Leveraging Open-Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models.

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large-scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community-based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large-scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri-urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM-enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%-100%). The final output, the OSM-enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM-derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open-source geographic databases and apply it to spatial modeling by generating research-specific landscape variables. As our occupancy results show, using context-specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations.

Metagenome-wide characterization of shared antimicrobial resistance genes in sympatric people and lemurs in rural Madagascar.

Background: Tracking the spread of antibiotic resistant bacteria is critical to reduce global morbidity and mortality associated with human and animal infections. There is a need to understand the role that wild animals in maintenance and transfer of antibiotic resistance genes (ARGs). Methods: This study used metagenomics to identify and compare the abundance of bacterial species and ARGs detected in the gut microbiomes from sympatric humans and wild mouse lemurs in a forest-dominated, roadless region of Madagascar near Ranomafana National Park. We examined the contribution of human geographic location toward differences in ARG abundance and compared the genomic similarity of ARGs between host source microbiomes. Results: Alpha and beta diversity of species and ARGs between host sources were distinct but maintained a similar number of detectable ARG alleles. Humans were differentially more abundant for four distinct tetracycline resistance-associated genes compared to lemurs. There was no significant difference in human ARG diversity from different locations. Human and lemur microbiomes shared 14 distinct ARGs with highly conserved in nucleotide identity. Synteny of ARG-associated assemblies revealed a distinct multidrug-resistant gene cassette carrying dfrA1 and aadA1 present in human and lemur microbiomes without evidence of geographic overlap, suggesting that these resistance genes could be widespread in this ecosystem. Further investigation into intermediary processes that maintain drug-resistant bacteria in wildlife settings is needed.

Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer-assisted pattern-identification algorithm.

Individual recognition of animals is an important aspect of ecological sciences. Photograph-based individual recognition options are of particular importance since these represent a non-invasive method to distinguish and identify individual animals. Recent developments and improvements in computer-based approaches make possible a faster semi-automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers (Castor fiber) collected as part of the Norwegian Beaver Project (NBP). Images were taken using a single-lens reflex camera and the pattern of scales on the tail, similar to a human fingerprint, was extracted using freely accessible image processing programs. The focus for individual recognition was not on the shape or the scarring of the tail, but purely on the individual scale pattern on the upper (dorsal) surface of the tail. The images were taken from two different heights above ground, and the largest possible area of the tail was extracted. The available data set was split in a ratio of 80% for training and 20% for testing. Overall, our study achieved an accuracy of 95.7%. We show that it is possible to distinguish individual beavers from their tail scale pattern images using the SIFT algorithm.

The rise of hyperabundant native generalists threatens both humans and nature.

In many disturbed terrestrial landscapes, a subset of native generalist vertebrates thrives. The population trends of these disturbance-tolerant species may be driven by multiple factors, including habitat preferences, foraging opportunities (including crop raiding or human refuse), lower mortality when their predators are persecuted (the 'human shield' effect) and reduced competition due to declines of disturbance-sensitive species. A pronounced elevation in the abundance of disturbance-tolerant wildlife can drive numerous cascading impacts on food webs, biodiversity, vegetation structure and people in coupled human-natural systems. There is also concern for increased risk of zoonotic disease transfer to humans and domestic animals from wildlife species with high pathogen loads as their abundance and proximity to humans increases. Here we use field data from 58 landscapes to document a supra-regional phenomenon of the hyperabundance and community dominance of Southeast Asian wild pigs and macaques. These two groups were chosen as prime candidates capable of reaching hyperabundance as they are edge adapted, with gregarious social structure, omnivorous diets, rapid reproduction and high tolerance to human proximity. Compared to intact interior forests, population densities in degraded forests were 148% and 87% higher for wild boar and macaques, respectively. In landscapes with >60% oil palm coverage, wild boar and pig-tailed macaque estimated abundances were 337% and 447% higher than landscapes with <1% oil palm coverage, respectively, suggesting marked demographic benefits accrued by crop raiding on calorie-rich food subsidies. There was extreme community dominance in forest landscapes with >20% oil palm cover where two pig and two macaque species accounted for >80% of independent camera trap detections, leaving <20% for the other 85 mammal species >1 kg considered. Establishing the population trends of pigs and macaques is imperative since they are linked to cascading impacts on the fauna and flora of local forest ecosystems, disease and human health, and economics (i.e., crop losses). The severity of potential negative cascading effects may motivate control efforts to achieve ecosystem integrity, human health and conservation objectives. Our review concludes that the rise of native generalists can be mediated by specific types of degradation, which influences the ecology and conservation of natural areas, creating both positive and detrimental impacts on intact ecosystems and human society.

