Seeing Is Be-Leaving: Perception Informs Migratory Decisions in Sierra Nevada Bighorn Sheep (Ovis canadensis sierrae)

Seasonal migration is a behavioral response to predictable variation in environmental resources, risks, and conditions. In behaviorally plastic migrants, migration is a conditional strategy that depends, in part, on an individual’s informational state. The cognitive processes that underlie how facultative migrants understand and respond to their environment are not well understood. We compared perception of the present environment to memory and omniscience as competing cognitive mechanisms driving altitudinal migratory decisions in an endangered ungulate, the Sierra Nevada bighorn sheep (Ovis canadensis sierrae) using 1,298 animal years of data, encompassing 460 unique individuals. We built a suite of statistical models to partition variation in fall migratory status explained by cognitive predictors, while controlling for non-cognitive drivers. To approximate attribute memory, we included lagged attributes of the range an individual experienced in the previous year. We quantified perception by limiting an individual’s knowledge of migratory range to the area and attributes visible from its summer range, prior to migrating. Our results show that perception, in addition to the migratory propensity of an individual’s social group, and an individual’s migratory history are the best predictors of migration in our system. Our findings suggest that short-distance altitudinal migration is, in part, a response to an individual’s perception of conditions on alterative winter range. In long-distance partial migrants, exploration of migratory decision-making has been limited, but it is unlikely that migratory decisions would be based on sensory cues from a remote target range. Differing cognitive mechanisms underpinning short and long-distance migratory decisions will result in differing levels of behavioral plasticity in response to global climate change and anthropogenic disturbance, with important implications for management and conservation of migratory species.

Puerto Rico plain pigeon, scaly-naped pigeon and red-tailed hawk: population dynamics and association patterns before and after hurricanes

Since the 1980s, 3 major hurricanes have made landfall on Puerto Rico: Hugo in September 1989 (Saffir-Simpson scale, category 4), Georges in September 1998 (category 3) and María in September 2017 (category 4). María was the most devastating hurricane since the 3 major hurricanes that occurred in 1899-�1932. Major hurricanes can cause severe abundance declines and population bottlenecks by decreasing survival and reproductive rates and increasing predation and competition for limited resources. In April to June 1986-2021, we used distance sampling to estimate abundance and monitor the population dynamics of the endangered Puerto Rico plain pigeon Patagioenas inornata wetmorei and the abundant scaly-naped pigeon P. squamosa and red-tailed hawk Buteo jamaicensis. Here, we fit a Bayesian state-space logistic model with distance sampling abundance estimates to generate posterior estimates of maximum population growth rate and population carrying capacity, and predict abundance in April to June 2020-2030. In addition, we used N-mixture and 2-species models to assess association patterns in April to June 2015-2019. The scaly-naped pigeon and red-tailed hawk populations did not decline, or recovered faster from their declines than the plain pigeon population after the hurricanes. The association patterns between species were positive but variable for the 2 pigeon species and negative but variable for the plain pigeon and red-tailed hawk. At lowered abundance (i.e. mean ± SE estimates N̂ = 1043 ± 476 island-wide and N̂ = 522 ± 157 at the centre of abundance in the east-central region in April to June 2018-2021), the plain pigeon may become extinct if another hurricane with the path and intensity of María makes landfall on the island during the current decade.

Large herbivores in a partially migratory population search for the ideal free home

