Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires a correct identification of kill sites, often achieved by ...field‐checking GPS location clusters (GLCs). However, there are potential sources of error included in kill‐site identification, such as failing to detect GLCs that are kill sites, and misclassifying the generated GLCs (e.g., kill for nonkill) that were not field checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx (Lynx lynx), an apex predator of conservation concern in Europe, in three multiprey systems, with different combinations of wild, semidomestic, and domestic prey. We first used a subsampling approach to investigate how different GPS‐fix schedules affected the detection of GLC‐indicated kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as nonkills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced from seven to three fixes per night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24 h decreased the probability of detecting GLCs connected with kill sites, particularly those of semidomestic or domestic prey, and small prey. Random forest successfully predicted between 73%–90% of ungulate kills, but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm's overall accuracy. We provide a set of recommendations for studies focusing on kill‐site detection that can be considered for other large carnivore species in addition to the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.
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•Detection variability should be controlled in modelling of species abundance.•Some relative abundance indices could be biased to detection variability.•Indices based on scats and ...captures /occasion are suboptimal for abundance studies.•Number of individuals could be used as reliable relative abundance index.•Small game and period variables explain detection variability in abundance models.
The correct interpretation of relative abundance indices provided by different sampling methods is essential to correctly estimate population size. Although multiple indices and models have been proposed, their ability to estimate relative abundances and their performance in models explaining abundance trends remains unclear. We used the red fox (Vulpes vulpes) as a model species to compare the relationship and derived models of relative abundance between three indices of relative abundance: RAI (number of captures/total occasions); NI (number of photo-identified individuals) obtained by camera-trapping, and NSE (number of segments with scats) obtained by the scat census sampling method. In addition, we modelled the relationship between a set of habitat predictors and fox relative abundance for each of the three estimated relative abundance indices. We compared the relative abundance models explained for each index against N-mixture models that estimate abundance controlled for variation in detection. Results showed a positive correlation between the RAI and NI indices, while both indices showed a negative relationship with the NSE index. Relative abundance models and N-Mixture models showed a different selection of predictors to explain abundance trends. NSE and RAI indices selected predictors that could explain variability in fox detection rather than fox abundance. In contrast, the NI index and N-Mixture models selected the same predictors to explain fox abundance. Our results suggest the use of the NI index for abundance models without the need to control for variation in detection. Relative abundance indices based on scats and captures per occasion are suboptimal indices for species abundance studies due to possible bias caused by animal behaviour. If count-based methods on captures per occasion (RAI) are selected, we suggest using session-based data processing to incorporate detectability variation in N-mixture models.
Individual‐level differences in animal spatial behavior can lead to differential exposure to risk. We assessed the risk‐exposure of a reintroduced population of kākā (Nestor meridionalis) in a fenced ...reserve in New Zealand by GPS tracking 10 individuals and comparing the proportion of each individual's home range beyond the reserve's fence in relation to age, sex, and fledging origin. To estimate dynamic space use, we used a sweeping window framework to estimate occurrence distributions (ODs) from temporally overlapping snapshots. For each OD, we calculated the proportion outside the reserve's fence to assess temporal risk exposure, and the area, centroid, and overlap to represent the behavioral pattern of space use. Home range area declined significantly and consistently with age, and the space use of juvenile kākā was more dynamic, particularly in relation to locational changes of space use. The wider‐ranging and more dynamic behavior of younger kākā resulted in more time spent outside the reserve, which aligned with a higher number of incidental mortality observations. Quantifying both home range and dynamic space use is an effective approach to assess risk exposure, which can provide guidance for management interventions. We also emphasize the dynamic space use approach, which is flexible and can provide insights into a species' spatial ecology.
We used home range and dynamic space use analyses to understand how the spatial behavior of kākā differs between individuals and groups, and how that leads to differential exposure to risk. In this population, younger kākā had much larger home ranges which were more dynamic, leading to potentially higher risk outside of a conservation reserve in New Zealand. The dynamic space use framework is a flexible approach to quantifying temporal variability in space use.