Density-habitat relationships of white-tailed deer (Odocoileus virginianus) in Finland.

In heterogeneous landscapes, resource selection constitutes a crucial link between landscape and population-level processes such as density. We conducted a non-invasive genetic study of white-tailed deer in southern Finland in 2016 and 2017 using fecal DNA samples to understand factors influencing white-tailed deer density and space use in late summer prior to the hunting season. We estimated deer density as a function of landcover types using a spatial capture-recapture (SCR) model with individual identities established using microsatellite markers. The study revealed second-order habitat selection with highest deer densities in fields and mixed forest, and third-order habitat selection (detection probability) for transitional woodlands (clear-cuts) and closeness to fields. Including landscape heterogeneity improved model fit and increased inferred total density compared with models assuming a homogenous landscape. Our findings underline the importance of including habitat covariates when estimating density and exemplifies that resource selection can be studied using non-invasive methods.

Giant panda age recognition based on a facial image deep learning system.

The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.

An approach to rapid processing of camera trap images with minimal human input.

Camera traps have become an extensively utilized tool in ecological research, but the manual processing of images created by a network of camera traps rapidly becomes an overwhelming task, even for small camera trap studies.We used transfer learning to create convolutional neural network (CNN) models for identification and classification. By utilizing a small dataset with an average of 275 labeled images per species class, the model was able to distinguish between species and remove false triggers.We trained the model to detect 17 object classes with individual species identification, reaching an accuracy up to 92% and an average F1 score of 85%. Previous studies have suggested the need for thousands of images of each object class to reach results comparable to those achieved by human observers; however, we show that such accuracy can be achieved with fewer images.With transfer learning and an ongoing camera trap study, a deep learning model can be successfully created by a small camera trap study. A generalizable model produced from an unbalanced class set can be utilized to extract trap events that can later be confirmed by human processors.

Automated location invariant animal detection in camera trap images using publicly available data sources.

A time-consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain-specific applications.We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image-sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out-of-sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%-88.59%) than that achieved by the models trained only on camera trap datasets (38.5%-66.74%). Infusion further improved mAP by 1.78%-32.08%.Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out-of-the-box software. Models can be further optimized by infusion of 5%-10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.

U-Infuse: Democratization of Customizable Deep Learning for Object Detection.

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

Review: COVID-19 highlights the importance of camera traps for wildlife conservation research and management.

COVID-19 has altered many aspects of everyday life. For the scientific community, the pandemic has called upon investigators to continue work in novel ways, curtailing field and lab research. However, this unprecedented situation also offers an opportunity for researchers to optimize and further develop available field methods. Camera traps are one example of a tool used in science to answer questions about wildlife ecology, conservation, and management. Camera traps have long battery lives, lasting more than a year in certain cases, and photo storage capacity, with some models capable of wirelessly transmitting images from the field. This allows researchers to deploy cameras without having to check them for up to a year or more, making them an ideal field research tool during restrictions on in-person research activities such as COVID-19 lockdowns. As technological advances allow cameras to collect increasingly greater numbers of photos and videos, the analysis techniques for large amounts of data are evolving. Here, we describe the most common research questions suitable for camera trap studies and their importance for biodiversity conservation. As COVID-19 continues to affect how people interact with the natural environment, we discuss novel questions for which camera traps can provide insights on. We conclude by summarizing the results of a systematic review of camera trap studies, providing data on target taxa, geographic distribution, publication rate, and publication venues to help researchers planning to use camera traps in response to the current changes in human activity.

Urban wildlife in times of COVID-19: What can we infer from novel carnivore records in urban areas?

