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Invasive snakes represent a serious threat to island biodiversity, being responsible for far-reaching impacts that are noticeably understudied, particularly regarding native reptiles. We analysed the impact of the invasive California kingsnake, Lampropeltis californiae—recently introduced in the Canary Islands—on the abundance of all endemic herpetofauna of the island of Gran Canaria. We quantified the density in invaded and uninvaded sites for the Gran Canaria giant lizard, Gallotia stehlini, the Gran Canaria skink, Chalcides sexlineatus, and Boettger’s wall gecko, Tarentola boettgeri. We used spatially explicit capture-recapture and distance-sampling methods for G. stehlini and active searches under rocks for the abundance of the other two reptiles. The abundance of all species was lower in invaded sites, with a reduction in the number of individuals greater than 90% for G. stehlini, greater than 80% for C. sexlineatus and greater than 50% for T. boettgeri in invaded sites. Our results illustrate the severe impact of L. californiae on the endemic herpetofauna of Gran Canaria and highlight the need for strengthened measures to manage this invasion. We also provide further evidence of the negative consequences of invasive snakes on island reptiles and emphasize the need for further research on this matter on islands worldwide.

A spatial open-population capture-recapture model is described that extends both the non-spatial open-population model of Schwarz and Arnason and the spatially explicit closed-population model of Borchers and Efford. The superpopulation of animals available for detection at some time during a study is conceived as a two-dimensional Poisson point process. Individual probabilities of birth and death follow the conventional open-population model. Movement between sampling times may be modeled with a dispersal kernel using a recursive Markovian algorithm. Observations arise from distance-dependent sampling at an array of detectors. As in the closed-population spatial model, the observed data likelihood relies on integration over the unknown animal locations; maximization of this likelihood yields estimates of the birth, death, movement, and detection parameters. The models were fitted to data from a live-trapping study of brushtail possums (Trichosurus vulpecula) in New Zealand. Simulations confirmed that spatial modeling can greatly reduce the bias of capture-recapture survival estimates and that there is a degree of robustness to misspecification of the dispersal kernel. An R package is available that includes various extensions.

1. Human activities have dramatic effects on the distribution and abundance of wildlife. Increased road densities and human presence in wilderness areas have elevated human-caused mortality of grizzly bears and reduced bears' use. Management agencies frequently attempt to reduce human-caused mortality by managing road density and thus human access, but the effectiveness of these actions is rarely assessed. 2. We combined systematic, DNA-based mark-recapture techniques with spatially explicit capture-recapture models to estimate population size of a threatened grizzly bear population (Kettle-Granby), following management actions to recover this population. We tested the effects of habitat and road density on grizzly bear population density. We tested both a linear and threshold-based road density metric and investigated the effect of current access management (closing roads to the public). 3. We documented an c. 50% increase in bear density since 1997 suggesting increased landscape and species conservation from management agencies played a significant role in that increase. However, bear density was lower where road denisities exceeded 0.6 km/km² and higher where motorised vehicle access had been restricted. The highest bear densities were in areas with large tracts of few or no roads and high habitat quality. Access management bolstered bear density in small areas by 27%. 4. Synthesis and applications. Our spatially explicit capture-recapture analysis demonstrates that population recovery is possible in a multi-use landscape when management actions target priority areas. We suggest that road density is a useful surrogate for the negative effects of human land use on grizzly bear populations, but spatial configuration of roads must still be considered. Reducing roads will increase grizzly bear density, but restricting vehicle access can also achieve this goal. We demonstrate that a policy target of reducing human access by managing road density below 0.6 km/km², while ensuring areas of high habitat quality have no roads, is a reasonable compromise between the need for road access and population recovery goals. Targeting closures to areas of highest habitat quality would benefit grizzly bear population recovery the most.

