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Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
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An empirical evaluation of camera trap study design: how many, how long, and when?
1. Camera traps deployed in grids or stratified random designs are a well-established survey tool for wildlife but there has been little evaluation of study design parameters. 2. We used an empirical subsampling approach involving 2225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy, detection rate) for mammals. 3. We found that 25-35 camera locations were needed for precise estimates of species richness, depending on scale of the study. The precision of species-level estimates of occupancy was highly sensitive to occupancy level, with <20 camera sites needed for precise estimates of common (>0.75) species, but more than 150 sites likely needed for rare (<0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted for habitat variability within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3-4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37-50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4-5, and some species were completely absent in one season due to hibernation or migration. 4. We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3-5 weeks across 40-60 sites per array. We recommend comparisons of detection rates be model-based and include local covariates to help account for small-scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which had strong impacts on mammal communities in both tropical and temperate sites.
Data from: Accuracy of identifications of mammal species from camera trap images: a northern Australian case study
Camera traps are a powerful and increasingly popular tool for mammal research, but like all survey methods, they have limitations. Identifying animal species from images is a critical component of camera trap studies, yet while researchers recognize constraints with experimental design or camera technology, image misidentification is still not well understood. We evaluated the effects of a species' attributes (body mass and distinctiveness) and individual observer variables (experience and confidence) on the accuracy of mammal identifications from camera trap images. We conducted an Internet‐based survey containing 20 questions about observer experience and 60 camera trap images to identify. Images were sourced from surveys in northern Australia and included 25 species, ranging in body mass from the delicate mouse (Pseudomys delicatulus, 10 g) to the agile wallaby (Macropus agilis, >10 kg). There was a weak relationship between the accuracy of mammal identifications and observer experience. However, accuracy was highest (100%) for distinctive species (e.g. Short‐beaked echidna [Tachyglossus aculeatus]) and lowest (36%) for superficially non‐distinctive mammals (e.g. rodents like the Pale field‐rat [Rattus tunneyi]). There was a positive relationship between the accuracy of identifications and body mass. Participant confidence was highest for large and distinctive mammals, but was not related to participant experience level. Identifications made with greater confidence were more likely to be accurate. Unreliability in identifications of mammal species is a significant limitation to camera trap studies, particularly where small mammals are the focus, or where similar‐looking species co‐occur. Integration of camera traps with conventional survey techniques (e.g. live‐trapping), use of a reference library or computer‐automated programs are likely to aid positive identifications, while employing a confidence rating system and/or multiple observers may lead to a collection of more robust data. Although our study focussed on Australian species, our findings apply to camera trap studies globally.
Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community
Emerging conservation paradigms have shifted from single to multi-species approaches focused on sustaining biodiversity. Multi-species hierarchical occupancy modelling provides a method for assessing biodiversity while accounting for multiple sources of uncertainty. We analysed camera trapping data with multi-species models using a Bayesian approach to estimate the distributions of a terrestrial mammal community in northern Botswana and evaluate community, group, and species-specific responses to human disturbance and environmental variables. Groupings were based on two life-history traits: body size (small, medium, large and extra-large) and diet (carnivore, omnivore and herbivore). We photographed 44 species of mammals over 6607 trap nights. Camera station-specific estimates of species richness ranged from 8 to 27 unique species, and species had a mean occurrence probability of 0·32 (95% credible interval = 0·21–0·45). At the community level, our model revealed species richness was generally greatest in floodplains and grasslands and with increasing distances into protected wildlife areas. Variation among species' responses was explained in part by our species groupings. The positive influence of protected areas was strongest for extra-large species and herbivores, while medium-sized species actually increased in the non-protected areas. The positive effect of grassland/floodplain cover, alternatively, was strongest for large species and carnivores and weakest for small species and herbivores, suggesting herbivore diversity is promoted by habitat heterogeneity. Synthesis and applications. Our results highlight the importance of protected areas and grasslands in maintaining biodiversity in southern Africa. We demonstrate the utility of hierarchical Bayesian models for assessing community, group and individual species' responses to anthropogenic and environmental variables. This framework can be used to map areas of high conservation value and predict impacts of land-use change. Our approach is particularly applicable to the growing number of camera trap studies world-wide, and we suggest broader application globally will likely result in reduced costs, improved efficiency and increased knowledge of wildlife communities.
