Biometrics and Population Modeling IV

Contributed Paper
ROOM: Rooms 16 – Acoma, 32 – Teseque and 15 – Zuni Combined
SESSION NUMBER: 104
 

1:10PM Novel Application of Adaptive Sampling Principles to Spatial Capture-Recapture
Alec Wong; Jeffrey A. Royle; Angela Fuller
Rare species present inherent challenges to data collection, particularly when the species is spatially clustered over large areas, such that the encounter frequency of the organism is low. These challenges can be ameliorated by the use of adaptive sampling, whereby a probabilistic sampling method (simple-random, systematic, etc.) is employed first, but additional sites are added in the vicinity of sites where some index variable exceeds an a priori threshold. We applied these principles to the spatially explicit capture-recapture (SCR) analytical framework and assessed the performance of the adaptive SCR model (AS-SCR) under a two-stage adaptive sampling procedure by simulation. In the AS-SCR scenario, SCR sampling was informed by a count index assumed to be proportional to local population size. The decision to re-visit the site and apply SCR sampling was dependent upon whether the index variable exceeded the a priori threshold. For comparison, we also evaluated the informative sampling scenario (AS-SCR without modeling index information), a full standard SCR (F-SCR) scenario as a reference baseline, and SCR applied at a simple random sample of sites (SRS-SCR) equal to AS-SCR. Standard capture-recapture data were simulated and analyzed using the aforementioned models and sampling scenarios. We observed minimal bias and comparable variance with respect to parameter estimates provided by the standard SCR model and sampling implementation, but with substantially reduced effort: with AS-SCR, the data were drawn from approximately 50% of the sites sampled in F-SCR, in expectation, indicating significant cost saving potential by applying adaptive sampling.
1:30PM Open Population Spatial Capture-Recapture Models – Promises and Challenges Capture-Recapture Models – Promises and Challenges
Rahel Sollmann; Beth Gardner; Jerrold Belant
With continued biodiversity loss and habitat fragmentation, the need for assessment of long-term population dynamics and population monitoring of threatened species is growing. Capture-recapture methods provide a powerful way to estimate population size and dynamics while accounting for imperfect individual detection. Spatial capture-recapture (SCR) models are a fairly recent expansion of capture-recapture methods. They are of particular interest for rare and wide-ranging animals as they make efficient use of capture-recapture data, while being robust to study design changes. Relatively few studies have implemented open SCR models and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state space definition and between primary period movement model on demographic parameter estimation. The results of our simulation study show that movement and survival are confounded in open SCR models when little information is known about between primary period movements of animals. Particularly, when activity centers are modeled as random across primary periods, estimates of survival increase with increasing state space. This effect is ameliorated when modeling shifts in activity centers between primary periods as a Markovian process. We demonstrate the implications of these difficulties on a multi-year hair snare data set of black bears (Ursus americanus) and bobcats (Lynx rufus) in Michigan. In general, we suggest that open SCR models can provide an efficient and flexible framework for long-term monitoring of populations, when detection data contain sufficient information to model between-year shifts in home ranges. Augmenting these models with telemetry information to inform between-year movement may be a promising avenue for further model development.
1:50PM Quantifying Spatial Variation in Growth in a Recovering Grizzly Bear Population
Tabitha Graves; Amy MacLeod; J. Andrew Royle; Kevin McKelvey; John Boulanger; Katherine Kendall
Understanding trend in population abundance is fundamental to wildlife management and is critically important for conservation of at-risk taxa. However, trend can be challenging to quantify, especially when population density is low, range is expanding or contracting, the conservation area is large, or there is spatial variation in trend within the population. We used genetic detection data and spatial capture-recapture (SCR) modeling to estimate local density and population growth rates in an expanding grizzly bear (Ursus arctos) population in northwestern Montana, USA. Bear rubs were surveyed twice annually in 2004, 2009-2012 (3,808-4,795 rubs) throughout the population’s range. We detected ~1/4 to 1/3 of the population each year. Using SCR models in a maximum likelihood framework, we estimated overall and spatially explicit growth rates (lambda). Total annual population rate of change 2004-2012 was 1.056 (95% CI = 1.033-1.079). Local rates of change within the NCDE were highest in the areas where density was lowest in 2004 and lowest in and near the initial core population in Glacier National Park. The amount of space used by bears, estimated by the spatial dispersion parameter σ, decreased as density increased. The large sample obtained by genetic detection provided fine-scale spatial information on variation in density and trend within the population useful for designing monitoring and management strategies tailored to area-specific needs and priorities. As efficient genetic and remote camera sampling approaches continue to be developed for an ever expanding array of taxa, spatial capture-recapture modeling of these types of data can provide valuable local and ecosystem-wide insight into the status of difficult-to-study populations.