Endangered yellow-eyed penguins (
Megadyptes antipodes
) are central-place, benthic-diving foragers that search for prey in the productive marine areas off the coast of the South Island, New Zealand. ...Like other seabirds, they target specific, reliable areas of high prey abundance, which are often associated with oceanographic characteristics such as bathymetry, seafloor sediment type, and sea surface temperature. Employing GPS tracking data collected between 2003 and 2021, we created species distribution models using maximum entropy modelling (Maxent) to determine foraging space use and habitat suitability for yellow-eyed penguins across their entire South Island range and within five distinct subpopulations: Banks Peninsula, North Otago, Otago Peninsula, the Catlins, and Stewart Island. We quantified the importance of environmental variables for predicting foraging site selection during and outside the breeding season. Significant regional variation existed in predicted probability of penguin presence, and proximity to the nearest breeding area was a key predictor of suitable foraging habitat. When distance was not included in the models, dissolved oxygen concentration was the most important predictor in the overall South Island model and the North Otago, Otago Peninsula, and the Catlins subpopulation models, whereas water current speed and mean monthly turbidity were most important in Banks Peninsula and Stewart Island subpopulation models, respectively. Dynamic variables related to prey availability were often the most important variables in model predictions of the habitat selection of yellow-eyed penguins. Visualisations and findings from this study, particularly of the observed interactions between penguins and their marine habitat, can be used to direct conservation and resources during marine spatial planning and species management.
Studies aspects of the spatial ecology of introduced feral cats (Felis catus) on subantarctic Auckland Island in order to assist eradication efforts on the island. Estimates home range sizes and ...identifies core areas of activity based on movement-rooted dynamic Brownian bridge models. Uses resource selection functions with generalised linear mixed models to identify seasonal patterns of space use associated to topographic, vegetation and other landscape predictors. Quantifies cats' daily movement rates within home ranges. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.
Context
Large-scale programs for eradication of pest mammals are confronted with the challenge of managing reinvasion. Exploiting high-elevation landscape features that naturally limit the rate of ...reinvasion is a strategy that is presumed to improve the success of such initiatives, however, the efficacy of doing so has not yet been investigated.
Objectives
We aimed to assess whether high-elevation landforms limit the movements of 10 species of invasive small mammal in New Zealand to such a degree that they could be exploited in landscape-scale eradication programmes.
Methods
We determined the upper elevation limits of species’ distributions, and made spatial predictions based on occupancy models. We applied these in concert to a 310,000 ha area of rugged mountainous environments and identified landforms that function as dispersal barriers to each species of interest. We validated our predictions with existing presence/absence and GPS movement data, and tested our predictions of high-elevation landform barriers with the GPS movement data of a sample of European hedgehogs (
Erinaceus europaeus
).
Results
We found that the extent of barriers which limited movement ranged from widespread (5/10 species), to localised, (3/10 species) to limited (2/10 species). Our predictions of hedgehog movement barriers were strongly supported by GPS movement data of 26 hedgehogs that were tracked in the study area.
Conclusions
Our findings show there is enormous potential to advance landscape-scale eradication of invasive small mammals in areas adjacent to high-elevation landforms by identifying and exploiting landscape features that limit the movement of target species in the strategies of eradication programmes.
The ongoing recolonisations of human-transformed environments in Europe by large carnivores like the wolf
means that conservation conflicts could re-surface, among other reasons, due to predation on ...ungulate game species. We estimated the effect of wolves on ungulate species using data on wolf prey selection, kill rates and territory size to build a hypothetical case of future expansion. We extrapolated results on predation from the current wolf distribution in central Sweden and eastern Poland to the eventual wolf recolonisation of southern Sweden. We then calculated the proportion of five ungulate game species killed annually by wolves, and the ratio between the predicted annual predation by wolves given future colonization and the number of ungulates currently harvested by hunters. Results showed that wolf recolonization in southern Sweden would have a minor impact on the estimated population densities of red deer
, fallow deer
and wild boar
, but is likely to lead to a significant reduction in human captures of moose
and roe deer
. The current five-ungulate species system in southern Sweden suggests a potential for two to four times higher wolf density than the two-ungulate species system in the northern part of their current distribution. Management and conservation of recolonizing large carnivores require a better understanding of the observed impact on game populations under similar ecological conditions to ameliorate conservation conflicts and achieve a paradigm of coexistence. Integrating these predictions into management is paramount to the current rewilding trend occurring in many areas of Europe or North America.
