Multi-scale Mexican spotted owl (Strix occidentalis lucida) nest/roost habitat selection in Arizona and a comparison with single-scale modeling results

Timm, Brad C.; McGarigal, Kevin; Cushman, Samuel A.; Ganey, Joseph L.

doi:10.1007/s10980-016-0371-0

Cited by 55 publications

(68 citation statements)

References 44 publications

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“…Brown bears within this region perceived anthropogenic disturbances, such as building density, agriculture, and transportation infrastructure, at broad scales, while environmental variables, such as edge effects amongst cover types and canopy closure, were selected at finer scales (Sánchez et al., 2014). Here, we show the importance of taking scale into account when conducting habitat selection studies, which is supported by other studies on, for example, Mexican spotted owl (Timm et al., 2016), mountain bongo antelope ( Tragelaphus euryceros isaaci ; Estes, Okin, Mwangi, & Shugart, 2008) and brown bears (Sánchez et al., 2014). However, the multiscale approach within habitat selection studies is still uncommon (McGarigal et al., 2016) and to our knowledge this is the first multiscale habitat selection study for cheetahs.…”

Section: Discussionmentioning

confidence: 99%

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Living on the edge: Multiscale habitat selection by cheetahs in a human‐wildlife landscape

Klaassen

Broekhuis

2018

Ecology and Evolution

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Animals select habitats that will ultimately optimize their fitness through access to favorable resources, such as food, mates, and breeding sites. However, access to these resources may be limited by bottom‐up effects, such as availability, and top‐down effects, such as risk avoidance and competition, including that with humans. Competition between wildlife and people over resources, specifically over space, has played a significant role in the worldwide decrease in large carnivores. The goal of this study was to determine the habitat selection of cheetahs (Acinonyx jubatus) in a human‐wildlife landscape at multiple spatial scales. Cheetahs are a wide‐ranging, large carnivore, whose significant decline is largely attributed to habitat loss and fragmentation. It is believed that 77% of the global cheetah population ranges outside protected areas, yet little is known about cheetahs’ resource use in areas where they co‐occur with people. The selection, or avoidance, of three anthropogenic variables (human footprint density, distance to main roads and wildlife areas) and five environmental variables (open habitat, semiclosed habitat, edge density, patch density and slope), at multiple spatial scales, was determined by analyzing collar data from six cheetahs. Cheetahs selected variables at different scales; anthropogenic variables were selected at broader scales (720–1440 m) than environmental variables (90–180 m), suggesting that anthropogenic pressures affect habitat selection at a home‐range level, whilst environmental variables influence site‐level habitat selection. Cheetah presence was best explained by human presence, wildlife areas, semiclosed habitat, edge density and slope. Cheetahs showed avoidance for humans and steep slopes and selected for wildlife areas and areas with high proportions of semiclosed habitat and edge density. Understanding a species’ resource requirements, and how these might be affected by humans, is crucial for conservation. Using a multiscale approach, we provide new insights into the habitat selection of a large carnivore living in a human‐wildlife landscape.

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Section: Discussionmentioning

confidence: 99%

“…For example, Timm, McGarigal, Cushman, and Ganey (2016) assessed a multiscale habitat selection for nesting and roosting areas of Mexican spotted owl ( Strix occidentalis lucida ) and compared this to a single scale habitat selection. They found that the multiscale habitat selection model outperformed the single scale model.…”

Section: Introductionmentioning

confidence: 99%

Living on the edge: Multiscale habitat selection by cheetahs in a human‐wildlife landscape

Klaassen

Broekhuis

2018

Ecology and Evolution

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“…We compared each subset model and selected the model that exhibits the lowest total out-of-bag error and lowest maximum within-class error. To identify the most parsimonious logistic regression model for each nation we used the same training and predictor variable set as in the random forest model for that nation, and employed all-subsets logistic regression with model averaging, using the ''Dredge'' function in the R packing MuMIN (e.g., Timm et al 2016;Chambers et al 2016).…”

