Kim M. Pepin

Comment on: ‘Blood does not buy goodwill: allowing culling increases poaching of a large carnivore’

Chapron & Treves [[1][1]] present a framework for examining effects of wolf culling policies on wolf population growth rate. They develop a population growth model that estimates an effect of the amount of time per year legal culling is allowed (‘policy effect’) on wolf population growth rates,

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Abstract Our ability to infer unobservable disease‐dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time‐averaged value and are based on population‐level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within‐host processes to FOI is needed. Specifically, within‐host antibody kinetics in wildlife hosts can be short‐lived and produce patterns that are repeatable across individuals, suggesting individual‐level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population‐level FOI signal can be recovered from individual‐level antibody kinetics, despite substantial individual‐level variation. In addition to improving inference, the cross‐scale quantitative antibody approach we describe can reveal insights into drivers of individual‐based variation in disease response, and the role of poorly understood processes such as secondary infections, in population‐level dynamics of disease.

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

BackgroundThe movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management.MethodsWe obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season.ResultsWe found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales.ConclusionsThe analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Deer response to exclusion from stored cattle feed in Michigan, USA.

Disease and damage from white-tailed deer (Odocoileus virginianus) continually threaten the livelihood of agricultural producers and the economy in the United States, as well as challenge state and federal wildlife managers. Threats can be partially addressed by excluding free-ranging deer from livestock-related resources. Throughout the year, use of stored livestock feed by deer in northern Lower Michigan (MI), USA fluctuates, though their presence is relatively consistent. Since 2008, use of livestock areas and resources by deer has been reduced through intensive efforts by livestock producers in cooperation with state and federal agencies. These efforts focused on excluding deer from stored cattle feed in areas where deer were abundant. We monitored deer activity from Jan 2012 to June 2013 on 6 cattle farms in northern MI using GPS collars to evaluate behavioral effects of excluding deer from stored feed. We characterized areas deer occupied before and after installing 2361 m of fences and gates to exclude deer from stored cattle feed. Following fence installation, 9 deer previously accessing stored feed shifted to patterns of habitat use similar to 5 deer that did not use stored feed. However, continued attempts to regain access to stored feed were made at low frequencies, emphasizing the need to maintain the integrity of fences and keep gates closed for damage prevention and biosecurity.

Effects of scale of movement, detection probability, and true population density on common methods of estimating population density

Knowledge of population density is necessary for effective management and conservation of wildlife, yet rarely are estimators compared in their robustness to effects of ecological and observational processes, which can greatly influence accuracy and precision of density estimates. In this study, we simulate biological and observational processes using empirical data to assess effects of animal scale of movement, true population density, and probability of detection on common density estimators. We also apply common data collection and analytical techniques in the field and evaluate their ability to estimate density of a globally widespread species. We find that animal scale of movement had the greatest impact on accuracy of estimators, although all estimators suffered reduced performance when detection probability was low, and we provide recommendations as to when each field and analytical technique is most appropriately employed. The large influence of scale of movement on estimator accuracy emphasizes the importance of effective post-hoc calculation of area sampled or use of methods that implicitly account for spatial variation. In particular, scale of movement impacted estimators substantially, such that area covered and spacing of detectors (e.g. cameras, traps, etc.) must reflect movement characteristics of the focal species to reduce bias in estimates of movement and thus density.

Potential effects of incorporating fertility control into typical culling regimes in wild pig populations

Effective management of widespread invasive species such as wild pigs (Sus scrofa) is limited by resources available to devote to the effort. Better insight of the effectiveness of different management strategies on population dynamics is important for guiding decisions of resource allocation over space and time. Using a dynamic population model, we quantified effects of culling intensities and time between culling events on population dynamics of wild pigs in the USA using empirical culling patterns and data-based demographic parameters. In simulated populations closed to immigration, substantial population declines (50–100%) occurred within 4 years when 20–60% of the population was culled annually, but when immigration from surrounding areas occurred, there was a maximum of 50% reduction, even with the maximum culling intensity of 60%. Incorporating hypothetical levels of fertility control with realistic culling intensities was most effective in reducing populations when they were closed to immigration and when intrinsic population growth rate was too high (> = 1.78) to be controlled by culling alone. However, substantial benefits from fertility control used in conjunction with culling may only occur over a narrow range of net population growth rates (i.e., where net is the result of intrinsic growth rates and culling) that varies depending on intrinsic population growth rate. The management implications are that the decision to use fertility control in conjunction with culling should rely on concurrent consideration of achievable culling intensity, underlying demographic parameters, and costs of culling and fertility control. The addition of fertility control reduced abundance substantially more than culling alone, however the effects of fertility control were weaker than in populations without immigration. Because these populations were not being reduced substantially by culling alone, fertility control could be an especially helpful enhancement to culling for reducing abundance to target levels in areas where immigration can’t be prevented.

Disease-emergence dynamics and control in a socially-structured wildlife species.