2:10PM Monitoring Matters
Douglas H. Johnson
Monitoring is critically important for assessing the status of systems, such as animal populations, habitat conditions, or ecosystems in general. Monitoring that is unguided by specific hypotheses has come under criticism from some and given the pejorative label, “surveillance monitoring.” Certainly, if some hypothesis is in need of testing, or some management action is planned, appropriate data should be collected to evaluate the hypothesis or assess the action. But gathering such data is a short-term and focused endeavor, whereas monitoring as generally defined is a long-term activity sensitive to a wider variety of changes in the system. I cite several examples of monitoring programs that provided valuable insight into critical issues and were essential in addressing consequences of unanticipated events such as global climate change and the Exxon-Valdez and Deepwater Horizon oil spills. I discuss other topics as well, such as why “trend” is a poorly defined concept, why seeking unbiased estimators can be a poor strategy, what to do if a monitored population expands beyond the area where it had been surveyed, and how new-and-improved survey methods can be reconciled with long-term historical data based on older methods.
2:30PM Generalized Spatial Partial Identity – a Generalization and Extension of Current Spatial Capture-Recapture Models with Latent Individual Identities
Ben C. Augustine; J. Andrew Royle; Marcella J. Kelly
Current spatial capture-recapture (SCR) models can accommodate a range of information about individual identity from all identities of captured individuals being known in typical spatial capture-recapture to no identities known in Chandler-Royle unmarked SCR. Intermediate cases are spatial mark-resight (SMR), the Chandler-Clark SCR-occupancy hybrid, an in development random identity thinning model, and the 2-flank spatial partial identity model (SPIM). In all but typical SCR, some or all of the individual identities are latent and the precision of the density estimate is a function of how much uncertainty there is in the latent individual identities. Here, we present the generalized SPIM (genSPIM), of which all models previously listed are special cases. GenSPIM allows for individual observations to be connected to other observations (observation A is certainly from the same individual as observation B) and for groups of connected observations to be excluded from matching with other groups of connected observations (observations A and B certainly do not match observations C and D). We show that genSPIM can fit the special cases listed above and allow for more information about individual identities to be used to further reduce the uncertainty in individual identities and thus increase the precision of density estimates. For example, in any of these models, we may have information that excludes matches between unmarked individuals such as sex, age class, or ear tag color that is currently not being used. Further, in SMR with natural marks, it often cannot be determined if two “marked” individuals are not actually the same individual and so only one can be used. GenSPIM allows both sets of linked observations to be used. Finally, we show that genSPIM can be used to model partial genotypes and that density can be estimated using a fraction of the loci at a fraction of the cost.
