Geospatial Data and Services for Wildlife Management and Conservation

Special Issue Editors


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Guest Editor
Wildlife Conservation Research Unit (WildCRU), Zoology Department, University of Oxford, The Recanati Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK
Interests: spatial ecology; biostatistics; GIS; spatial behaviour; landscape ecology; habitat use; species distribution models; remote sensing; population genetics; landscape change simulations; wildlife conservation

E-Mail Website
Guest Editor
Wildlife Conservation Research Unit (WildCRU), Zoology Department, University of Oxford, The Recanati Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK
Interests: marine ecology; spatial ecology; biostatistics; GIS; spatial behaviour; species distribution models; landscape ecology; wildlife conservation

E-Mail Website
Guest Editor
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 Knoles Dr, Flagstaff, AZ 86011, USA
Interests: earth observation; remote sensing; GIS; lidar; hyperspectral imaging; species distribution modelling; habitat and wildlife conservation

Special Issue Information

Dear Colleagues,

There has been a rapid development of methods to remotely collect large amounts of geospatial data on landscape characteristics, natural resources, and even animal locations and movement across various spatial and temporal scales. Combining these new datasets together creates a unique opportunity for efficient, data-driven wildlife management and conservation from local to global extents. However, acquiring and managing this vast amount of information comes with challenges. Geospatial services, which often utilize GIS (geographic information systems), GPS (global positioning system), and/or remote sensing observations, provide the integrative technology to create, analyze, maintain, and distribute geospatial data and information. Various geospatial applications enable improved and often on-the-fly data visualization, analysis, interoperability, modeling, and decision support.


In this Special Issue, we call for papers exploring the vast subject of acquiring, managing and analyzing geospatial data for improved wildlife management and conservation. The issue will also focus on novel examples of spatial data applications and services. This includes studies on monitoring and modeling biodiversity and species behavioral ecology through camera trapping or satellite tracking of marine and terrestrial megafauna movements and distribution ranges; use of geospatial data and applications in landscape connectivity, genetics and populations dynamics studies; remote sensing applications to understand species–habitat relationships and to monitor and model both the dynamics and the impact of temporal changes in terrestrial and marine ecosystems; and real examples of geospatial datasets or services (applications) being used by management and conservation groups/agencies.

 

Dr. Żaneta Kaszta
Dr. Lara L. Sousa
Mr. Patrick Burns
Guest Editors

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Keywords

  • Remote Sensing
  • Spatial Modelling
  • GIS Services
  • Species Occurrence and Distribution
  • Change Detection
  • Landscape Connectivity
  • Decision Support Systems

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Published Papers (3 papers)

