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36 results for “camera-trap”
Locations of black bear (Ursus americanus) reproduction in Nevada from camera-trap data
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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>
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.
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>
Figure 1 in Egg predation and vertebrates associated with wild crocodilian nests in Mexico determined using camera-traps
Figure 1. Geographical location of the study areas and photographic records of eggs predation. Procyon lotor (a, e, f), Didelphis virginiana (b), Cuniculus paca (c), Nasua narica (d, g), and Caracara cheriway (h).
Figure 2 in Egg predation and vertebrates associated with wild crocodilian nests in Mexico determined using camera-traps
Figure 2. Non-linear regression models: (a)- Predator species increase with the number of vertebrates recorded in the areas of study. (b)- The number of nests lost decreases as crocodilian size increases.
Data from: The challenges of recognising individuals with few distinguishing features: identifying red foxes Vulpes vulpes from camera-trap photos
Over the last two decades, camera traps have revolutionised the ability of biologists to undertake faunal surveys and estimate population densities, although identifying individuals of species with subtle markings remains challenging. We conducted a two-year camera-trapping study as part of a long-term study of urban foxes: our objectives were to determine whether red foxes could be identified individually from camera-trap photos, and highlight camera-trapping protocols and techniques to facilitate photo identification of species with few or subtle natural markings. We collected circa 800,000 camera-trap photos over 4945 camera days in suburban gardens in the city of Bristol, UK: 152,134 (19 %) included foxes, of which 13,888 (9 %) contained more than one fox. These provided 174,063 timestamped capture records of individual foxes; 170,923 were of foxes ≥ 3 months old. Younger foxes were excluded because they have few distinguishing features. We identified the individual (192 different foxes: 110 males, 49 females, 33 of unknown sex) in 168,417 (99 %) of these capture records; the remainder could not be identified due to poor image quality or because key identifying feature(s) were not visible. We show that carefully designed survey techniques facilitate individual identification of subtly-marked species. Accuracy is enhanced by camera-trapping techniques that yield large numbers of high resolution, colour images from multiple angles taken under varying environmental conditions. While identifying foxes manually was labour-intensive, currently available automated identification systems are unlikely to achieve the same levels of accuracy, especially since different features were used to identify each fox, the features were often inconspicuous, and their appearance varied with environmental conditions. We discuss how studies based on low numbers of photos, or which fail to identify the individual in a significant proportion of photos, risk losing important biological information, and may come to erroneous conclusions.
Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities
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Data from: The challenges of recognising individuals with few distinguishing features: identifying red foxes Vulpes vulpes from camera-trap photos
Open the record for dataset details and reuse information.
Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks
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Camera-trapping records of birds and mammals visiting water-filled tree holes in the Calakmul region in southern Mexico
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Camera-traps and the city: spatiotemporal adaptations of wildlife to urban environments
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Fig. 1 in Assessing large mammal and bird richness from camera-trap records in the Hukaung Valley of Northern Myanmar
Fig. 1. Location of Hukaung Valley Wildlife Sanctuary and Core study area (hatched) in Northern Myanmar.
Tiwi Island cat density camera-trap data 2017 and 2018
<p>This data was collected as part of the National Environmental Science Program's Threatened Species Recovery Hub (Project 1.1.12 - Mitigating cat impacts on the brush-tailed rabbit-rat). This dataset includes all detections of feral cats recorded on large grids of camera-traps deployed at four locations on the Tiwi Islands. Each of these grids consisted of 70 camera-traps, deployed in 14 rows of five cameras, with each camera spaced ~500 m apart. Camera-traps remained continuously recording for eight weeks. The location of each camera-trap is also provided.</p>
Tiwi Island cat density camera-trap data 2017 and 2018
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