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Remote Sensing
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Camera Trapping for Animal Ecology and Conservation
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Camera Trapping for Animal Ecology and Conservation

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 7352

Special Issue Editors

Dr. Hongbo Yang

E-Mail Website
Guest Editor
Global Development Policy Center, Boston University, Boston, MA 02215, USA
Interests: landscape ecology; wildlife habitat; protected areas; coupled human and natural systems; conservation policy
Dr. Jindong Zhang

E-Mail Website
Guest Editor
Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong 637009, China
Interests: wildlife ecology and management; conservation biology; animal behavior; sustainability in protected areas; coupled human and natural systems
Dr. Fang Wang

E-Mail Website
Guest Editor
School of Life Sciences, Fudan University, 220 Handan Rd., Yangpu District, Shanghai, China
Interests: biodiversity; conservation planning; population ecology; landscape ecology; camera trapping; habitat modeling; inter-species interaction
Dr. Thomas Connor

E-Mail Website
Guest Editor
Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA
Interests: spatial ecology; landscape genetics; species distribution modeling; conservation biology; population ecology

Special Issue Information

Dear Colleagues,

We are glad to launch a new Special Issue for the journal Remote Sensing titled “Camera Trapping for Animal Ecology and Conservation”. Camera traps (i.e., cameras that are remotely activated via an active or passive sensor) offer a reliable, inexpensive, and informative means of surveying animals in the wild. As a relatively new remote sensing technique, camera traps have advanced significantly in the last two decades, providing a wealth of data that have helped transform animal ecology and conservation research. New survey methods, data management tools, and algorithms to analyze camera trap images have been developed. For example, cutting-edge artificial intelligence has been applied to analyze vast amounts of animal images to automatically identify species and individuals. In recent years, the rich information on wild animals from camera trap data has been increasingly combined with other data (e.g., remotely sensed land cover from satellites, climate-change predictions, social survey, and other socio-economic data) to address important conservation challenges, such as monitoring the dynamics of wild animal populations, assessing the impacts of human disturbances on animals, and identifying the key areas for conservation.

This Special Issue calls for contributions that expand the existing knowledge of camera trapping in the field of animal ecology and conservation, and enhance the capability of using camera trap technology to support wildlife conservation. We invite submissions on new theories, methods, and empirical analyses. Both quantitative and qualitative studies are welcome. Review papers will also be considered for this Special Issue.

Dr. Hongbo Yang
Dr. Jindong Zhang
Dr. Fang Wang
Dr. Thomas Connor
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Animal behavior
  • Animal identification
  • Camera trap
  • Climate change
  • Conservation management
  • Habitat selection
  • Remote sensing
  • Wildlife conservation

Published Papers (3 papers)

