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9 results for “wildlife corridor”
Wildlife along the Salt River corridor of the greater Phoenix, Arizona, USA metropolitan area: results of a camera-trapping project (2020-2021)
The goal of this research project was to evaluate how wildlife populations responded to the gradient of urbanization, water, and vegetation. We deployed 43 wildlife cameras across the gradient of urbanization January 2021 to January 2022. We documented a suite of wildlife species, from small mammals and birds to large mammals. Data present whether a species was detected at a site during this time period.
Dataset for: An optimisation approach for designing wildlife corridors with ecological and spatial considerations
<p>The fragmentation of wildlife habitats caused by anthropogenic activities has reduced biodiversity and impaired key ecosystem functions. Wildlife corridors play an important role in linking detached habitats. The optimal design of such corridors considering spatial, ecological, and economic factors is addressed in this paper.</p> <p>We present a novel graph-theoretic optimisation approach and a mixed-integer linear programming model to determine an optimal wildlife corridor connecting two given habitat patches. The model maximises the total quality of the corridor and satisfies pre-specified corridor width and length requirements under a resource constraint.</p> <p>Compared to the corridor design models presented in the literature, our model is conceptually simpler, and it is computationally convenient. We applied the model to a real data set for Eldorado National Forest in California, USA, involving 1,363 irregular land parcels.</p> <p>The model can be extended to design multiple corridors that connect two or more existing habitat patches.</p>
Dataset for: An optimisation approach for designing wildlife corridors with ecological and spatial considerations
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Data from: Does wildlife resource selection accurately inform corridor conservation?
Evaluating landscape connectivity and identifying and protecting corridors for animal movement have become central challenges in applied ecology and conservation. Currently, resource selection analyses are widely used to focus corridor planning where animal movement is predicted to occur. An animal's behavioural state (e.g. foraging, dispersing) is a significant determinant of resource selection patterns, yet has largely been ignored in connectivity assessments. We review 16 years of connectivity studies employing resource selection analysis to evaluate how researchers have incorporated animal behaviour into corridor planning, and highlight promising new approaches for identifying wildlife corridors. To illustrate the importance of behavioural information in such analyses, we present an empirical case study to test behaviour-specific predictions of connectivity with long-distance dispersal movements of African wild dogs Lycaon pictus. We conclude by recommending strategies for developing more realistic connectivity models for future conservation efforts. Our review indicates that most connectivity studies conflate resource selection with connectivity requirements, which may result in misleading estimates of landscape resistance, and lack validation of proposed connectivity models with movement data. Our case study shows that including only directed movement behaviour when measuring resource selection reveals markedly different, and more accurate, connectivity estimates than a model measuring resource selection independent of behavioural state. Synthesis and applications. Our results, using African wild dogs as a case study, suggest that resource selection analyses that fail to consider an animal's behavioural state may be insufficient in targeting movement pathways and corridors for protection. This failure may result in misidentification of wildlife corridors and misallocation of limited conservation resources. Our findings underscore the need for considering patterns of animal movement in appropriate behavioural contexts to ensure the effective application of resource selection analyses for corridor planning.
FIG. 3 in On the Road Again: Assessing the Use of Roadsides as Wildlife Corridors for Gopher Tortoises (Gopherus polyphemus)
FIG. 3. (A) Log-scaled minimum convex polygon (MCP) home ranges compared between habitat types (i.e., ruderal corridors, inland scrub, and coastal strand). Corridor tortoises occupied slightly smaller home ranges but were not significantly different because of the large variance seen in their home ranges. Inland home ranges were obtained from Smith et al. (1997). (B) Log-scaled average distances traveled between tracking events (m) compared between habitat types (i.e., ruderal corridors, coastal strand) but excluding inland scrub for which the data were unavailable. Average distances were not significantly different between habitat types.
FIG. 2 in On the Road Again: Assessing the Use of Roadsides as Wildlife Corridors for Gopher Tortoises (Gopherus polyphemus)
FIG. 2. Details of the two distinct sites within Kennedy Space Center, USA, used for the different parts of this study where (A) and (B) correlate to the study sites outlined in Figure 1. (A) Map of the study site where current roadside corridor use was determined via radiotelemetry of tortoises captured along the roads. Eight example minimum convex polygon (MCP) home ranges are colored showing movement confined to areas along the corridor but no movement directly through the corridor. Tortoise 5221 was the only individual observed moving from the corridor to coastal strand habitat over a distance of 500 m. (B) Map of the study site where we assessed the feasibility of roadsides to function as movement corridors. We translocated tortoises (dotted colored lines) from either inland or coastal habitat into the potential corridor. Daily radiotelemetry (solid colored lines) determined if tortoises used corridors rather than straight-line paths to return to their original home range.
FIG. 1 in On the Road Again: Assessing the Use of Roadsides as Wildlife Corridors for Gopher Tortoises (Gopherus polyphemus)
FIG. 1. Map of Gopher Tortoise range highlighting their conservation status in different parts of their range. Included is an outline of the study site at Kennedy Space Center, USA, with the potential roadside corridors connecting coastal and inland habitat outlined in black. We conducted both parts of this study along two different roads (boxed and labeled). Details of these two distinct sites appear in Figure 2. Box (A) is the study site for examining current roadside corridors use using radiotelemetry to determine how Gopher Tortoises in this region spatially used the roadsides. Box (B) is the study site for translocating tortoises along the roadside to determine if movement through the corridor back to their original home range was feasible.
FIG. 4 in On the Road Again: Assessing the Use of Roadsides as Wildlife Corridors for Gopher Tortoises (Gopherus polyphemus)
FIG. 4. An example rose diagram (circular histogram) of a single inland tortoise's (ID: 5250) direction of travel when the distance traveled was>7 m. The Rayleigh test of directional uniformity displayed insignificance in the directional movement for both true and corridor homing.
Data from: Does wildlife resource selection accurately inform corridor conservation?
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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.