Local perceptions of grizzly bear (Ursus arctos) numbers in southwestern Alberta, Canada are incongruent with their threatened status. We used non-invasive genetic sampling to estimate grizzly bear density and abundance in southwestern Alberta. We established 899 bear rub objects (e.g., tree, power pole, fence post) for hair sample collection across the study area by surveying trail networks, using geographic information system layers, and working with >70 landowners to identify priority sampling areas. The study area included 2 management zones: the Recovery Zone where the objective was to recover the grizzly bear population, and a Support Zone intended to maintain those bears not exclusively within the Recovery Zone. We visited rub objects every 3 weeks from late May through early November for 8 visits (7 sampling occasions) per field season. We also allowed for opportunistically collected hair samples (e.g., trapped bears, hair at agricultural bear-conflict sites). We identified species, individual identity, and sex based on nuclear DNA extracted from hair follicles. From 2013 through 2014, we identified 164 individual grizzly bears. Using spatially explicit capture–recapture models (SECR), we estimated density in 2 ways. First, we estimated density for each sex and year separately (2013: M = 9.2/1,000 km2 in the Recovery Zone and 8.1/1,000 km2 in the Support Zone, F = 14.9/1,000 km2 in the Recovery Zone and 13.6/1,000 km2 in the Support Zone; 2014: M = 7.2/1,000 km2 in the Recovery Zone and 5.7/1,000 km2 in the Support Zone, F = 9.0/1,000 km2 in the Recovery Zone and 8.5/1,000 km2 in the Support Zone). Second, we did not allow density to vary across years and instead estimated a single density for the study area (M = 8.0/1,000 km2 in the Recovery Zone and 7.1/1,000 km2 in the Support Zone, F = 12.4/1,000 km2 in the Recovery Zone and 10.0/1,000 km2 in the Support Zone). Though yearly variation occurred, we derived from our density estimates an expected abundance of approximately 67.4 resident grizzly bears, indicating a 4% per year increase since a 2007 estimate of 51 bears. These SECR density estimates pertain only to bears with home ranges that were centered within the study area. Using traditional capture-mark-recapture (CMR) models with the same data yielded a higher estimate of bears because it included all bears that were using the study area (2013: F = 68.9, M = 102.6; 2014: F = 63.0, M = 108.6), including >50% of bears previously genotyped in Montana or British Columbia. In contrast with the SECR estimates, the CMR estimates represent the number of bears that southwestern Alberta residents could have encountered (i.e., the population of bears that had potential to have been involved in conflict). Shifts in grizzly bear distribution resulted in large changes in our SECR density estimates between years, whereas our estimate of the number of bears using the area remained constant. We recommend increased inter-jurisdictional monitoring and management of this international grizzly bear population.

Spatially explicit capture-recapture methods, used widely to estimate the abundance of large carnivores, allow for movement within home ranges during sampling. Probability of detection is a decreasing function of distance from the home range center, with one parameter for magnitude and another for spatial scale. Sex-based and other differences in home range size potentially cause heterogeneity in individual detection and bias in estimates of density. The two parameters of detection have hitherto been treated as independent, but we suggest that an inverse relation is expected when detection probability depends on time spent near the detector. Variation in the spatial scale of detection is then compensated by reciprocal variation in the magnitude parameter. We define a net measure of detection (\"single-detector sampling area,\" a 0 ), and show by simulation that its coefficient of variation (CV) is a better predictor of bias than the CV of either component or the sum of their squared CVs. In an example using the grizzly bear Ursus arctos , the estimated sex variation in a 0 was small despite large variation in each component. From the simulations, the relative bias of density estimates was generally negligible (<5%) when CV( a 0 ) < 30%. Parameterization of the detection model in terms of a 0 and spatial scale can be more parsimonious and significantly aids the biological interpretation of detection parameters.

Maintaining jaguar Panthera onca subpopulations throughout Mesoamerica is vital to range-wide jaguar conservation. Corcovado National Park in Costa Rica is critical habitat for the Osa Peninsula jaguar subpopulation. There is a debate regarding whether the jaguars in this National Park are in a state of crisis. To examine this, we implemented long-term camera-trap monitoring throughout Corcovado National Park during 2015–2021. Using a spatially explicit Jolly–Seber model we estimated jaguar populations and distribution throughout our study area. Additionally, we reran our model using a constrained study area to compare our findings with those of a previous study. Trends in jaguar abundance indices and population estimates during 2015–2021 indicate that jaguar abundance has increased over time. Our jaguar density estimates also fall within the range of jaguar densities reported for relatively stable populations elsewhere. Using the same study area as that of a prior study, jaguar densities also increased over the duration of our study and were mostly comparable to previous density estimates. Our results suggest that jaguars within Corcovado National Park may not be in a state of crisis. Rather, our findings provide further hope for the jaguars of the Osa Peninsula. They do not, however, diminish the importance of continued conservation efforts. These will remain critical both inside and outside Corcovado National Park, as threats appear to have persisted over time.

Large carnivores such as the jaguar Panthera onca are particularly susceptible to population decline and local extinction as a result of habitat loss. Here we report on the long-term monitoring of a local jaguar population in a mixed land-use area in the eastern lowlands of Bolivia from March 2017 to December 2019. We recorded 15 jaguar individuals and four reproduction events (five offspring from three females), suggesting that our study area harbours a resident breeding population. Seven iterations of spatially explicit capture–recapture models provided density estimates of 1.32–3.57 jaguars per 100 km2. Jaguar capture rates were highest in forested areas, with few to no jaguar captures in pastures used for livestock. Massive deforestation after the survey period reduced the proportion of dense forest cover by 33%, shrinking the availability of suitable jaguar habitat and placing the resident jaguar population at risk. We use the jaguar as an indicator species to highlight the threat of habitat destruction in the Chiquitano region and we emphasize the importance of intact forest patches for jaguar conservation.

