Detecting And Locating Prey Through Depositional Odor Trails Conover PdfBy Nuriya M. In and pdf 10.12.2020 at 13:01 10 min read
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- The Structuring Role of Submerged Macrophytes in Lakes
- Predator-Prey Dynamics: The Role of Olfaction
Protecting an endangered and highly poached species can conflict with providing an open and ecologically connected landscape for coexisting species.
The Structuring Role of Submerged Macrophytes in Lakes
Protecting an endangered and highly poached species can conflict with providing an open and ecologically connected landscape for coexisting species. In Kenya, about half of the black rhino Diceros bicornis live in electrically fenced private conservancies. Purpose-built fence-gaps permit some landscape connectivity for elephant while restricting rhino from escaping.
We monitored the usage patterns at these gaps by motion-triggered cameras and found high traffic volumes and predictable patterns of prey movement. The prey-trap hypothesis PTH proposes that predators exploit this predictable prey movement.
We tested the PTH at two semi-porous reserves using two different methods: a spatial analysis and a temporal analysis. Using spatial analysis, we mapped the location of predation events with GPS and looked for concentration of kill sites near the gaps as well as conducting clustering and hot spot analysis to determine areas of statistically significant predation clustering.
We found no support for the PTH and conclude that the design of the fence-gaps is well suited to promoting connectivity in these types of conservancies. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Predation data are deposited in Dryad doi: Competing interests: The authors have declared that no competing interests exist. In many parts of Africa, including in Kenya, there is an increased reliance on electrical fencing to protect wildlife and reduce human-wildlife conflicts [ 1 — 4 ], including in very large protected areas such as the Abedare Conservation Area [ 5 , 6 ] and an ambitious project to enclose the Mount Kenya Forest Reserve with a km electrified fence [ 7 ].
Lion Panthera leo and other predators can thrive within small fenced reserves [ 8 ]. Population numbers can be close to their estimated carrying capacity and prey abundance regulates space use and density [ 9 , 10 ]. Although fencing is viewed as the most effective way to protect wildlife and reduce human-wildlife conflicts [ 11 ], fences come with a long list of drawbacks.
Fencing wildlife causes mortality as animals can get entangled and killed while attempting to leave the fenced habitat [ 12 — 14 ]. Fencing also has many secondary drawbacks that can affect long-term population viability, including reduced access to resources [ 15 — 17 ], and the creation of edge effects [ 5 , 18 , 19 ].
Further, fencing is expensive to install and maintain as elephant often break their way through fencing [ 1 , 4 , 20 ] leading to costly repairs and potential human-wildlife conflict. Wildlife managers attempt to mitigate these shortcomings by designing better fences [ 21 ], creating effective linkages between protected habitat [ 18 , 22 ], reconnecting habitat by removing certain portions of fencing [ 23 ] and ensuring minimal encroachment from agriculture, urban development or roads [ 24 — 27 ].
Biologists emphasize creating connected landscape systems, on private and public lands, to ensure long-term persistence of highly mobile species [ 28 — 31 ].
Purpose-built fence-gaps permit some landscape connectivity for migration and dispersal. However, linkages and other connecting structures necessarily funnel animal movement into narrow areas that predators could learn to exploit due to the spatial predictability of prey passage.
Due to this funnelling of movement, there is a concentration of spoor near the fence-gaps, which creates depositional odour trails that can be detected and followed by predators [ 32 ]. Predators do not necessarily have to kill at the fence-gaps but could use these cues to track prey further away from the crossing structures.
For example, spotted hyena Crocuta crocuta use olfaction for hunting and will follow migrating prey for long distances [ 33 ] and can run down prey in an active chase for up to 4km [ 34 ].
The prey-trap hypothesis PTH has been advanced as a possible negative consequence of highway crossing structures by suggesting that predators can improve their predation success by hunting in and around these high prey traffic areas [ 35 — 37 ]. Although the empirical evidence is weak [ 38 ], anecdotal [ 39 , 40 ] or unsupportive [ 35 , 41 , 42 ], there is evidence that fencing can lead to behavioural changes in some predators.
For example, wild dog Lycaon pictus will incorporate fences into their hunting strategy to significantly increase their ability to take down large prey [ 43 — 45 ]. These studies raise fundamentally interesting questions that have yet to be fully tested in different ecosystems although the possibility of prey-traps developing at passageways has been raised [ 46 ].