The onset of the COVID-19 pandemic brought an unusual decrease in human activity associated with partial and total lockdowns. Simultaneously, a series of wildlife sightings-mainly in urban areas-have been brought to public attention and often attributed to lockdown measures. Here we report on a series of wild carnivore records, including threatened species, obtained through camera traps set in urban forests, campuses, suburbs, and peri-urban areas of two cities in Chile, during partial lockdown measures. Our records are novel for Chile, a country where urban carnivore ecology is mostly unknown, and include the detection of four native carnivores, including the vulnerable güiña (Leopardus guigna) and the endangered southern river otter (Lontra provocax). These records also constitute a valuable baseline collected during partial lockdown measures in two cities of the Global South. We emphasize, however, that these findings cannot be used to argue for or against an effect of lockdown measures on wildlife. More generally, we call for caution in the interpretation of seemingly novel carnivore records during periods of lockdown and stress the value of international collaboration in evaluating the effects of the Anthropause on wildlife.

Response of Bolivian gray titi monkeys (Plecturocebus donacophilus) to an anthropogenic noise gradient: behavioral and hormonal correlates.

Worldwide urban expansion and deforestation have caused a rapid decline of non-human primates in recent decades. Yet, little is known to what extent these animals can tolerate anthropogenic noise arising from roadway traffic and human presence in their habitat. We studied six family groups of titis residing at increasing distances from a busy highway, in a park promoting ecotourism near Santa Cruz de la Sierra, Bolivia. We mapped group movements, sampled the titis' behavior, collected fecal samples from each study group and conducted experiments in which we used a mannequin simulating a human intrusion in their home range. We hypothesized that groups of titi monkeys exposed to higher levels of anthropogenic noise and human presence would react weakly to the mannequin and show higher concentrations of fecal cortisol compared with groups in least perturbed areas. Sound pressure measurements and systematic monitoring of soundscape inside the titis' home ranges confirmed the presence of a noise gradient, best characterized by the root-mean-square (RMS) and median amplitude (M) acoustic indices; importantly, both anthropogenic noise and human presence co-varied. Study groups resided in small, overlapping home ranges and they spent most of their time resting and preferentially used the lower forest stratum for traveling and the higher levels for foraging. Focal sampling analysis revealed that the time spent moving by adult pairs was inversely correlated with noise, the behavioral change occurring within a gradient of minimum sound pressures ranging from 44 dB(A) to 52 dB(A). Validated enzyme-immunoassays of fecal samples however detected surprisingly low cortisol concentrations, unrelated to the changes observed in the RMS and M indices. Finally, titis' response to the mannequin varied according to our expectation, with alarm calling being greater in distant groups relative to highway. Our study thus indicates reduced alarm calling through habituation to human presence and suggests a titis' resilience to anthropogenic noise with little evidence of physiological stress.

Evidence for the continued use of river dolphin oil for bait fishing and traditional medicine: implications for conservation.

Populations of the Ganges river dolphin (Platanista gangetica gangetica) are endangered, with ~3500 individuals estimated worldwide. Threats to this precarious population is exacerbated by accidental entanglement and illegal hunting for oil, which is used in bait fisheries and traditional medicine. Alternatives to dolphin oil have been proposed and extensively promoted in India, to curb the immediate threat to dolphin populations. However, it is not known whether dolphins are still being poached for oil, despite the proposal of aforementioned alternatives. Herein, a molecular protocol to monitor the presence of Dolphin DNA, using species identification of DNA extracted from bait oils obtained from fishermen is presented. This is coupled with information from social surveys to understand the current status of use of dolphin oil. Results indicate that molecular tools provide an accurate technique for detecting the presence of dolphin DNA, and can be used by enforcement agencies to monitor and identify points of threat to dolphins. Social survey results indicate the preference of fishermen to continue the use of dolphin oil for bait, despite knowing the legal implications. It is found that alternate oils do not provide an effective solution to curb dolphin oil use, and only shifts the threats of endangerment from one species to another, in the long run. The ban of bait fishing, effective enforcement combined with monitoring through molecular tools, continued community engagement and livelihood skill development are the most viable solutions for a holistic conservation approach.

Loss of Coilia nasus habitats in Chinese freshwater lakes: An otolith microchemistry assessment.

