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7 results for “primary prey”
Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey
<p><strong>Data and file overview:</strong></p> <ol> <li>Kamaru_Path_Analysis_Data.csv</li> <li>Kamaru_Path_Analysis.R</li> <li>Kamaru_Zebra_RSF_Data.csv</li> <li>Kamaru_Zebra_RSF.R</li> </ol> <p><strong>Layers used to build Zebra RSF:</strong></p> <ol> <li>Kamaru_DWater: distance to water</li> <li>Kamaru_DGlade: distance to glade</li> <li>Kamaru_DSettlement: distance to human settlement</li> <li>Kamaru_OPC_Veg: vegetation layer (classes: <em>V. drepanolobium</em>, <em>E. divinorum, </em>others)</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv</strong></p> <ol> <li>Number of variables: 11</li> <li>Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020)</p> <p>(B) Kill_ID: kill identifier.</p> <p>(C) Lion_ID: individual lion pride identifier.</p> <p>(D) Date (Day, Month, Year) when a specific kill occurred.</p> <p>(E) Zebra_kill (1 = kill site, 0 = paired random location).</p> <p>(F). Species: Zebra.</p> <p>(G) Visibility: openness measurement using a rangefinder in (m).</p> <p>(H) Lion_activity: Utilization distributions (UD) of lions.</p> <p>(I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants).</p> <p>(J) zeb.rsf: resource selection function value.</p> <p>(K) zeb.density: zebra density estimated from resource selection functions.</p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv</strong></p> <ol> <li>Number of variables: 10</li> <li>Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) Species: Zebra.</p> <p>(B) Date (Day, Month, Year) for that sighting.</p> <p>(C) Survey: count identifier (Survey 2 to 21).</p> <p>(D) GPS location (X and Y), longitude and latitude of that sighting location.</p> <p>(E) Transect: Transect number.</p> <p>(F) Used: (1= zebra sighting, 0 = paired point).</p> <p>(G) zebra.ct: total number of zebras in each sighting.</p> <p> </p> <p><strong>R CODE</strong></p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models.</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.</li> </ol>
Data from: Asymmetrical predation intensity produces divergent antipredator behaviors in primary and secondary prey
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Data from: Urbanization and primary productivity mediate the predator-prey relationship between deer and coyotes
<p>Predator-prey interactions are important to regulating populations and structuring communities but are affected by many dynamic, complex factors, across larges-scales, making them difficult to study. Integrated population models (IPMs) offer a potential solution to understanding predator-prey relationships by providing a framework for leveraging many different datasets and testing hypotheses about interactive factors. Here, we evaluate the coyote-deer (<em>Canis latrans</em> – <em>Odocoileus virginianus</em>) predator-prey relationship across the state of North Carolina (NC). Because both species have similar habitat requirements and may respond to human disturbance, we considered net primary productivity (NPP) and urbanization as key mediating factors. We estimated deer survival and fecundity by integrating camera trap, harvest, biological and hunter observation datasets into a two-stage, two-sex Lefkovich population projection matrix. We allowed survival and fecundity to vary as functions of urbanization, NPP and coyote density and projected abundance forward to test eight hypothetical scenarios. We estimated initial average deer and coyote densities to be 11.83 (95% CI: 5.64, 20.80) and 0.46 (95% CI: 0.02, 1.45) individuals/km<sup>2</sup>, respectively. We found a negative relationship between current levels of coyote density and deer fecundity in most areas which became more negative under hypothetical conditions of lower NPP or higher urbanization, leading to lower projected deer abundances. These results suggest that coyotes could have stronger effects on deer populations in NC if their densities rise, but primarily in less productive and/or more suburban habitats. Our case study provides an example of how IPMs can be used to better understand the complex relationships between predator and prey under changing environmental conditions.</p>
Data from: Urbanization and primary productivity mediate the predator-prey relationship between deer and coyotes
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Raw sequence of: Field investigation- and dietary metabarcoding-based screening of arthropods that prey on primary tea pests
<p><span>Predatory natural enemies play key functional roles in </span><span>biological control</span><span>.</span><span> Abundant </span><span>predatory arthropod species</span> <span>have been recorded</span><span> in tea plantation ecosystems.</span><span> However, few studies have comprehensively evaluated the control effect of predatory arthropods on tea pests in the field. We performed a one-year field investigation and collected predatory arthropods and pests in the tea </span><span>canopy.</span><span> Total 7,931 predatory arthropod individuals were collected, and </span><em><span>Coleosoma blandum</span></em><span> (Araneae, Theridiidae) was the most abundant species in the studied tea plantation. The population dynamics between <em>C. blandum</em> and four main tea pest species (<em>Aleurocanthus spiniferus, Empoasca onukii, Ectropis grisescens</em> and <em>Scopula subpunctaria</em>) were established using the individual number of predators and pests in each month. The results showed that the occurrence of </span><span>C. blandum</span> <span>showed high synchronism</span><span> with the occurrence of <em>A. spiniferus, Em. onukii </em>and <em>Ec. grisescens</em></span><span>. </span><span>The prey spectrum of <em>C. blandum</em> was </span><span>further analyzed using DNA metabarcoding. Among prey species, <em>A. spiniferus, Em. onuki</em>i and <em>Ec. grisescens</em> were included, and the relative abundance and positive rates of target DNA fragments of <em>A. spiniferus </em>were </span><span>obviously</span><span> greater</span> <span>than</span><span> those of other two pests.</span></p>
Data from: Response of pumas (Puma concolor) to migration of their primary prey in Patagonia
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Raw sequence of: Field investigation- and dietary metabarcoding-based screening of arthropods that prey on primary tea pests
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