Migration is a tactic used across taxa to access resources in temporally heterogenous landscapes. Populations that migrate can attain higher abundances because such movements allow access to higher quality resources, or reduction in predation risk resulting in increased fitness. However, most migratory species occur in partially migratory populations, a mix of migratory and non-migratory individuals. It is thought that the portion of migrants in a partial migration population is maintained either through 1) a population-level evolutionary stable state where counteracting density-dependent vital rates act on migrants and residents to balance fitness, or 2) conditional migration, where the propensity to migrate is influenced by the individual's state. However, in many respects, migration is also a form of habitat selection and the proportion of migrants and residents may be the result of density-dependent habitat selection. Here, we test whether the theory of Ideal Free Distribution (IFD) can explain the coexistence of different migratory tactics in a partially migratory population. IFD predicts individuals exhibit density-dependent vital rates and select different migratory tactics to maximize individual fitness resulting in equal fitness (λ) between tactics. We tested the predictions of IFD in a partially migratory elk population that declined by 70% with 19 years of demographic data and migratory tactic switching rates from >300 individuals. We found evidence of density dependence for resident pregnancy and adult female survival providing a fitness incentive to switch tactics. Despite differences in vital rates between migratory tactics, mean λ (fitness) was equal. However, as predicted by the IFD, individuals switched tactics toward those of higher fitness. Our analysis reveals that partial migration may be driven by tactic selection that follows the ideal free distribution. These findings reinforce that migration across taxa may be a polymorphic behavior in large herbivores where migratory tactic selection is determined by differential costs and benefits, mediated by density-dependence.

Optimising deployment time of remote cameras to estimate abundance of female bighorn sheep

Abstract ContextWildlife biologists accumulate large quantities of images from remote cameras, which can be time- and cost-prohibitive to archive and analyse. Remote-camera projects would benefit from not setting cameras longer than needed and not analysing more images than needed; however, there is a lack of information about optimal deployment time required for remote-camera surveys to estimate ungulate abundance. AimsThe objective was to estimate abundance of adult females in a population of Rocky Mountain bighorn sheep (Ovis canadensis canadensis) in Utah, USA, from 2012 to 2014, and determine whether this type of study can be conducted more efficiently. Because females are the most important cohort for population growth, remote cameras were set at three water sources and mark–resight models in Program MARK were used. MethodsWe compared estimated abundance of collared and uncollared females by number of days cameras were set using 31 replicated abundance estimates from each year starting 1 July. Each replicated estimate used a different number of days and photographs from a 62-day sampling period (1 July to 31 August). Key resultsAbundance estimates ranged from 44 to 98 animals. Precise estimates of abundance, however, were obtained with only 12 days of sampling in each year. By analysing only 12 days of images rather than 62 days in all years, the estimated mean of 58 adult females would have changed by only 7 individuals (±4 individuals, range=3–10 animals), the s.e. would have increased by a mean of only 4 individuals (±1.6, range=2.0–5.2 individuals) and a mean of only 18% (±10.5%, range=8–29%) of images would have been analysed. Across the study, analysis of >23000 (>80%) images could have been avoided, saving time and money. ConclusionsThe results indicate that an asymptotic relationship exists between estimated abundance of female bighorn sheep and remote-camera deployment time. ImplicationsThe mark–resight methods used in the present study would work for other ungulates in which individuals are radio collared or marked using remote cameras set at water sources, trail crossings or mineral licks. These findings can help researchers reduce cost of setting, servicing, archiving and analysing photographs from remote cameras for ungulate population monitoring.

Linking population performance to nutritional condition in an alpine ungulate

Bighorn sheep (Ovis canadensis) can live in extremely harsh environments and subsist on submaintenance diets for much of the year. Under these conditions, energy stored as body fat serves as an essential reserve for supplementing dietary intake to meet metabolic demands of survival and reproduction. We developed equations to predict ingesta-free body fat in bighorn sheep using ultrasonography and condition scores in vivo and carcass measurements postmortem. We then used in vivo equations to investigate the relationships between body fat, pregnancy, overwinter survival, and population growth in free-ranging bighorn sheep in California and Nevada. Among 11 subpopulations that included alpine winter residents and migrants, mean ingesta-free body fat of lactating adult females during autumn ranged between 8.8% and 15.0%; mean body fat for nonlactating females ranged from 16.4% to 20.9%. In adult females, ingesta-free body fat > 7.7% during January (early in the second trimester) corresponded with a > 90% probability of pregnancy and ingesta-free body fat > 13.5% during autumn yielded a probability of overwinter survival > 90%. Mean ingesta-free body fat of lactating females in autumn was positively associated with finite rate of population increase (λ) over the subsequent year in bighorn sheep subpopulations that wintered in alpine landscapes. Bighorn sheep with ingesta-free body fat of 26% in autumn and living in alpine environments possess energy reserves sufficient to meet resting metabolism for 83 days on fat reserves alone. We demonstrated that nutritional condition can be a pervasive mechanism underlying demography in bighorn sheep and characterizes the nutritional value of their occupied ranges. Mountain sheep are capital survivors in addition to being capital breeders, and because they inhabit landscapes with extreme seasonal forage scarcity, they also can be fat reserve obligates. Quantifying nutritional condition is essential for understanding the quality of habitats, how it underpins demography, and the proximity of a population to a nutritional threshold.