2:50PM Refreshment Break
3:20PM Identifying Unique Individuals Using Natural Pelt Patterns to Estimate Population Abundance with Mark-Resight Models
Ben S. Teton; Jesse S. Lewis; Christina Tombach Wright; Mike White; Hillary Young
Invasive wild pigs (Sus scrofa) cause extensive damage to ecological resources, agricultural land, and private property throughout much of the United States. Since at least 1990, the 270,000-acre, privately owned Tejon Ranch in the Tehachapi Mountains of California has been occupied by a population of wild pigs. To facilitate wildlife management planning, the Tejon Ranch Conservancy (TRC) has developed a method of identifying and cataloguing individual wild pigs using natural markings alone, for the purpose of mark-resight population estimation. This method was tested over a period of 16 months using an array of 48 trap-cameras across a 48km² survey grid. Wild pig images captured during this period were standardized based on image quality and flank distinctiveness. Thresholds of identifiability were established to account for the inherent variability of natural markings between individuals. The majority of wild pigs in this region are homogeneous in appearance, with indistinct black pelts and few defining features, yet this method was able to reliably identify approximately 20% of all individuals that encountered the camera array. Capture histories of these identifiable individuals were recorded and catalogued in order to facilitate estimation of population abundance through time with standard mark-resight analytical methods. As this method requires no trapping or tagging of any kind, it may be utilized as a cost and resource efficient alternative to traditional mark-resight techniques that rely on ear-tags or neck-bands for individual identification. To better enable comparison of these methods, the results of analyses from natural mark photo-ID are compared with those derived from a traditional trapping and tagging effort that took place contemporaneously across the same survey grid. This study uses wild pigs to highlight the potential of a simple, natural mark photo-ID catalogue as a management tool that could be widely applied to other species found in remote wilderness settings.
3:40PM Novel Methods to Estimate Abundance of Unmarked Animals Using Camera Trap Data
Anna K. Moeller; Paul M. Lukacs; Jon Horne
Abundance estimates are central to the field of ecology and are an important tool for wildlife managers. To estimate deer and elk abundances, most western wildlife agencies rely on aerial survey methods, which can be expensive, dangerous, and difficult to implement. One step toward noninvasive abundance estimation is the use of passive “traps” such as remote cameras or acoustic recording devices. While these have been used successfully to estimate abundance from individually identifiable animals, current methods are lacking when species have no natural markings. To address these issues, we developed three methods for estimating abundance of unmarked animals using camera trap data. First, we used random sampling theory to use pictures at multiple cameras as snapshot samples of density. Then we expanded this theory within a time-to-event framework to leverage the relationship between abundance and encounter rate. Intuitively, as the number of animals in an area increases, the trapping rate increases. We developed two new models to estimate abundance from trapping rate in time and space. We tested all three methods on simulated data and used them to estimate elk abundance in Idaho. In simulations with known population size, all three methods produced unbiased estimates of abundance, regardless of animal movement rate. In one study area, they produced abundance estimates that compared favorably to the most recent aerial survey estimate. In the other study area, these models allowed us to estimate elk abundance where this has never before been possible. Our three methods provide a new framework for estimating abundance from unmarked animals and for utilizing camera data most efficiently. They have wide applications across species; biologists can select the method that best meets their specific circumstances. All three methods greatly reduce the amount of data required for analysis, making them practical management tools.
4:00PM Single Survey Bobcat Density Estimation Using Fecal DNA and Spatial Capture Recapture
Dana J. Morin; Lisette P. Waits; David C. McNitt; Marcella J. Kelly
Bobcat (Lynx rufus) population density estimates are necessary to inform management and conservation yet are difficult to obtain for such cryptic species. We implemented a single-survey, closed session scat sampling protocol to estimate bobcat density using fecal DNA and spatial capture-recapture at two sites in Virginia, USA. We estimated density for five sessions at one study site (three summer sessions and two winter sessions 2011 through 2013). However, we only estimated density for three summer sessions at the second site due to low detection rates and few spatial recaptures in the winter. Summer densities ranged from 6.65 to 13.96 bobcats/100 km2 across both sites (posterior modes), and some estimates suffered from poor precision. We suggest the summer session estimates are representative of the resident population, that differences in density between summer and winter are representative of potential net recruitment, and that differences between consecutive summer sessions are representative of the net recruitment realized for the population (dependent on survival and emigration). We also estimated realized density surfaces to demonstrate how spatial heterogeneity in local densities can be used for spatially-explicit harvest, for evaluating habitat associations, or for identifying areas with potential for increased predation or human-wildlife conflict. Finally, we assessed factors affecting precision in estimates and provide recommendations to improve detection and reduce credible intervals that may be applicable across the bobcat range and to other species. The single catch-effort transect methodology represents an improvement in monitoring bobcat populations specifically, or as part of concurrent monitoring for multiple carnivore species.