Section: Modelling Approachesmentioning

confidence: 99%

Multiple-scale prediction of forest loss risk across Borneo

et al. 2017

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Context The forests of Borneo have among the highest biodiversity and also the highest forest loss rates on the planet. Objectives Our objectives were to: (1) compare multiple modelling approaches, (2) evaluate the utility of landscape composition and configuration as predictors, (3) assess the influence of the ratio of forest loss and persistence points in the training sample, (4) identify the multiple-scale drivers of recent forest loss and (5) predict future forest loss risk across Borneo. Methods We compared random forest machine learning and logistic regression in a multi-scale approach to model forest loss risk between 2000 and 2010 as a function of topographical variables and landscape structure, and applied the highest performing model to predict the spatial pattern of forest loss risk between 2010 and 2020. We utilized a naïve model as a null comparison and used the total operating characteristic AUC to assess model performance. Results Our analysis produced five main results. We found that: (1) random forest consistently outperformed logistic regression and the naïve model; (2) including landscape structure variables substantially improved predictions; (3) a ratio of occurrence to nonoccurrence points in the training dataset that does not match the actual ratio in the landscape biases the predictions of both random forest and logistic regression; (4) forest loss risk differed between the three nations that comprise Borneo, with patterns in Kalimantan highly related to distance from the edge of the previous frontier of forest loss, while Malaysian Borneo showed a more diffuse pattern related to the structure of the landscape; (5) we predicted continuing 123Landscape Ecol (2017) 32:1581-1598 DOI 10.1007 very high rates of forest loss in the 2010-2020 period, and produced maps of the expected risk of forest loss across the full extent of Borneo. Conclusions These results confirm that multiplescale modelling using landscape metrics as predictors in a random forest modelling framework is a powerful approach to landscape change modelling. There is immense immanent risk to Borneo's forests, with clear spatial patterns of risk related to topography and landscape structure that differ between the three nations that comprise Borneo.

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“…However, environmental variables are frequently only considered in the immediate vicinity of the depredation event, thereby ignoring the possibility that environmental factors further afield may influence the presence of predators and hence the likelihood of a depredation event to occur (but see Rostro-García et al, 2016). Recent studies have shown that ecological processes may be driven by environmental factors across a range of spatial scales (Cushman, Elliot, Macdonald, & Loveridge, 2015;Timm, McGarigal, Cushman, & Ganey, 2016) and multiscale approaches should also be considered when determining environmental predictors for human-wildlife conflict (Rostro-García et al, 2016). Failure to take scale into account may result in an erroneous evaluation of a relationship or detection of a relationship altogether (Cushman & Landguth, 2010) which could misinform management decisions.…”

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confidence: 99%

Identification of human–carnivore conflict hotspots to prioritize mitigation efforts

Broekhuis

Cushman

Elliot

2017

Ecology and Evolution

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Human–carnivore conflict is a primary driver of carnivore declines worldwide and resolving these conflicts is a conservation priority. However, resources to mitigate conflicts are limited and should be focused on areas of highest priority. We conducted 820 semistructured interviews with community members living within Kenya's Maasai Mara ecosystem. A multiscale analysis was used to determine the influence of husbandry and environmental factors on livestock depredation inside livestock enclosures (bomas). Areas with a high proportion of closed habitat and protected areas had the highest risk of depredation. Depredation was most likely to occur at weak bomas and at households where there were fewer dogs. We used the results to identify potential conflict hotspots by mapping the probability of livestock depredation across the landscape. 21.4% of the landscape was classified as high risk, and within these areas, 53.4% of the households that were interviewed had weak bomas. Synthesis and applications. With limited resources available to mitigate human–carnivore conflicts, it is imperative that areas are identified where livestock is most at risk of depredation. Focusing mitigation measures on high‐risk areas may reduce conflict and lead to a decrease in retaliatory killings of predators.

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Multi-scale Mexican spotted owl (Strix occidentalis lucida) nest/roost habitat selection in Arizona and a comparison with single-scale modeling results

Cited by 55 publications

References 44 publications

Living on the edge: Multiscale habitat selection by cheetahs in a human‐wildlife landscape

Living on the edge: Multiscale habitat selection by cheetahs in a human‐wildlife landscape

Multiple-scale prediction of forest loss risk across Borneo

Identification of human–carnivore conflict hotspots to prioritize mitigation efforts

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