Artificial feeding of wildlife is a widespread, but controversial, management practice with many positive and negative effects. Besides the effects on the target species, it can also affect other ...(non-target) species by modulating interspecific interactions. Previous research showed that the presence of artificial feeding sites for bears and ungulates increases the risk of kleptoparasitism by the brown bear (Ursus arctos) on kills made by Eurasian lynx (Lynx lynx). However, it remains unknown whether lynx adjust their behaviour to the distribution of artificial feeding sites, which also attract potential prey (ungulates) for lynx. Using GPS telemetry data, we explored the spatiotemporal lynx response to such sites and studied how this response varied with experience level, i.e. between adult resident individuals ('experienced lynx') and juveniles and/or translocated individuals ('naïve lynx'). We found that lynx experience played an important role in the use of artificial feeding sites. Specifically, while both experienced and naïve lynx selected feeding sites while moving within their home range, the attraction was stronger among the naïve lynx. Considering the distribution of kill sites, naïve lynx killed prey closer to the artificial feeding sites than expected, while experienced lynx avoided them. Finally, the proximity to artificial feeding sites by experienced lynx showed an annual variation, matching the seasonal kleptoparasitism risk, with overall closer proximity to feeding sites during the winter, when bears are less active, which is also when ungulates are more concentrated around feeding sites. Our study suggests that, despite the relatively recent introduction of artificial feeding in the ecosystems, wildlife can learn to respond to the altered interactions with other species. However, this appears to be a learning process with manifold management and conservation implications. A better understanding of species interactions and space use in the context of resource manipulation is increasingly relevant as the use of anthropogenic food by wildlife is nowadays substantial and widespread across most parts of the world.
Conservation of the endangered yellow-eyed penguin (Megadyptes antipodes) in New Zealand has principally sought to manage terrestrial threats, while relatively little has been done to understand or ...address marine threats, such as reduced prey availability, commercial fisheries interactions, and habitat destruction. We assessed spatial similarities between mainland yellow-eyed penguin marine distribution and resources (prey), risks (fisheries interactions), and areas of refugia (marine protected areas, MPAs). We determined if suitable penguin foraging habitat, based on environmental predictors using a Maxent species distribution model (SDM), also supports a high diversity of key prey species identified using a stacked SDM. We also created a novel index to predict areas of potential commercial fisheries interactions and mapped the overlap of penguin distribution and MPAs. Areas along the middle of the continental shelf had the highest prey diversity and probability of penguin presence, which overlapped with gillnet fisheries in these regions. Suitable penguin habitat also overlaps with trawl fisheries inshore along much of the South Island coast. <1 % of the penguin range overlaps with current MPAs, and the proposed South-East Marine Protected Areas network would protect only 3.6 % of the current penguin foraging distribution. This study takes an ecosystem approach to assess complex interactions between commercial fisheries, marine ecosystems, and MPAs, which is urgently required for marine spatial planning and adaptive ecosystem management of not only this endangered seabird but for southeast New Zealand coastal habitat and biota also.
Applying the most appropriate sampling method is essential for estimating population size. Sampling methods and techniques to estimate abundance may be limited by environmental characteristics, ...species traits, specific requirements of the techniques, or the economic resources to carry out the sampling. Thus, evaluating multiple sampling methods in monitoring populations is essential for establishing effective conservation strategies. In this study, we compare two of the most commonly used sampling methods with the red fox (
Vulpes vulpes
) as the type species. On the one hand, we compared the minimum number of individuals (NI) detected by camera trapping, identifying individuals by morphological characteristics with the minimum number of individuals detected by DNA faeces and a set of 16 microsatellites. On the other hand, we estimated abundance by performing an N-mixture model using information from camera-traps to study the relationship between abundance and the minimum number of individuals detected. Results showed that the minimum NI provided by camera trapping was slightly higher than that of DNA faecal genotyping, with 23.66 and 19 individuals, respectively. In addition, abundance and NI detected by camera trapping showed a positive relationship. In contrast, there was a non-significant negative relationship between NI detected by faecal DNA and abundance estimates. Our results suggest using the minimum number of photo-identified individuals as a reliable index to study variation in red fox abundance when other advanced methods cannot be implemented in the study of population size. However, it is necessary to improve the methods of faecal sampling to study the relationship with camera-trap data.
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