4:20PM Are Forecasts of Future Abundance for Rare And Declining Species Valid for Population Viability Analyses?
Edward O. Garton; Christian A. Hagen
Stochastic growth models offer a powerful tool to evaluate long-term population dynamics of Greater Sage-Grouse (Centrocercus urophasianus) and Lesser Prairie Chicken (Tympanuchus palidicinctus)by integrating analyses of both density-dependent and density independent factors affecting annual rates of change into a single analysis capable of estimating current and future carrying capacity for a species across a region. We evaluated the potential for 26 stochastic growth models to describe the observed dynamics of 6 Greater Sage-Grouse and 4 Lesser Prairie Chicken populations attending leks across each species range from 1964 to 2016 and identified the Gompertz model with declining carrying capacities (-1.1% to -14.1% per year for grouse and -1.8% to -9.6% per year for chickens) as statistically superior to the 25 alternative models. The best models imply substantially different probabilities of persistence for populations if observed patterns of population fluctuations and trend are valid to forecast into the foreseeable future. The validity of future forecasts for each species were evaluated by forecasting future abundance for each species based on models developed previously in earlier analyses and comparing those forecasts to estimates of abundance from 5 additional years of lek counts for grouse and 4 additional years for chickens. Predicted future abundances of Greater Sage-Grouse from the best models predicted 98% of variation in observed lek attendance while forecasts for Lesser Prairie Chickens predicted 91% of variation in observed lek attendance from ground counts and 88% of variation in aerial surveys.
4:40PM The Influence of Population Density on the Expression of Heterogeneity in Reproductive Traits in a Long-Lived Marine Predator, the Grey Seal
Janelle Badger; W. Don Bowen; Cornelia den Heyer; Greg A. Breed
Increasing population density can reduce the amount of food resources that females can invest in reproduction. Individual heterogeneity in fitness components can create a ranked population structure in which “robust” individuals are consistently more successful than others, or are more robust to changes in resources. We tested for heterogeneity in reproductive performance using 30 years of mark-resighting data on female grey seals (Halichoerus grypus) at Sable Island, Canada. We used Bayesian generalized linear mixed- effect models and hierarchical multi-state mark-recapture models to investigate if population size negatively influences annual and lifetime reproductive success and if this effect is variable among females. Sighting histories of 2278 known-aged females with a total of 22561 pupping events were used for analysis. After accounting for effects of female age, parity, offspring sex, and random year effects, we found that population size was a positive predictor of both annual pupping success and longer-term reproductive rate. Among- individual variance in reproductive traits (pupping success: σα2 credible intervals (CRI) = [0.868,0.940], reproductive rate: σα2 CRI = [0.334,0.339]) and among-individual variance in their response to population size (pupping success: σβ2 CRI = [0.322,0.546], reproductive rate: σβ2 CRI = [0.281,0.354]) showed considerable individual reproductive heterogeneity in this population. Previous breeders had a higher transition rate into a reproductive state than previous non-breeders (ψBB CRI: [0.883,0.965], ψNB CRI: [0.796, 0.862]). Previous successful breeding is also a significant and positive predictor for pupping success. Robust females responded to increases in population size better than their more “frail” conspecifics (pupping success: R2 = 0.477, reproductive rate: R2 = 0.636). This inertia indicates individual quality is more influential to consistent yearly reproductive success than costs associated with rearing pups. Assessing individual-level reproductive success in varying conditions provides insights into the evolution of life histories and population responses to environmental change.

 

Contributed Paper
Location: Albuquerque Convention Center Date: September 27, 2017 Time: 1:10 pm - 5:00 pm