Loss of valued diadromous fishes and their habitats is one of the most critical problems in aquatic habitat connection and resource management worldwide. In China, the Poyang, Dongting, Gaobao, Gucheng, Dongping, and Taihu lakes were known to be historical migratory spawning sites of the anadromous estuarine tapertail anchovy Coilia nasus. However, except for Poyang Lake, it is believed that these lakes are no longer used by anadromous fish owing to overfishing, water pollution, and loss of connectivity. To confirm this assumption, we used an electron probe microanalyzer to analyze elemental strontium (Sr) and calcium (Ca) microchemical patterns in the otoliths of C. nasus individuals sampled from these lakes, in accordance with our previous analysis of the otolithic patterns of the same species sampled from habitat areas characterized by different salinity gradients. The results of line transect analysis of Sr/Ca ratios and Sr X-ray intensity maps of the otoliths indicated that all individuals from Dongting, Gaobao, Gucheng, Taihu, and Dongping lakes were characterized by a freshwater-resident life history. In contrast, individuals from Poyang Lake exhibited both freshwater-resident and anadromous life histories. The findings of this pilot study suggest that anadromous C. nasus can be found in Poyang Lake but are unlikely to be found in Dongting, Gaobao, Gucheng, Dongping, or Taihu lakes, despite these lakes being historical distribution areas or even spawning sites. This anchovy can possibly be used as a good model species for understanding the aforementioned global problem. Given that C. nasus is a commercially important species, restoration of its natural habitats and maintenance of their connections are recommended for its management and conservation.

Habitat variations affect morphological, reproductive and some metabolic traits of Mediterranean Centaurea glomerata Vahl populations.

Centaurea glomerata Vahl is an annual, monoecious and herbaceous member of Asteraceae, found in some localities of different topographic features/habitat conditions along the Mediterranean coastal region of Egypt. This study aimed to investigate some environmental gradients including edaphic and climate criteria on morphological, reproductive traits as well as phenolic and flavonoid metabolites in this species. Three distinct populations were selected. Two of them were located in coastal sand dunes (found in Rosetta region in Egypt); one was located on flat sand dunes, whereas the other grown on sloping ones. Meanwhile, the third population was represented in the rocky hillside of Burg El Arab region. The population detected in the sloping sand dunes showed best morphological and reproductive features, whilst the opposite was true for that represented on the rocky hillside. Moreover, the free phenolic and flavonoid compounds prevailed in the later. The meteorological data revealed that the rocky hillside received relatively lower minimum temperature and higher solar irradiance, while the sand dunes of Rosetta showed more warmer conditions. Light intensity and wind speed were reduced on the sloping sand dunes. The Canonical Correspondence Analysis (CCA) exhibited a clear correlation between most of metabolites detected and the population found on the rocky hillside along with higher solar irradiance prevails. The morpho-reproductive traits were related to climatic gradients and some soil criteria. These results revealed that the changes in micro-topography, that may lead to change in soil and climate variables, is the most important environmental gradient that controls the morphological and biochemical features of C. glomerata. Solar irradiance and/or light intensity are key factors playing a role influencing the measured traits of this species. These findings suggest that accumulation of secondary metabolites could be a biochemical strategy and an adaptational criterion for such species under stress conditions.

Trends in the occurrence of large Whooping Crane groups during migration in the great plains, USA.

Recent detections of large gatherings of Whooping Cranes suggest that flock sizes may be increasing at some stopover locations during both the spring and fall migrations. We used the public sightings database managed by the US Fish and Wildlife Service from 1942 to 2018 to analyze data for long-term trends in group size. We then examined the spatial distribution of large groups to explore potential explanations for these occurrences. The proportion of Whooping Crane groups comprised of 2, 3, and 4-6 individuals showed no trend over time. However, observations of individuals showed a declining trend and groups of 7-9 and ≥10 showed an increasing trend. The frequency of groups observed exceeding 5 and 10 individuals were better predicted by survey year than by Whooping Crane population size suggesting that an increasing population is not the sole driver of large group occurrences. Our results indicate that large groups occur disproportionately within the 50% migration corridor, at staging areas within the first or last 20-30% of the migration path, and near conservation-managed wetlands, particularly within the southern Great Plains. Our results suggest that in addition to population growth, conspecific attraction, location within the migration corridor, and habitat loss may be contributing to large group occurrences. Further research is needed to determine the degree to which these factors influence large Whooping Crane group formation. The gathering of large numbers of Whooping Cranes in a single location presents potential tradeoffs for the species. While increasing group sizes may improve threat detection and avoidance, it comes at a cost of increased disease and mass mortality risk.

Explosive exhalations by common bottlenose dolphins during Karenia brevis red tides.