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
Data from: Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities
Camera trap surveys exclusively targeting features of the landscape that increase the probability of photographing one or several focal species are commonly used to draw inferences on the richness, composition and structure of entire mammal communities. However, these studies ignore expected biases in species detection arising from sampling only a limited set of potential habitat features. In this study, we test the influence of camera trap placement strategy on community-level inferences by carrying out two spatially and temporally concurrent surveys of medium to large terrestrial mammal species within Tanzania's Ruaha National Park, employing either strictly game trail-based or strictly random camera placements. We compared the richness, composition and structure of the two observed communities, and evaluated what makes a species significantly more likely to be caught at trail placements. Observed communities differed marginally in their richness and composition, although differences were more noticeable during the wet season and for low levels of sampling effort. Lognormal models provided the best fit to rank abundance distributions describing the structure of all observed communities, regardless of survey type or season. Despite this, carnivore species were more likely to be detected at trail placements relative to random ones during the dry season, as were larger bodied species during the wet season. Our findings suggest that, given adequate sampling effort (> 1400 camera trap nights), placement strategy is unlikely to affect inferences made at the community level. However, surveys should consider more carefully their choice of placement strategy when targeting specific taxonomic or trophic groups.
Data from: Revealing kleptoparasitic and predatory tendencies in an African mammal community using camera traps: a comparison of spatiotemporal approaches
Camera trap data are increasingly being used to characterise relationships between the spatiotemporal activity patterns of sympatric mammal species, often with a view to inferring inter-specific interactions. In this context, we attempted to characterise the kleptoparasitic and predatory tendencies of spotted hyaenas Crocuta crocuta and lions Panthera leo from photographic data collected across 54 camera trap stations and two dry seasons in Tanzania's Ruaha National Park. We applied four different methods of quantifying spatiotemporal associations, including one strictly temporal approach (activity pattern overlap), one strictly spatial approach (co-occupancy modelling), and two spatiotemporal approaches (co-detection modelling and temporal spacing at shared camera trap sites). We expected a kleptoparasitic relationship between spotted hyaenas and lions to result in a positive spatiotemporal association, and further hypothesised that the association between lions and their favourite prey in Ruaha, the giraffe Giraffa camelopardalis and the zebra Equus quagga, would be stronger than those observed with non-preferred prey species (the impala Aepyceros melampus and the dikdik Madoqua kirkii). Only approaches incorporating both the temporal and spatial components of camera trap data resulted in significant associative patterns. The latter were particularly sensitive to the temporal resolution chosen to define species detections (i.e. occasion length), and only revealed a significant positive association between lion on spotted hyaena detections, as well as a tendency for both species to follow each other at camera trap sites, during the dry season of 2013, but not that of 2014. In both seasons, observed spatiotemporal associations between lions and each of the four herbivore species considered provided no convincing or consistent indications of any predatory preferences. Our study suggests that, when making inferences on inter-specific interactions from camera trap data, due regards should be given to the potential behavioural and methodological processes underlying observed spatiotemporal patterns.
Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
Occupancy models are widely used in camera trap studies to analyze species presence, abundance, and geographic distribution, among other important ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and observed detection of a species. Under certain experimental setups, parameter estimation in a latent variable framework can be challenging. Several studies have issued guidelines on the number of independent replicated observations (surveys) needed for each unchanging occupancy field (season) to ensure reliable estimation. In this paper we present a spatio-temporal occupancy model, and show through a simulation study that it can be fit to data obtained from a \textit{single} survey per season, so long as the number of seasons is sufficiently large. We include an application using camera-trap data on the Thomson's gazelle in the Serengeti in Tanzania.
Diel niche of sympatric small mammals revealed by year-round camera trapping
<p>Data and scripts to reproduce results presented in the research article manuscript.</p>
A systematic review of global road ecology camera trap studies that monitored animals' use of wildlife crossings in road-fragmented landscapes
<p>Much research has emphasised the importance of incorporating wildlife crossing-structures in the design of road networks to facilitate connectivity of wildlife crossings in road-fragmented landscapes. Although camera traps have been effective in monitoring wildlife crossing structures, limited studies explore camera trap protocol to monitor wildlife use of crossing structures, particularly in Africa. Our study reviewed and assessed camera trap peer-reviewed research that monitored the use of crossing-structures by wildlife to navigate landscapes fragmented by roads. We found 70 camera trap peer-reviewed publications from 2001 to 2022 that monitored wildlife use of crossing-structures in landscapes intersected by roads, and these were from 22 countries and six continents. The included peer-reviewed studies varied significantly globally, with geographical trends indicating that most studies were conducted in North America. However, the methods used varied considerably between studies, especially in terms of camera trap placement protocol (placement height of camera trap, survey length, and camera multi-shot settings). This showed that camera trap usage for monitoring animal use of crossing structures is still an emerging area of research, and there is a potential for developing a standardised protocol for each type of crossing structure design and size. Future camera trap studies exploring wildlife use of crossing-structures should consider monitoring existing crossing structures (culverts, bridges, and tunnels) as this provides a less costly method of restoring landscape connectivity. We recommend that further research develop a standardised camera trap protocol for monitoring wildlife using crossing-structures to reduce the threats to biodiversity.</p>
