Effectiveness of population-based recovery actions for threatened southern mountain caribou.

Habitat loss is affecting many species, including the southern mountain caribou (Rangifer tarandus caribou) population in western North America. Over the last half century, this threatened caribou population's range and abundance have dramatically contracted. An integrated population model was used to analyze 51 years (1973-2023) of demographic data from 40 southern mountain caribou subpopulations to assess the effectiveness of population-based recovery actions at increasing population growth. Reducing potential limiting factors on threatened caribou populations offered a rare opportunity to identify the causes of decline and assess methods of recovery. Southern mountain caribou abundance declined by 51% between 1991 and 2023, and 37% of subpopulations were functionally extirpated. Wolf reduction was the only recovery action that consistently increased population growth when applied in isolation, and combinations of wolf reductions with maternal penning or supplemental feeding provided rapid growth but were applied to only four subpopulations. As of 2023, recovery actions have increased the abundance of southern mountain caribou by 52%, compared to a simulation with no interventions. When predation pressure was reduced, rapid population growth was observed, even under contemporary climate change and high levels of habitat loss. Unless predation is reduced, caribou subpopulations will continue to be extirpated well before habitat conservation and restoration can become effective.

Integrating basic and applied research to estimate carnivore abundance.

A clear connection between basic research and applied management is often missing or difficult to discern. We present a case study of integration of basic research with applied management for estimating abundance of gray wolves (Canis lupus) in Montana, USA. Estimating wolf abundance is a key component of wolf management but is costly and time intensive as wolf populations continue to grow. We developed a multimodel approach using an occupancy model, mechanistic territory model, and empirical group size model to improve abundance estimates while reducing monitoring effort. Whereas field-based wolf counts generally rely on costly, difficult-to-collect monitoring data, especially for larger areas or population sizes, our approach efficiently uses readily available wolf observation data and introduces models focused on biological mechanisms underlying territorial and social behavior. In a three-part process, the occupancy model first estimates the extent of wolf distribution in Montana, based on environmental covariates and wolf observations. The spatially explicit mechanistic territory model predicts territory sizes using simple behavioral rules and data on prey resources, terrain ruggedness, and human density. Together, these models predict the number of packs. An empirical pack size model based on 14 years of data demonstrates that pack sizes are positively related to local densities of packs, and negatively related to terrain ruggedness, local mortalities, and intensity of harvest management. Total abundance estimates for given areas are derived by combining estimated numbers of packs and pack sizes. We estimated the Montana wolf population to be smallest in the first year of our study, with 91 packs and 654 wolves in 2007, followed by a population peak in 2011 with 1252 wolves. The population declined ~6% thereafter, coincident with implementation of legal harvest in Montana. Recent numbers have largely stabilized at an average of 191 packs and 1141 wolves from 2016 to 2020. This new approach accounts for biologically based, spatially explicit predictions of behavior to provide more accurate estimates of carnivore abundance at finer spatial scales. By integrating basic and applied research, our approach can therefore better inform decision-making and meet management needs.

Lack of phenological shift leads to increased camouflage mismatch in mountain hares.

Understanding whether organisms will be able to adapt to human-induced stressors currently endangering their existence is an urgent priority. Globally, multiple species moult from a dark summer to white winter coat to maintain camouflage against snowy landscapes. Decreasing snow cover duration owing to climate change is increasing mismatch in seasonal camouflage. To directly test for adaptive responses to recent changes in snow cover, we repeated historical (1950s) field studies of moult phenology in mountain hares (Lepus timidus) in Scotland. We found little evidence that population moult phenology has shifted to align seasonal coat colour with shorter snow seasons, or that phenotypic plasticity prevented increases in camouflage mismatch. The lack of responses resulted in 35 additional days of mismatch between 1950 and 2016. We emphasize the potential role of weak directional selection pressure and low genetic variability in shaping the scope for adaptive responses to anthropogenic stressors.