Our research is the first to examine and formally test the PTH in a fenced conservancy equipped with fence-gaps to allow the passage of wildlife and is the first to test the PTH in an African savannah ecosystem. The objective of this study was to test if predation events clustered near the fence-gaps and if we could detect active hunting or tracking at the fence-gaps. Successful management of migratory species within fenced conservancies depends on wildlife crossing structures acting as safe passageways in and out of suitable habitat, enhancing connectivity and long-term survival.
Therefore, it is critical to verify that the connecting structures do not have any unforeseen negative consequences. The properties comprise approximately 37, ha of electrically fenced wildlife refuge within a larger mixed habitat matrix that includes many small plot agricultural and pastoral communities, roads, towns, farms, and other conservancies.
Both conservancies support the full breadth of the Eastern African savannah wildlife. The vegetation of Lewa and Borana is classified as a mix of Northern Acacia-Commiphora bushlands and thicket [ 47 ] with significant areas of savannah. A km long, two-meter high fence, consisting of twelve-strand of alternating electrified and grounded wires surrounds Lewa. The Northern fence-gaps measures approximately 30 m and leads to an unfenced pastoral area Leparua community.
The Western fence-gap measures approximately 20 m and joins the Borana Conservancy. The fence-gaps serve primarily to let elephant and other migratory species move through the fence and in and out of the conservancy.
Lewa also has a fence-gap to the South that leads to a 14 km long elephant corridor linking Mt-Kenya and Lewa [ 26 ]. Borana has nine fence-gaps, including the shared fence-gap with Lewa see Fig 1. The design of the fence-gaps varies considerably between conservancies. As such, the Lewa design exploits a few of the unique anatomical features of the rhino, i.
The placement of the bollards makes it very difficult for an adult rhino to squeeze through and the rock wall acts as a further obstacle should a smaller individual make it past the bollards.
Species using the Lewa fence-gaps usually slow their pace and cross the fence-gap cautiously, but without much difficulty. The combination of the rock wall with the bollards is used to restrict rhino escaping from the conservancies into non-protected areas.
In order to test if predators learned to exploit the passage of prey at the fence-gaps, we captured baseline data measuring the volume of traffic through the fence-gaps using camera-traps. We then performed spatial analyses of predation events as well as a temporal analysis of predator and prey movements at the gaps. We used remotely triggered cameras Reconyx RC60HO Hyperfire, Holmen, WI to capture movement data at the fence-gaps in order to verify that there was regular prey traffic volume.
Each fence-gap had at least one camera; all camera fields of view were perpendicular to the direction of wildlife travel.
Cameras were configured for a three exposure burst upon being triggered by their inbuilt motion detectors and set for rapid-fire to ensure continuous shooting for as long as their sensors detected motion. From time to time photographs were uploaded into a central database for later analysis. We collected predation data from Lewa dating back to kills and from Borana since kills.
The collection of carcass data is a strategic imperative for all field staff at the study site and, as of included anti-poaching patrollers, rhino rangers, safari guides, fencers, trackers, herders, and research personnel. These patrollers are deployed hours a day, seven days a week and patrol mostly near the access points, roads, fence-line and fence-gaps but can access all of the study site, including by vehicle, plane and helicopter. They also patrol the entire fence-line searching for security breaches and elephant damage to the electrical fence on a regular basis.
These rangers follow the rhino everywhere they go on the conservancy. Further, it is important to note that the safari guides are highly incentivized to locate fresh kills for the tourists. It is also noteworthy that the study site has an extensive road network in excess of km of internal tracks and roads and that no point is more than 1 km away from a track or road.
The study area is thus well monitored and mortality data are considered representative. We established the exact location of the predation events by assigning a set of GPS coordinates to the descriptive physical locations of each reported carcasses.
We used only verified predator kills in our data set. We assigned a cause of death to every carcass found and animals that had died of other causes unverified as predator kills, drought, electrocution, etc. Starting in April , Lewa began using a cluster point method nearest neighbour as described by Davidson et al. Researchers collared five lion groups with GPS radio collars and monitored potential kill sites by identifying locations where the lion activity clustered in excess of four hours.
The detection of carcasses in the field is sensitive to prey size, as larger carcasses are easier to detect and last longer increasing the probability of discovery, therefore smaller prey is likely under-represented in our sample [ 48 ].