SiMPL Wildlife Magnets: A camera trap tool for detecting all creatures great and small
<p>Small mammals compose a substantial portion of the seed predators and dispersers, as well as the prey, in many ecosystems. Nevertheless, information on distribution and habitat use is limited, partly because inexpensive, minimally -invasive surveying of small mammals has remained challenging. We created the SiMPL wildlife magnet – a baited camera trap design that allows passive monitoring of wildlife, especially small- to medium-sized mammals. The SiMPL magnet is inexpensive and easy to construct. To evaluate its effectiveness, we conducted a case study using 10 sites operating for two years along an elevation gradient in the White Mountains of the northeastern U.S. We found that the SiMPL wildlife magnet can be used to detect rodents, mesocarnivores, and, to a lesser extent, ungulates. We examined how the detection probability of mammal species changed with the inclusion of a SiMPL wildlife magnet using data from pre- and post-establishment. We found a significant increase in community-level detection probability with the use of SiMPL magnets and for some species, including red squirrels (<em>Tamiasciurus hudsonicus</em>), American marten (<em>Martes americana</em>), and fisher (<em>Pekania pennanti</em>). Moreover, we were able to capture more species with SiMPL magnets than without, including flying squirrels (<em>Glaucomys</em> spp.), various <em>Cricetidae</em> spp., black bears (<em>Ursus americanus</em>), moose (<em>Alces alces</em>), owls and other birds. The SiMPL wildlife magnet is an effective, low-cost method for surveying wildlife communities, especially rodents and mesocarnivores. It addresses the limited range view presented by other techniques for capturing small mammals on camera traps and enables efficient collection of phenology data, including vegetation and snowpack. This tool has several applications, including monitoring species' responses to silvicultural practices and global change.</p>
Leopard and spotted hyena camera trap dataset
<p><span>Human disturbance has the potential to alter competitive interactions, favoring species better able to adapt to areas used by humans. One such species is the spotted hyena (<em>Crocutu crocuta</em>), which has been successful in human dominated areas throughout Africa, competing through kleptoparasitism with other carnivore species (e.g., leopards [<em>Panthera pardus</em>]). In the Udzungwa Mountains, Tanzania, leopard density declines sharply close to human settlements and hyenas are their only competitors. Using camera trap data and a spatio-temporal occupancy model, we assessed the relative dominance of each species through spatial co-occurrence, altered activity patterns and temporary site avoidance. We tested the hypothesis that hyenas gain a competitive advantage over leopards in human-dominated areas due to their relatively higher tolerance for anthropogenic activities. We found that while hyena occupancy was best predicted by prey occupancy and not strongly affected by landscape factors associated with humans, leopards, </span><span>especially male leopards, were </span><span>less likely to be detected close to human settlements</span><span>. Female leopards, which are smaller than males, exhibited activity shifts and temporary site avoidance in response to hyenas, whereas hyenas shifted their activity patterns in response to male leopards. These results suggest that while hyenas may be behaviorally dominant over female leopards, they are subordinate to male leopards. We found that male leopards and hyenas were less </span><span>likely to co-occur closer to people, especially where prey was scarce, suggesting </span><span>subordinance of hyenas to male leopards may be mitigated by human disturbance</span><span>.</span><span> Furthermore, young male leopards shifted their activity patterns to be more diurnal in response to hyena presence, suggesting that dominance relationship between hyenas and leopards develops with age and is probably related to body size. These results indicate that human disturbance has the potential to affect the competitive relationship between leopards and hyenas in the Udzungwa mountains, but that relationships will vary with sex and body size.</span></p>
Camera-trapping records of birds and mammals visiting water-filled tree holes in the Calakmul region in southern Mexico
<p>Using camera-traps we documented that 21 bird and 9 mammal species visited water-filled tree holes (dendrotelmata) in the seasonal tropical forest of the Calakmul Biosphere Reserve, in southern Mexico. These species visited dendrotelmata primarily for foraging and drinking. The overall use of dendrotelmata was equally frequent between dry and rainy seasons but drinking behavior increased among birds during the dry season. This dataset includes information on the identity of visiting species, time and date of the visit, behavior of the visiting species, season (rainy/dry) in which the species was recorded, station (dendrotelma) in which the species was recorded, associated temperature and the number of individuals recorded in each visit.</p>
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
<p><a name="_Hlk58254629"></a></p> <p><a name="_Hlk58254629">Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (<i>Lepus timidus</i>) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km<sup>-2</sup> (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km<sup>-2 </sup>(95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km<sup>-2 </sup>(95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. </a>We discuss the merits and limitations of each method with respect to the estimation of population density in the field.</p>
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear. in Tragulidae
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear.