The spotted hyaena Crocuta crocuta is relatively understudied across its range despite evidence of widespread declines. It is therefore essential that robust baseline population density assessments are conducted to inform current management and future conservation policy. In Mozambique this is urgent as decades of armed conflict followed by unchecked poaching have resulted in large-scale wildlife declines and extirpations. We conducted the first robust population density estimate for a spotted hyaena population in Mozambique using spatially explicit capture–recapture methodologies. We recorded a relatively low population density of 0.8–2.1 hyaenas/100 km2 in the wildlife management area Coutada 11 in the Zambezi Delta of central Mozambique in 2021. These densities are well below the estimated carrying capacity for the landscape and are comparable to published densities in high human-impact, miombo woodland-dominated and arid environments. The combination of historical armed conflict, marginal trophy hunting and bushmeat poaching using wire snares and gin traps (with physical injuries evident in 9% of identified individuals) presents persistent anthropogenic pressure, limiting the post-war recovery of this resident hyaena population. We provide insights into the dynamics of hyaena population status and recovery in such post-war landscapes, adding to mounting evidence that the species is less resilient to severe anthropogenic disturbances than previously believed. We recommend long-term monitoring of this and other carnivore populations in post-war landscapes to ascertain demographic trends and implement effective conservation interventions for population recovery.

Population density estimation is integral to the effective conservation and management of wildlife. The snow leopard Panthera uncia is categorized as Vulnerable on the IUCN Red List, and reliable information on its density is a prerequisite for its conservation and management. Little is known about the status of the snow leopard in the central and eastern Qilian Mountains, China. To address this, we estimated the population density of the snow leopard using a spatially explicit capture–recapture model based on camera trapping in Machang in the central and eastern Qilian Mountains during January–March 2019. We set up 40 camera traps and recorded 84 separate snow leopard captures over 3,024 trap-days. We identified 18 individual snow leopards and estimated their density to be 2.26/100 km2. Our study provides baseline information on the snow leopard and the first population estimate for the species in the central and eastern Qilian Mountains.

Developing high‐quality, non‐invasive population monitoring techniques is important for endangered species that are difficult to trap or susceptible to capture myopathy. One such species is the mala (Lagorchestes hirsutus, Central Australian subspecies), an Australian macropod that is extinct in the wild and only conserved inside four predator‐free reserves and on one offshore island. DNA extracted from scat is an effective non‐invasive method to ‘genetically tag’ individuals for capture‐mark‐recapture analyses to estimate population abundance. Microsatellites have previously been successfully used to identify individuals from mala scats, but are prone to genotyping errors, especially when DNA quality is low. Here, we developed an array of 50 highly informative Single Nucleotide Polymorphisms (SNPs) for targeted genotyping on the MassARRAY system to improve efficiency and confidence in individual identification. We compared the accuracy of individual identification using the SNP panel to eleven previously published microsatellite markers and compared estimates of mala abundance derived from each genotyping method. Mala scats were collected in 2020 and 2021 along nine transects within a predator‐free reserve at Matuwa Kurrara Kurrara National Park, Western Australia. The SNP panel had similar amplification success to microsatellites but reduced genotyping error rates. Both genotyping methods showed a similar number of individuals identified and similar patterns in genetic diversity and relatedness across two years. Spatially explicit capture‐recapture modelling using microsatellites produced an estimate of 108 (±37) mala in 2020 and 59 (±22) in 2021. Samples genotyped on the SNP panel produced an estimate of 122 (±45) individuals in 2020 and 81 (±32) in 2021. Both methods indicated a substantial decline in mala abundance from 2020 to 2021, which was likely a lag effect associated with a drought that occurred in 2019. We developed a species‐specific SNP panel for non‐invasive population monitoring of the mala (Lagorchestes hirsutus, Central Australian subspecies), an Australian macropod that is extinct in the wild, and conserved inside four predator‐free reserves and on one offshore island. We compared the accuracy of individual identification using a species‐specific SNP panel to eleven previously published microsatellite markers, and compared estimates of mala abundance and genetic diversity derived from each genotyping method. The SNP panel had similar amplification success to microsatellites, but substantially reduced error rates, and estimates of abundance were similar for both genotyping methods, both indicating a substantial decline in mala abundance from 2020 to 2021.