We created concentric ring buffers around the fence-gaps at various radii , and m and intersected those buffers with the shape of the conservancy to calculate the captured areas. We then measured the number of kills recorded in each buffer and compared that number to the expected number of kills on each conservancy for an area of equal size. The total buffer area at each radius distance represents the sum of the areas captured within the defined radius at all the gaps on each respective conservancy.
Scheel [ 50 ] observed hunting distances of up to m. Although Holekamp et al. The General G returns a global z-score, if it is significantly positive then the areas of high predation tend to cluster with other areas of high predation.
If the z-score is significantly negative then areas of low predation are clustered with other areas of low predation. The null hypothesis is complete spatial randomness.
If the z-score is significantly positive then the spatial distribution is more spatially clustered than would be expected under a random spatial process. If the z-score is significantly negative, then the spatial distribution is more dispersed than would be expected under a random scenario. We calculated the spatial autocorrelation for a number of incremental neighborhood sizes.
We selected the smallest distance needed to ensure that all weighted predation locations had at least one neighbor as our starting point. We created a table of z-scores from which we selected the neighborhood distance that corresponded to the first peak in significant z-scores i. The selected neighborhood size represents the smallest neighborhood distance where significant spatial autocorrelation is occurring, i. Significantly positive scores indicate statistically significant clustering of locations with high predation counts i.
For the temporal analysis we used a different data set based on the time stamp from each photo. For every crossing event, we recorded date, time, species detected, direction of travel and number of individuals of that species crossing in the same direction. We selected a subset of these data where the crossing of a prey species was followed by the crossing in the same direction of a predator species. We calculated the time differential of these paired crossing events and designated this time differential as the HUNT variable.
We also paired the crossing of the predator to the next prey crossing. We calculated the time differential between these paired crossings and designated this time differential as the AVOID variable. To test whether prey passage modified predator behavior, we followed Ford and Clevenger [ 35 ], where we compared the mean time lapse between the passage of prey followed by predator HUNT versus the inverse, the passage of predator followed by prey AVOID. We expected that if predators were actively using the gaps to pick-up scent tracks and if prey species were actively avoiding using the gaps after predator passage the HUNT time interval should be shorter than the AVOID time interval.
We monitored wildlife movements through the various fence-gaps using camera-traps. During —13, we recorded in excess of 50, mammals of 34 species crossing through the Lewa fence-gaps 46, crossings at the Northern fence-gap, 1, at the Southern fence-gap, 3, at the Western fence-gap joining Lewa and Borana. Temporal analysis of the movement data showed strong predictability of prey through the busiest fence-gaps see Fig 3.
We recorded two kills within m of the crossing gaps, nine kills within m and 46 kills within m over both conservancies. We calculated the chi-squared statistic for the probability of the kills being over or under represented in the sampled area near the gaps. As shown in Table 1 , predation events were significantly under-represented near the gaps.
We found that predation locations were significantly clustered on both conservancies see Table 2. We aggregated the data points that fell within the precision tolerance of m resulting in the individual predation locations to aggregate into weighted data points weighted by incident count ranging from 1—15 individual predation events on Lewa.
Similarly, the individual kill locations on Borana aggregated into a set of 89 weighted data points ranging from 1—4 individual predation events.
Predator-Prey Dynamics: The Role of Olfaction
Navigationsleiste aufklappen. Sehr geehrter ZLibrary-Benutzer! Wir haben Sie an die spezielle Domain de1lib. Conover Humans, being visually oriented, are well versed in camouflage and how animals hide from predators that use vision to locate prey. However, many predators do not hunt by sight; they hunt by scent.
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Analyzed the data: MDD. Predation data are deposited in Dryad doi: Protecting an endangered and highly poached species can conflict with providing an open and ecologically connected landscape for coexisting species.
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I would like to thank my advisor, Dr. Michael Conover, for his patience and encouragement. Predators that rely on scents to locate prey do so by detecting the prey's odor depositional trails should affect predator foraging behavior. Some of.
Humans, being visually oriented, are well versed in camouflage and how Michael R. Conover or other special characters, the eBook will be available in PDF (PBK) format, which cannot be reflowed. Olfactory Predators and Odorants, Detecting and Locating Prey Through Depositional Odor Trails.