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Research

20 pages, 6887 KiB  
Article
Structural Connectivity of Asia’s Protected Areas Network: Identifying the Potential of Transboundary Conservation and Cost-Effective Zones
by Melissa Penagos Gaviria, Żaneta Kaszta and Mohammad S. Farhadinia
ISPRS Int. J. Geo-Inf. 2022, 11(7), 408; https://doi.org/10.3390/ijgi11070408 - 19 Jul 2022
Cited by 5 | Viewed by 2924
Abstract
Human activities can degrade landscape connectivity and disrupt ecological flows, jeopardising the functional integrity of processes. This study presents a quantitative assessment of Asia’s protected areas’ (PAs) structural connectivity using landscape metrics, as well as analyses of the Cost-Effective Zones’ (CEZs). Using nine [...] Read more.
Human activities can degrade landscape connectivity and disrupt ecological flows, jeopardising the functional integrity of processes. This study presents a quantitative assessment of Asia’s protected areas’ (PAs) structural connectivity using landscape metrics, as well as analyses of the Cost-Effective Zones’ (CEZs). Using nine landscape metrics, we assessed connectivity at zonal (country borders and interior), national, regional, and geographical (islands and continent) levels. The results showed that the structural connectivity of Asia’s PAs network measured by a Connectance index was very low (0.08% without country borders and 9.06% for the average country analysis). In general, connectivity was higher within borders (0.36%) than within the countries (0.22%). Islands exhibited significantly higher Area-weighted mean patch area, Proximity index and Largest patch index, suggesting more integrity and connectiveness. When comparing Asian regions, Western Asia presented the lowest values for Percentage of landscape and Proximity index. We found that only 15% of the CEZs in Asia were under PAs designation, and more CEZs are located in the interior, but the majority with the highest priority was in the borders (9%). We advocate that expanding PAs coverage, specifically targeting areas that increase connectivity (e.g., through transboundary PAs), should be a priority to maintain their ecological function. Full article
(This article belongs to the Special Issue Geospatial Data and Services for Wildlife Management and Conservation)
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17 pages, 1086 KiB  
Article
Pathwalker: A New Individual-Based Movement Model for Conservation Science and Connectivity Modelling
by Siddharth Unnithan Kumar, Żaneta Kaszta and Samuel A. Cushman
ISPRS Int. J. Geo-Inf. 2022, 11(6), 329; https://doi.org/10.3390/ijgi11060329 - 30 May 2022
Cited by 6 | Viewed by 3958
Abstract
Understanding organism movement is at the heart of many ecological disciplines. The study of landscape connectivity—the extent to which a landscape facilitates organism movement—has grown to become a central focus of spatial ecology and conservation science. Several computational algorithms have been developed to [...] Read more.
Understanding organism movement is at the heart of many ecological disciplines. The study of landscape connectivity—the extent to which a landscape facilitates organism movement—has grown to become a central focus of spatial ecology and conservation science. Several computational algorithms have been developed to model connectivity; however, the major models in use today are limited by their lack of flexibility and simplistic assumptions of movement behaviour. In this paper, we introduce a new spatially-explicit, individual- and process-based model called Pathwalker, which simulates organism movement and connectivity through heterogeneous landscapes as a function of landscape resistance, the energetic cost of movement, mortality risk, autocorrelation, and directional bias towards a destination, all at multiple spatial scales. We describe the model’s structure and parameters and present statistical evaluations to demonstrate the influence of these parameters on the resulting movement patterns. Written in Python 3, Pathwalker works for any version of Python 3 and is freely available to download online. Pathwalker models movement and connectivity with greater flexibility compared with the dominant connectivity algorithms currently available in conservation science, thereby, enabling more detailed predictions for conservation practice and management. Moreover, Pathwalker provides a highly capable simulation framework for exploring theoretical and methodological questions that cannot be addressed with empirical data alone. Full article
(This article belongs to the Special Issue Geospatial Data and Services for Wildlife Management and Conservation)
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19 pages, 3596 KiB  
Article
Investigating Carnivore Guild Structure: Spatial and Temporal Relationships amongst Threatened Felids in Myanmar
by Pyae Phyoe Kyaw, David W. Macdonald, Ugyen Penjor, Saw Htun, Hla Naing, Dawn Burnham, Żaneta Kaszta and Samuel A. Cushman
ISPRS Int. J. Geo-Inf. 2021, 10(12), 808; https://doi.org/10.3390/ijgi10120808 - 30 Nov 2021
Cited by 6 | Viewed by 3757
Abstract
The co-occurrence of felid species in Southeast Asia provides an unusual opportunity to investigate guild structure and the factors controlling it. Using camera-trap data, we quantified the space use, temporal activity, and multi-dimensional niche overlap of the tiger, clouded leopard, Asiatic golden cat, [...] Read more.
The co-occurrence of felid species in Southeast Asia provides an unusual opportunity to investigate guild structure and the factors controlling it. Using camera-trap data, we quantified the space use, temporal activity, and multi-dimensional niche overlap of the tiger, clouded leopard, Asiatic golden cat, marbled cat, and leopard cat in the Htamanthi Wildlife Sanctuary, Myanmar. We hypothesised that the spatio-temporal behaviour of smaller cats would reflect the avoidance of the larger cats, and similar-sized guild members would partition their niches in space or time to reduce resource competition. Our approach involved modelling single-species occupancy, pairwise spatial overlap using Bayesian inference, activity overlap with kernel density estimation, and multivariate analyses. The felid assembly appeared to be partitioned mainly on a spatial rather than temporal dimension, and no significant evidence of mesopredator release was observed. Nonetheless, the temporal association between the three mesopredators was inversely related to the similarity in their body sizes. The largest niche differences in the use of space and time occurred between the three smallest species. This study offers new insight into carnivore guild assembly and adds substantially to knowledge of five of the least known felids of conservation concern. Full article
(This article belongs to the Special Issue Geospatial Data and Services for Wildlife Management and Conservation)
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