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Research

16 pages, 2059 KiB  
Article
Effects of Inter- and Intra-Specific Interactions on Moose Habitat Selection Limited by Temperature
by Heng Bao, Penghui Zhai, Dusu Wen, Weihua Zhang, Ye Li, Feifei Yang, Xin Liang, Fan Yang, Nathan J. Roberts, Yanchun Xu and Guangshun Jiang
Remote Sens. 2022, 14(24), 6401; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246401 - 19 Dec 2022
Cited by 1 | Viewed by 1622
Abstract
Habitat selection and daily activity patterns of large herbivores might be affected by inter- and intra-specific interaction, changes of spatial scale, and seasonal temperature. To reveal what factors were driving the habitat selection of moose, we collected moose (Alces alces) and [...] Read more.
Habitat selection and daily activity patterns of large herbivores might be affected by inter- and intra-specific interaction, changes of spatial scale, and seasonal temperature. To reveal what factors were driving the habitat selection of moose, we collected moose (Alces alces) and roe deer (Capreolus pygargus bedfordi) occurrence data, analyzed the multi-scale habitat selection and daily activity patterns of moose, and quantified the effects of spatial heterogeneity distribution of temperature, as well as the occurrence of roe deer on these habitat selection processes. Our results suggested that moose and roe deer distribution spatially overlap and that moose habitat selection is especially sensitive to landscape variables at large scales. We also found that the activity patterns of both sexes of moose had a degree of temporal separation with roe deer. In the snow-free season, temperatures drove moose habitat selection to be limited by threshold temperatures of 17 °C; in the snowy season, there were no similar temperature driving patterns, due to the severe cold environment. The daily activity patterns of moose showed seasonal change, and were more active at dawn and nightfall to avoid heat pressure during the snow-free season, but more active in the daytime for cold adaptation to the snow season. Consequently, this study provides new insights on how the comprehensive effects of environmental change and inter- and intra- specific relationships influence the habitat selection and daily activity patterns of moose and other heat sensitive animals with global warming. Full article
(This article belongs to the Special Issue Camera Trapping for Animal Ecology and Conservation)
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15 pages, 5676 KiB  
Article
Wavelet Analysis Reveals Phenology Mismatch between Leaf Phenology of Temperate Forest Plants and the Siberian Roe Deer Molting under Global Warming
by Heqin Cao, Yan Hua, Xin Liang, Zexu Long, Jinzhe Qi, Dusu Wen, Nathan James Roberts, Haijun Su and Guangshun Jiang
Remote Sens. 2022, 14(16), 3901; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14163901 - 11 Aug 2022
Viewed by 1391
Abstract
Global warming is deeply influencing various ecological processes, especially regarding the phenological synchronization pattern between species, but more cases around the world are needed to reveal it. We report how the forest leaf phenology and ungulate molting respond differently to climate change, and [...] Read more.
Global warming is deeply influencing various ecological processes, especially regarding the phenological synchronization pattern between species, but more cases around the world are needed to reveal it. We report how the forest leaf phenology and ungulate molting respond differently to climate change, and investigate whether it will result in a potential phenology mismatch. Here, we explored how climate change might alter phenological synchronization between forest leaf phenology and Siberian roe deer (Capreolus pygargus) molting in northeast China based on a camera-trapping dataset of seven consecutive years, analyzing forest leaf phenology in combination with records of Siberian roe deer molting over the same period by means of wavelet analysis. We found that the start of the growing season of forest leaf phenology was advanced, while the end of the growing season was delayed, so that the length of the growing season was prolonged. Meanwhile, the start and the end of the molting of Siberian roe deer were both advanced in spring, but in autumn, the start of molting was delayed while the end of molting was advanced. The results of wavelet analysis also suggested the time lag of synchronization fluctuated slightly from year to year between forest leaf phenology and Siberian roe deer molting, with a potential phenology mismatch in spring, indicating the effect of global warming on SRD to forest leaf phenology. Overall, our study provides new insight into the synchronization between forest leaf phenology and ungulate molting, and demonstrates feasible approaches to data collection and analysis using camera-trapping data to explore global warming issues. Full article
(This article belongs to the Special Issue Camera Trapping for Animal Ecology and Conservation)
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12 pages, 5087 KiB  
Article
Estimating Wildlife Density as a Function of Environmental Heterogeneity Using Unmarked Data
by Thomas Connor, Wildlife Division, Emilio Tripp, William T. Bean, B. J. Saxon, Jessica Camarena, Asa Donahue, Daniel Sarna-Wojcicki, Luke Macaulay, William Tripp and Justin Brashares
Remote Sens. 2022, 14(5), 1087; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051087 - 23 Feb 2022
Cited by 2 | Viewed by 2179
Abstract
Recent developments to spatial-capture recapture models have allowed their use on species whose members are not uniquely identifiable from photographs by including individual identity as a latent, unobserved variable in the model. These ‘unmarked’ spatial capture recapture (uSCR) models have also been extended [...] Read more.
Recent developments to spatial-capture recapture models have allowed their use on species whose members are not uniquely identifiable from photographs by including individual identity as a latent, unobserved variable in the model. These ‘unmarked’ spatial capture recapture (uSCR) models have also been extended to presence-absence data and modified to allow categorical environmental covariates on density, but a uSCR model, which allows fitting continuous environmental covariates to density, has yet to be formulated. In this paper, we fill this gap and present an extension to the uSCR modeling framework by modeling animal density on a discrete state space as a function of continuous environmental covariates and investigate a form of Bayesian variable selection to improve inference. We used an elk population in their winter range within Karuk Indigenous Territory in Northern California as a case study and found a positive credible effect of increasing forb/grass cover on elk density and a negative credible effect of increasing tree cover on elk density. We posit that our extensions to uSCR modeling increase its utility in a wide range of ecological and management applications in which spatial counts of wildlife can be derived and environmental heterogeneity acts as a control on animal density. Full article
(This article belongs to the Special Issue Camera Trapping for Animal Ecology and Conservation)
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