transition matrix of a three-age, single-sex matrix model and then used the matrix to calculate effects of CWD on λ. When effects of CWD on both survival and recruitment were included, the modeled λ was 0.97 (95% credible interval = 0.82-1.09). Effects of disease on λ were mediated almost entirely by elevated mortality of infected animals. We conclude that although CWD may affect mule deer recruitment, these effects seem to be sufficiently small that they can be omitted in estimating the influences of CWD on population growth rate.

Mountain lions prey selectively on prion-infected mule deer.

The possibility that predators choose prey selectively based on age or condition has been suggested but rarely tested. We examined whether mountain lions (Puma concolor) selectively prey upon mule deer (Odocoileus hemionus) infected with chronic wasting disease, a prion disease. We located kill sites of mountain lions in the northern Front Range of Colorado, USA, and compared disease prevalence among lion-killed adult (> or =2 years old) deer with prevalence among sympatric deer taken by hunters in the vicinity of kill sites. Hunter-killed female deer were less likely to be infected than males (odds ratios (OR) = 0.2, 95% confidence intervals (CI) = 0.1-0.6; p = 0.015). However, both female (OR = 8.5, 95% CI = 2.3-30.9) and male deer (OR = 3.2, 95% CI = 1-10) killed by a mountain lion were more likely to be infected than same-sex deer killed in the vicinity by a hunter (p < 0.001), suggesting that mountain lions in this area actively selected prion-infected individuals when targeting adult mule deer as prey items.