Determining the efficacy of camera traps, live capture traps, and detection dogs for locating cryptic small mammal species
<p>Metal box (e.g., Elliott, Sherman) traps and remote cameras are two of the most commonly employed methods presently used to survey terrestrial mammals. However, their relative efficacy at accurately detecting cryptic small mammals has not been adequately assessed. The present study therefore compared the effectiveness of metal box (Elliott) traps and vertically oriented, close range, white flash camera traps in detecting small mammals occurring in the Scenic Rim of eastern Australia. We also conducted a preliminary survey to determine effectiveness of a conservation detection dog (CDD) for identifying presence of a threatened carnivorous marsupial, <i>Antechinus arktos,</i> in present-day and historical locations, using camera traps to corroborate detections. 200 Elliott traps and 20 white flash camera traps were set for four deployments per method, across a site where the target small mammals, including <i>A. arktos</i>, are known to occur. Camera traps produced higher detection probabilities than Elliott traps for all four species. Thus, vertically mounted white flash cameras were preferable for detecting the presence of cryptic small mammals in our survey. The CDD, which had been trained to detect <i>A. arktos</i> scat, indicated in total 31 times when deployed in the field survey area, with subsequent camera trap deployments specifically corroborating <i>A. arktos</i> presence at 100% (3) indication locations. Importantly, the dog indicated twice within Border Ranges National Park, where historical (1980s-1990s) specimen-based records indicate the species was present, but extensive Elliott and camera trapping over the last 5-10 years have resulted in zero <i>A. arktos</i> captures. Camera traps subsequently corroborated <i>A. arktos</i> presence at these sites. This demonstrates that detection dogs can be a highly effective means of locating threatened, cryptic species, especially when traditional methods are unable to detect low-density mammal populations.</p>
Global camera trap synthesis highlights the importance of protected areas in maintaining mammal diversity
<p>The establishment of protected areas (PAs) is a central strategy for global biodiversity conservation. While the role of PAs in protecting habitat has been highlighted, their effectiveness at protecting mammal communities remains unclear. We analyzed a global dataset from over 8,671 camera traps in 23 countries on four continents that detected 321 medium- to large-bodied mammal species. We found a strong positive correlation between mammal taxonomic diversity and the proportion of a surveyed area covered by PAs at a global scale (b = 0.39, 95% CI = 0.19, 0.60) and in Indomalaya (b = 0.69, 95% CI = 0.19,1.2), as well as between functional diversity and PA coverage in the Nearctic (b = 0.47, 95% CI = 0.09, 0.85), after controlling for human disturbances and environmental variation. Functional diversity was only weakly (and insignificantly) correlated with PA coverage at the global scale (b =0.22, 95% CI = -0.02, 0.46), pointing to a need to better understand the functional response of mammal communities to protection. Our study provides important evidence of the global effectiveness of PAs in conserving terrestrial mammals and emphasizes the critical role of area-based conservation in a post-2020 biodiversity framework.</p>
Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities
<p>The use of camera traps in ecology helps affordably address questions about the distribution and density of cryptic and mobile species. The Random encounter model (REM) is a camera-trap method that has been developed to estimate population densities using unmarked individuals. However, few studies have evaluated its reliability in the field, especially considering that this method relies on parameters obtained from collared animals (<i>i.e.</i> average speed, in km/h), which can be difficult to acquire at low cost and effort. Our objectives were to (1) assess the reliability of this camera-trap method and (2) evaluate the influence of parameters coming from different populations on density estimates. We estimated a reference density of black bears (<i>Ursus americanus</i>) in Forillon National Park (Québec, Canada) using a spatial capture-recapture estimator based on hair-snag stations. We calculated average speed using telemetry data acquired from four different bear populations located outside our study area and estimated densities using the REM. The reference density, determined with a Bayesian spatial capture-recapture model, was 2.87 individuals/10km<sup>2</sup> [95% CI: 2.41–3.45], which was slightly lower (although not significatively different) than the different densities estimated using REM (ranging from 4.06–5.38 bears/10km<sup>2 </sup>depending on the average speed value used). Average speed values obtained from different populations had minor impacts on REM estimates when the difference in average speed between populations was low. Bias in speed values for slow-moving species had more influence on REM density estimates than for fast-moving species. We pointed out that a potential overestimation of density occurs when average speed is underestimated, i.e. using GPS telemetry locations with large fix-rate intervals. Our study suggests that REM could be an affordable alternative to conventional spatial capture-recapture, but highlights the need for further research to control for potential bias associated with speed values determined using GPS telemetry data.</p>
Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks
<p>Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large-scale regional biodiversity monitoring programs. The use of camera-trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co-occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera-trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (<i>Neotamias</i>) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key, and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote camera data with live-trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera-trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera-trap workflow.</p>
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
<p>Estimating habitat and spatial associations for wildlife is common across ecological studies, and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inferences in an effort to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eularian) data for seven species collected simultaneously in eastern Washington (2018 – 2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differ depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, though specific habitat relationships differed. We hypothesized the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.