Once a pathogen is introduced in a population, key factors governing rate of spread include contact structure, supply of susceptible individuals and pathogen life-history. We examined the interplay of these factors on emergence dynamics and efficacy of disease prevention and response. We contrasted transmission dynamics of livestock viruses with different life-histories in hypothetical populations of feral swine with different contact structures (homogenous, metapopulation, spatial and network). Persistence probability was near 0 for the FMDV-like case under a wide range of parameter values and contact structures, while persistence was probable for the CSFV-like case. There were no sets of conditions where the FMDV-like pathogen persisted in every stochastic simulation. Even when population growth rates were up to 300% annually, the FMDV-like pathogen persisted in <25% of simulations regardless of transmission probabilities and contact structure. For networks and spatial contact structure, persistence probability of the FMDV-like pathogen was always <10%. Because of its low persistence probability, even very early response to the FMDV-like pathogen in feral swine was unwarranted while response to the CSFV-like pathogen was generally effective. When pre-emergence culling of feral swine caused population declines, it was effective at decreasing outbreak size of both diseases by ≥80%.

The persistence of multiple strains of avian influenza in live bird markets

Multiple subtypes of avian influenza (AI) and novel reassortants are frequently isolated from live bird markets (LBMs). However, our understanding of the drivers of persistence of multiple AI subtypes is limited. We propose a stochastic model of AI transmission within an LBM that incorporates market size, turnover rate and the balance of direct versus environmental transmissibility. We investigate the relationship between these factors and the critical community size (CCS) for the persistence of single and multiple AI strains within an LBM. We fit different models of seeding from farms to two-strain surveillance data collected from Shantou, China. For a single strain and plausible estimates for continuous turnover rates and transmissibility, the CCS was approximately 11 800 birds, only a 4.2% increase in this estimate was needed to ensure persistence of the co-infecting strains (two strains in a single host). Precise values of CCS estimates were sensitive to changes in market turnover rate and duration of the latent period. Assuming a gradual daily sell rate of birds the estimated CCS was higher than when an instantaneous selling rate was assumed. We were able to reproduce prevalence dynamics similar to observations from a single market in China with infection seeded every 5–15 days, and a maximum non-seeding duration of 80 days. Our findings suggest that persistence of co-infections is more likely to be owing to sequential infection of single strains rather than ongoing transmission of both strains concurrently. In any given system for a fixed set of ecological and epidemiological conditions, there is an LBM size below which the risk of sustained co-circulation is low and which may suggest a clear policy opportunity to reduce the frequency of influenza co-infection in poultry.

Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA

Abstract Understanding factors that influence observation processes is critical for accurate assessment of underlying ecological processes. When indirect methods of detection, such as environmental DNA, are used to determine species presence, additional levels of uncertainty from observation processes need to be accounted for. We conducted a field trial to evaluate observation processes of a terrestrial invasive species (wild pigs‐ Sus scrofa) from DNA in water bodies. We used a multi‐scale occupancy analysis to estimate different levels of observation processes (detection, p): the probability DNA is available per sample (θ), the probability of capturing DNA per extraction (γ), and the probability of amplification per qPCR run (δ). We selected four sites for each of three water body types and collected 10 samples per water body during two months (September and October 2016) in central Texas. Our methodology can be used to guide sampling adaptively to minimize costs while improving inference of species distributions. Using a removal sampling approach was more efficient than pooling samples and was unbiased. Availability of DNA varied by month, was considerably higher when water pH was near neutral, and was higher in ephemeral streams relative to wildlife guzzlers and ponds. To achieve a cumulative detection probability >90% (including availability, capture, and amplification), future studies should collect 20 water samples per site, conduct at least two extractions per sample, and conduct five qPCR replicates per extraction. Accounting for multiple levels of uncertainty of observation processes improved estimation of the ecological processes and provided guidance for future sampling designs.

Quantifying site-level usage and certainty of absence for an invasive species through occupancy analysis of camera-trap data

Efficient implementation of management programs for invasive species depends on accurate surveillance for guiding prioritization of surveillance and control resources in space and time. Occupancy probabilities can be used to determine where surveillance should occur. Conversely, knowledge of the certainty of site-level absence is of special interest in situations where the objective is to completely remove populations despite substantial risk of re-invasion. Indeed, the decision to shift from emphasizing control activities over the full range to emphasizing reinvasion prevention, surveillance, and response near the borders, depends on accurate knowledge of absence across space. We used a dynamic occupancy model to monitor changes in the distribution of an invasive species, feral swine (Sus scrofa), based on camera-trap data collected as part of a management program from June 2014 to January 2016 in San Diego County, California. Site usage of feral swine declined overall. The most informative predictors of site usage were spatial (latitude and longitude). Site-level non-usage rates increased over time and in response to management removal efforts; and site-level usage rates were heavily impacted by having neighboring sites that were used. Combining the detection probability estimated from the occupancy model and Bayes Theorem, we demonstrated how certainty of local (site-level) absence can be estimated iteratively in time in areas with negative surveillance (no detections) data. Our framework provides a means for using management-based surveillance data to quantify certainty of site-level absence of an invasive species, allowing for adaptive prioritization of surveillance and control resources. Our approach is flexible for application to other species and types of surveillance (e.g., track-plates, eDNA).