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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Water tables constrain height recovery of willow on Yellowstone's northern range.

Excessive levels of herbivory may disturb ecosystems in ways that persist even when herbivory is moderated. These persistent changes may complicate efforts to restore ecosystems affected by herbivores. Willow (Salix spp.) communities within the northern range in Yellowstone National Park have been eliminated or degraded in many riparian areas by excessive elk (Cervus elaphus L.) browsing. Elk browsing of riparian willows appears to have diminished following the reintroduction of wolves (Canis lupis L.), but it remains uncertain whether reduced herbivory will restore willow communities. The direct effects of elk browsing on willows have been accompanied by indirect effects from the loss of beaver (Castor canadensis Kuhl) activity, including incision of stream channels, erosion of fine sediments, and lower water tables near streams historically dammed by beaver. In areas where these changes have occurred, lowered water tables may suppress willow height even in the absence of elk browsing. We conducted a factorial field experiment to understand willow responses to browsing and to height of water tables. After four years of protection from elk browsing, willows with ambient water tables averaged only 106 cm in height, with negligible height gain in two of three study species during the last year of the experiment. Willows that were protected from browsing and had artificially elevated water tables averaged 147 cm in height and gained 19 cm in the last year of the experiment. In browsed plots, elevated water tables doubled height gain during a period of slightly reduced browsing pressure. We conclude that water availability mediates the rate of willow height gain and may determine whether willows grow tall enough to escape the browse zone of elk and gain resistance to future elk browsing. Consequently, in areas where long-term beaver absence has resulted in incised stream channels and low water tables, a reduction in elk browsing alone may not be sufficient for recovery of tall willow stands. Because tall willow stems are important elements of habitat for beaver, mitigating water table decline may be necessary in these areas to promote recovery of historical willow-beaver mutualisms.

Composite forces shape population dynamics of copepod crustaceans.

Understanding the processes that control species abundance and distribution is a major challenge in ecology, yet for a large number of potentially important organisms, we know little about the biotic and abiotic factors that influence population size. One group of aquatic organisms that defies traditional demographic analyses is the Crustacea, particularly those with complex life cycles. We used likelihood techniques and information theoretics to evaluate a suite of models representing alternative hypotheses on factors controlling the abundance of two copepod crustaceans in a small, tropical floodplain lake. Quantitative zooplankton samples were collected at three stations in a Venezuelan floodplain lake from June through December 1984; the average sampling interval was two days. We constructed a series of models with stage structure that incorporated six biotic and abiotic covariates in various combinations to account for temporal changes in abundance of these target species and in their population growth rates. Our analysis produced several novel insights into copepod population dynamics. We found that multiple forces affected the abundance of particular stages, that these factors differed between species as well as among stages within each species, and that biotic processes had the largest effects on copepod population dynamics. Density dependence had a large effect on the survival of Oithona amazonica copepodites and on population growth rate of Diaptomus negrensis.

Hydrologic regime and herbivory stabilize an alternative state in Yellowstone National Park.

A decline in the stature and abundance of willows during the 20th century occurred throughout the northern range of Yellowstone National Park, where riparian woody-plant communities are key components in multiple-trophic-level interactions. The potential causes of willow decline include climate change, increased elk browsing coincident with the loss of an apex predator, the gray wolf, and an absence of habitat engineering by beavers. The goal of this study was to determine the spatial and temporal patterns of willow establishment through the 20th century and to identify causal processes. Sampled willows established from 1917 to 1999 and contained far fewer young individuals than was predicted from a modeled stable willow population, indicating reduced establishment during recent decades. Two hydrologically distinct willow establishment environments were identified: fine-grained beaver pond sediments and coarse-grained alluvium. Willows established on beaver pond sediment earlier in time, higher on floodplain surfaces, and farther from the current stream channel than did willows on alluvial sediment. Significant linear declines from the 1940s to the 1990s in alluvial willow establishment elevation and lateral distance from the stream channel resulted in a much reduced area of alluvial willow establishment. Willow establishment was not well correlated with climate-driven hydrologic variables, but the trends were consistent with the effects of stream channel incision initiated in ca. 1950, 20-30 years after beaver dam abandonment. Radiocarbon dates and floodplain stratigraphy indicate that stream incision of the present magnitude may be unprecedented in the past two millennia. We propose that hydrologic changes, stemming from competitive exclusion of beaver by elk overbrowsing, caused the landscape to transition from a historical beaver-pond and willow-mosaic state to its current alternative stable state where active beaver dams and many willow stands are absent. Because of hydrologic changes in streams, a rapid return to the historical state may not occur by reduction of elk browsing alone. Management intervention to restore the historical hydrologic regime may be necessary to recover willows and beavers across the landscape.

Spatial and temporal variability modify density dependence in populations of large herbivores.

A central challenge in ecology is to understand the interplay of internal and external controls on the growth of populations. We examined the effects of temporal variation in weather and spatial variation in vegetation on the strength of density dependence in populations of large herbivores. We fit three subsets of the model ln(Nt) = a + (1 + b) x ln(N(t-1)) + c x ln(N(t-2)) to five time series of estimates (Nt) of abundance of ungulates in the Rocky Mountains, USA. The strength of density dependence was estimated by the magnitude of the coefficient b. We regressed the estimates of b on indices of temporal heterogeneity in weather and spatial heterogeneity in resources. The 95% posterior intervals of the slopes of these regressions showed that temporal heterogeneity strengthened density-dependent feedbacks to population growth, whereas spatial heterogeneity weakened them. This finding offers the first empirical evidence that density dependence responds in different ways to spatial heterogeneity and temporal heterogeneity.

Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.

Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.

Dynamics of prion disease transmission in mule deer.

Chronic wasting disease (CWD), a contagious prion disease of the deer family, has the potential to severely harm deer populations and disrupt ecosystems where deer occur in abundance. Consequently, understanding the dynamics of this emerging infectious disease, and particularly the dynamics of its transmission, has emerged as an important challenge for contemporary ecologists and wildlife managers. Although CWD is contagious among deer, the relative importance of pathways for its transmission remains unclear. We developed seven competing models, and then used data from two CWD outbreaks in captive mule deer and model selection to compare them. We found that models portraying indirect transmission through the environment had 3.8 times more support in the data than models representing transmission by direct contact between infected and susceptible deer. Model-averaged estimates of the basic reproductive number (R0) were 1.3 or greater, indicating likely local persistence of CWD in natural populations under conditions resembling those we studied. Our findings demonstrate the apparent importance of indirect, environmental transmission in CWD and the challenges this presents for controlling the disease.

Linking chronic wasting disease to mule deer movement scales: a hierarchical Bayesian approach.

Observed spatial patterns in natural systems may result from processes acting across multiple spatial and temporal scales. Although spatially explicit data on processes that generate ecological patterns, such as the distribution of disease over a landscape, are frequently unavailable, information about the scales over which processes operate can be used to understand the link between pattern and process. Our goal was to identify scales of mule deer (Odocoileus hemionus) movement and mixing that exerted the greatest influence on the spatial pattern of chronic wasting disease (CWD) in northcentral Colorado, USA. We hypothesized that three scales of mixing (individual, winter subpopulation, or summer subpopulation) might control spatial variation in disease prevalence. We developed a fully Bayesian hierarchical model to compare the strength of evidence for each mixing scale. We found strong evidence that the finest mixing scale corresponded best to the spatial distribution of CWD infection. There was also evidence that land ownership and habitat use play a role in exacerbating the disease, along with the known effects of sex and age. Our analysis demonstrates how information on the scales of spatial processes that generate observed patterns can be used to gain insight when process data are sparse or unavailable.

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.