Harmful algal blooms (HABs) such as those produced by Karenia brevis have acute negative impacts on common bottlenose dolphins (Tursiops truncatus) in Florida coastal waters, frequently causing illness and death. However, much less is known about chronic, sub-acute effects on these important sentinel species. This study investigates whether bottlenose dolphin behavior in Sarasota Bay, Florida is influenced by the presence of severe red tide events, focusing on respiratory and other behaviors likely affected by abundant toxin aerosols produced during these blooms. Through focal animal behavioral follows, we observed free-ranging dolphin respiratory behavior, activity budgets, and movement patterns relative to K. brevis abundance in the study area. We compared behavior from dolphins observed during a 2005 K. brevis bloom to those observed during inter-bloom conditions where K. brevis was present at background concentrations. We found that the rate of "chuffing", an explosive type of exhalation, was significantly greater in dolphins observed during the bloom. No apparent effect on respiratory rate, heading change rate or activity budgets was observed. We propose that this chuffing behavior is analogous to symptoms of respiratory irritation observed in humans exposed to such red tide events, and suggest that this may be a type of disturbance response. With an observed increase in both the frequency and severity of HABs, such disturbance responses may have large-scale chronic impacts to the health and fitness of bottlenose dolphins in regions where such HABs are common.

The ecology of suburban juvenile European hedgehogs (Erinaceus europaeus) in Denmark.

European hedgehog (Erinaceus europaeus) populations are widespread across diverse habitats but are declining in Western Europe. Drastic declines have been described in the UK, with the most severe declines occurring in rural areas. Hedgehogs are widely distributed in Denmark, but their status remains unknown.Fieldwork on hedgehogs has tended to focus on rural areas, leaving their ecology in suburban habitats largely unexplored, with clear implications for conservation initiatives. Here, we study the ecology of 35 juvenile hedgehogs using radio tracking during their first year of life in the suburbs of western Copenhagen.We use radio-tracking data to estimate (a) home range sizes in autumn and spring/summer, (b) survival during their first year of life, (c) the body mass changes before, during, and after hibernation, and (d) the hibernation behavior of the juvenile hedgehogs.We show that males and females have small home ranges compared with previous studies. The 95% MCP home range sizes in autumn were 1.33 ha (95% CI = 0.88-2.00) for males and 1.40 ha (95% CI = 0.84-2.32) for females; for spring/summer they were 6.54 ha (95% CI = 3.76-11.38) for males and 1.51 ha (95% CI = 0.63-3.63) for females. The juvenile survival probabilities during the study period from September 2014 to July 2015 were .56 for females and .79 for males. All healthy individuals gained body mass during the autumn and survived hibernation with little body mass loss thus demonstrating that the juveniles in the study were capable of gaining sufficient weight in the wild to survive their first hibernation.The climate is changing, but there is a lack of knowledge on how this affects mammal ecology. The exceptionally mild autumn of 2014 caused the juvenile hedgehogs to delay hibernation for up to a month compared with previous studies in Denmark.

The relationship between reported domestic canine parvovirus cases and wild canid distribution.

Canine parvovirus (CPV) is an important and often fatal pathogen of domestic dogs. It is resistant in the environment and cross-species transmission has been indicated in some canid populations, but never in Australia. The aim of this study was to determine if an association exists between 1. reported CPV cases in domestic dogs, and 2. the wild canid distribution in New South Wales (NSW), Australia. Reported CPV cases, and reports of the presence of wild dogs and the red fox (Vulpes vulpes), were extracted from a voluntary surveillance database and a voluntary pest reporting system, respectively. A total of 1,984 CPV cases in domestic dogs, and 3,593 fox and 3,075 wild dog sightings were reported between 2011 and 2016. Postcodes in which CPV cases were reported were significantly (P = 0.0002) more likely to report wild dogs (odds ratio 2.07, 95% CI 1.41-3.03). Overall, CPV cases were significantly (P < 0.05) correlated with both fox reports (rSP 0.225) and wild dog reports (rSP 0.247). The strength of association varied by geographical region and year; the strongest correlations were found in the mid-North Coast region (rSP 0.607 for wild dogs) and in 2016 (rSP 0.481 for foxes). Further serological and virological testing is required to confirm the apparent and plausible association between domestic CPV cases and wild canid distribution found in this study.