Assessing the importance of demographic parameters for population dynamics using Bayesian integrated population modeling

To successfully respond to changing habitat, climate or harvest, managers need to identify the most effective strategies to reverse population trends of declining species and/or manage harvest of game species. A classic approach in conservation biology for the last two decades has been the use of matrix population models to determine the most important vital rates affecting population growth rate (λ), that is, sensitivity. Ecologists quickly realized the critical role of environmental variability in vital rates affecting λ by developing approaches such as life-stage simulation analysis (LSA) that account for both sensitivity and variability of a vital rate. These LSA methods used matrix-population modeling and Monte Carlo simulation methods, but faced challenges in integrating data from different sources, disentangling process and sampling variation, and in their flexibility. Here, we developed a Bayesian integrated population model (IPM) for two populations of a large herbivore, elk (Cervus canadensis) in Montana, USA. We then extended the IPM to evaluate sensitivity in a Bayesian framework. We integrated known-fate survival data from radio-marked adults and juveniles, fecundity data, and population counts in a hierarchical population model that explicitly accounted for process and sampling variance. Next, we tested the prevailing paradigm in large herbivore population ecology that juvenile survival of neonates <90 d old drives λ using our Bayesian LSA approach. In contrast to the prevailing paradigm in large herbivore ecology, we found that adult female survival explained more of the variation in λ than elk calf survival, and that summer and winter elk calf survival periods were nearly equivalent in importance for λ. Our Bayesian IPM improved precision of our vital rate estimates and highlighted discrepancies between count and vital rate data that could refine population monitoring, demonstrating that combining sensitivity analysis with population modeling in a Bayesian framework can provide multiple advantages. Our Bayesian LSA framework will provide a useful approach to addressing conservation challenges across a variety of species and data types.

Estimating demographic parameters using a combination of known-fate and open N-mixture models

Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark-resight data sets. We provide implementations in both the BUGS language and an R package.

Population Estimation Methods for Free-Ranging Dogs: A Systematic Review

The understanding of the structure of free-roaming dog populations is of extreme importance for the planning and monitoring of populational control strategies and animal welfare. The methods used to estimate the abundance of this group of dogs are more complex than the ones used with domiciled owned dogs. In this systematic review, we analyze the techniques and the results obtained in studies that seek to estimate the size of free-ranging dog populations. Twenty-six studies were reviewed regarding the quality of execution and their capacity to generate valid estimates. Seven of the eight publications that take a simple count of the animal population did not consider the different probabilities of animal detection; only one study used methods based on distances; twelve relied on capture-recapture models for closed populations without considering heterogeneities in capture probabilities; six studies applied their own methods with different potential and limitations. Potential sources of bias in the studies were related to the inadequate description or implementation of animal capturing or viewing procedures and to inadequacies in the identification and registration of dogs. Thus, there was a predominance of estimates with low validity. Abundance and density estimates carried high variability, and all studies identified a greater number of male dogs. We point to enhancements necessary for the implementation of future studies and to potential updates and revisions to the recommendations of the World Health Organization with respect to the estimation of free-ranging dog populations.