Estimating large carnivore populations at global scale based on spatial predictions of density and distribution - Application to the jaguar (Panthera onca).

Broad scale population estimates of declining species are desired for conservation efforts. However, for many secretive species including large carnivores, such estimates are often difficult. Based on published density estimates obtained through camera trapping, presence/absence data, and globally available predictive variables derived from satellite imagery, we modelled density and occurrence of a large carnivore, the jaguar, across the species' entire range. We then combined these models in a hierarchical framework to estimate the total population. Our models indicate that potential jaguar density is best predicted by measures of primary productivity, with the highest densities in the most productive tropical habitats and a clear declining gradient with distance from the equator. Jaguar distribution, in contrast, is determined by the combined effects of human impacts and environmental factors: probability of jaguar occurrence increased with forest cover, mean temperature, and annual precipitation and declined with increases in human foot print index and human density. Probability of occurrence was also significantly higher for protected areas than outside of them. We estimated the world's jaguar population at 173,000 (95% CI: 138,000-208,000) individuals, mostly concentrated in the Amazon Basin; elsewhere, populations tend to be small and fragmented. The high number of jaguars results from the large total area still occupied (almost 9 million km2) and low human densities (< 1 person/km2) coinciding with high primary productivity in the core area of jaguar range. Our results show the importance of protected areas for jaguar persistence. We conclude that combining modelling of density and distribution can reveal ecological patterns and processes at global scales, can provide robust estimates for use in species assessments, and can guide broad-scale conservation actions.

A multispecies dependent double-observer model: A new method for estimating multispecies abundance.

Conservation of biological communities requires accurate estimates of abundance for multiple species. Recent advances in estimating abundance of multiple species, such as Bayesian multispecies N-mixture models, account for multiple sources of variation, including detection error. However, false-positive errors (misidentification or double counts), which are prevalent in multispecies data sets, remain largely unaddressed. The dependent-double observer (DDO) method is an emerging method that both accounts for detection error and is suggested to reduce the occurrence of false positives because it relies on two observers working collaboratively to identify individuals. To date, the DDO method has not been combined with advantages of multispecies N-mixture models. Here, we derive an extension of a multispecies N-mixture model using the DDO survey method to create a multispecies dependent double-observer abundance model (MDAM). The MDAM uses a hierarchical framework to account for biological and observational processes in a statistically consistent framework while using the accurate observation data from the DDO survey method. We demonstrate that the MDAM accurately estimates abundance of multiple species with simulated and real multispecies data sets. Simulations showed that the model provides both precise and accurate abundance estimates, with average credible interval coverage across 100 repeated simulations of 94.5% for abundance estimates and 92.5% for detection estimates. In addition, 92.2% of abundance estimates had a mean absolute percent error between 0% and 20%, with a mean of 7.7%. We present the MDAM as an important step forward in expanding the applicability of the DDO method to a multispecies setting. Previous implementation of the DDO method suggests the MDAM can be applied to a broad array of biological communities. We suggest that researchers interested in assessing biological communities consider the MDAM as a tool for deriving accurate, multispecies abundance estimates.

High fitness costs of climate change-induced camouflage mismatch.

Anthropogenic climate change has created myriad stressors that threaten to cause local extinctions if wild populations fail to adapt to novel conditions. We studied individual and population-level fitness costs of a climate change-induced stressor: camouflage mismatch in seasonally colour molting species confronting decreasing snow cover duration. Based on field measurements of radiocollared snowshoe hares, we found strong selection on coat colour molt phenology, such that animals mismatched with the colour of their background experienced weekly survival decreases up to 7%. In the absence of adaptive response, we show that these mortality costs would result in strong population-level declines by the end of the century. However, natural selection acting on wide individual variation in molt phenology might enable evolutionary adaptation to camouflage mismatch. We conclude that evolutionary rescue will be critical for hares and other colour molting species to keep up with climate change.