Disentangling the effects of climate, density dependence, and harvest on an iconic large herbivore's population dynamics

Understanding the relative effects of climate, harvest, and density dependence on population dynamics is critical for guiding sound population management, especially for ungulates in arid and semiarid environments experiencing climate change. To address these issues for bison in southern Utah, USA, we applied a Bayesian state-space model to a 72-yr time series of abundance counts. While accounting for known harvest (as well as live removal) from the population, we found that the bison population in southern Utah exhibited a strong potential to grow from low density (β0 = 0.26; Bayesian credible interval based on 95% of the highest posterior density [BCI] = 0.19-0.33), and weak but statistically significant density dependence (β1 = -0.02, BCI = -0.04 to -0.004). Early spring temperatures also had strong positive effects on population growth (Pfat1 = 0.09, BCI = 0.04-0.14), much more so than precipitation and other temperature-related variables (model weight > three times more than that for other climate variables). Although we hypothesized that harvest is the primary driving force of bison population dynamics in southern Utah, our elasticity analysis indicated that changes in early spring temperature could have a greater relative effect on equilibrium abundance than either harvest or. the strength of density dependence. Our findings highlight the utility of incorporating elasticity analyses into state-space population models, and the need to include climatic processes in wildlife management policies and planning.

Integrating acoustic telemetry into mark-recapture models to improve the precision of apparent survival and abundance estimates

Capture-mark-recapture models are useful tools for estimating demographic parameters but often result in low precision when recapture rates are low. Low recapture rates are typical in many study systems including fishing-based studies. Incorporating auxiliary data into the models can improve precision and in some cases enable parameter estimation. Here, we present a novel application of acoustic telemetry for the estimation of apparent survival and abundance within capture-mark-recapture analysis using open population models. Our case study is based on simultaneously collecting longline fishing and acoustic telemetry data for a large mobile apex predator, the broadnose sevengill shark (Notorhynchus cepedianus), at a coastal site in Tasmania, Australia. Cormack-Jolly-Seber models showed that longline data alone had very low recapture rates while acoustic telemetry data for the same time period resulted in at least tenfold higher recapture rates. The apparent survival estimates were similar for the two datasets but the acoustic telemetry data showed much greater precision and enabled apparent survival parameter estimation for one dataset, which was inestimable using fishing data alone. Combined acoustic telemetry and longline data were incorporated into Jolly-Seber models using a Monte Carlo simulation approach. Abundance estimates were comparable to those with longline data only; however, the inclusion of acoustic telemetry data increased precision in the estimates. We conclude that acoustic telemetry is a useful tool for incorporating in capture-mark-recapture studies in the marine environment. Future studies should consider the application of acoustic telemetry within this framework when setting up the study design and sampling program.

Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry

Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and generalized linear mixed-effects models to relate camera trap probabilities for marked animals to independent space use from telemetry relocations using 2 years of data for fishers (Pekania pennanti) as a case study. We evaluated (1) camera trap efficacy by estimating how camera detection probabilities are related to nearby telemetry relocations and (2) whether home range utilization density estimated from telemetry data adequately predicts camera detection probabilities, which would indicate consistency of the two data types. The number of telemetry relocations within 250 and 500 m from camera traps predicted detection probability well. For the same number of relocations, females were more likely to be detected during the first year. During the second year, all fishers were more likely to be detected during the fall/winter season. Models predicting camera detection probability and photo counts solely from telemetry utilization density had the best or nearly best Akaike Information Criterion (AIC), suggesting that telemetry and camera traps provide consistent information on space use. Given the same utilization density, males were more likely to be photo-captured due to larger home ranges and higher movement rates. Although methods that combine data types (spatially explicit capture–recapture) make simple assumptions about home range shapes, it is reasonable to conclude that in our case, camera trap data do reflect space use in a manner consistent with telemetry data. However, differences between the 2 years of data suggest that camera efficacy is not fully consistent across ecological conditions and make the case for integrating other sources of space-use data.