Effects of non-representative sampling design on multi-scale habitat models: flammulated owls in the Rocky Mountains.

Chiaverini, Luca; Wan, Ho Yi; Hahn, Beth; Cilimburg, Amy B.; Wasserman, Tzeidle N.; Cushman, Samuel A.

doi:10.1016/j.ecolmodel.2021.109566

Cited by 14 publications

(7 citation statements)

References 34 publications

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“…However, by sampling within protected areas, we likely provided a more realistic view on species richness patters, reflecting the expected species‐habitat relationships that would occur in undisturbed contexts, without accounting for adverse effects of human disturbance, poaching and other factors not associated with measured habitat factors. However, the camera trap surveys could have still been inadequate to represent the range of environmental conditions of the islands, which is known to affect the ability to correctly identify species‐habitat relationships (Chiaverini et al ., 2021). Therefore, we have minimized the potential errors arising from the spatially biased survey by masking the areas showing the strongest ecological differences from the sampling locations in the multivariate environment of the Mahalanobis distance surface.…”

Section: Discussionmentioning

confidence: 99%

“…Ideally, sampling strategies should be designed to identify the full range of gradients that are most influential to species' habitat selection processes, rather than being biased towards a fraction of the full spectrum of environmental conditions (Wessels et al ., 1998). Failing to do so might result in models with erroneous parameter estimates and lower predictive performances (Chiaverini et al ., 2021). However, in our case, it was impossible to implement a randomly stratified survey given the extent of the study area and the inaccessibility of some regions.…”

Section: Discussionmentioning

confidence: 99%

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Multi‐scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands

Chiaverini

Macdonald

Bothwell

et al. 2022

Animal Conservation

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Species occur in sympatric assemblages, bound together by ecological relationships and interspecific interactions. Borneo and Sumatra host some of the richest assemblages of biota worldwide. The region, however, faces the highest global deforestation rates, which seriously threaten its unique biodiversity. We used a large camera trap dataset that recorded data for 70 terrestrial species of mammals and birds, to explore the drivers of regional species richness patterns. Using a multi-scale, multivariate modelling framework which quantified the main environmental factors associated with patterns of biodiversity, while simultaneously assessing individual relationships of each species, we determined the ecological drivers of sampled biodiversity, and their contributions to community assemblages. We then mapped predicted species richness, evaluated the effectiveness of protected areas in securing biodiversity hotspots, performed gap analysis to highlight biodiverse areas lacking protection and compared our predictions with species richness maps produced by using IUCN range layers. Finally, we investigated the performance of each species as an indicator of sampled biodiversity. We demonstrate that biodiversity in Borneo and Sumatra is primarily affected by gradients of ecological and anthropogenic factors, and only marginally by topographic and spatial factors. In both islands, species are primarily associated with elevational gradients in vegetation and climate, leading to altitudinal zonation in niche separation as a major factor characterizing the islands' biodiversity. Species richness was highest in north-eastern Borneo and in western Sumatra. We found that most predicted biodiversity hotspots are not formally protected in either island; only 9.2 and 18.2% of the modelled species richness occurred within protected areas in Borneo and Sumatra, respectively. We highlighted that our prediction for Borneo performed better than, and differed drastically from, the IUCN species richness layer, while for Sumatra our modelled species richness layer and the IUCN one were similar, and both showed low predictive power. Our analysis suggests that common and generalist carnivores are the most effective indicators of sampled biodiversity and have high potential as focal, umbrella or indicator species to assist multi-species vertebrate conservation planning. Understanding existing drivers and patterns of biodiversity is critical to support the development of effective community conservation strategies in this rapidly changing region.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multi‐scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands

Chiaverini

Macdonald

Bothwell

et al. 2022

Animal Conservation

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“…One of the main limitations of this study is that data were collected by surveying along roads and fire cutlines, and are therefore not an unbiased representation of the entire study area. However, simulations have shown that, while models trained with spatially representative datasets out‐performed models trained with spatially non‐representative datasets in standard metrics of model performance, spatially non‐representative models produced superior predictions of species‐environment relationships (Chiaverini et al, 2021). Our ecological inferences should thus not be compromised by the non‐representative sampling design, which is typical of many ecological assessments.…”

Section: Discussionmentioning

confidence: 99%

Random forest modelling of multi‐scale, multi‐species habitat associations within KAZA transfrontier conservation area using spoor data

Searle

Kaszta

Bauer

et al. 2022

Journal of Applied Ecology

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1. As landscape-scale conservation models grow in prominence, assessments of how wildlife utilise multiple-use landscapes are required to inform effective conservation and management planning. Such efforts should incorporate multispecies perspectives to maximise value for conservation, and should account for scale to accurately capture species-environment relationships.2. We show that the random forest machine learning algorithm can be used to model large-scale sign-based data in a multi-scale framework. We used this method to investigate scale-dependent habitat associations for 16 mammal species of high conservation importance across the southern Kavango Zambezi (KAZA) Transfrontier Conservation Area in Botswana and Zimbabwe.3. Our findings revealed substantial variation in factors shaping habitat use across species, and illustrate that different species often have divergent responses to the same environmental and anthropogenic factors, and differ in the scales at which they respond to them. For all variables across all species, scale optimisation most often selected our largest scale. 4. Precipitation, soil nutrients, and vegetation appeared to be the most important factors determining mammal distributions, likely through their associations with food resources for herbivores and, in turn, prey availability for carnivores.5. Anthropogenic pressures also had an important influence, with many species selecting against areas with high cattle density. The variety of relationships with human density indicated that species vary in their tolerance of humans. We found a consistent positive relationship with areas under high protection, and negative relationship with unprotected and less-strictly protected areas.

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“…One can not neglect the fact that the choice of spatial scale is essential to understanding the patterns and processes of urban ecosystems. Existing studies have also shown that species will inevitably respond differently to environments and resources at different spatial scales when making habitat choices [37][38][39][40]. Moreover, the overall habitat quality is usually determined by the interaction of multiple spatial processes at different scales [41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Driving Mechanism of Habitat Quality at Different Grid-Scales in a Metropolitan City

Dong

et al. 2022

Forests

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Urban ecosystem dysfunction, habitat fragmentation, and biodiversity loss caused by rapid urbanization have threatened sustainable urban development. Urban habitat quality is one of the important indicators for assessing the urban ecological environment. Therefore, it is of great practical significance to carry out a study on the driving mechanism of urban habitat quality and integrate the results into urban planning. In this study, taking Zhengzhou, China, as an example, the InVEST model was used to analyze the spatial differentiation characteristics of urban habitat quality and Geodetector software was adopted to explore the driving mechanism of habitat quality at different grid-scales. The results show the following: (1) LUCC, altitude, slope, surface roughness, relief amplitude, population, nighttime light, and NDVI are the dominant factors affecting the spatial differentiation of habitat quality. Among them, the impacts of slope, surface roughness, population, nighttime light, and NDVI on habitat quality are highly sensitive to varying grid-scales. At the grid-scale of 1000 to 1250 m, the impacts of the dominant factors on habitat quality is closer to the mean level of multiple scales. (2) The impact of each factor on the spatial distribution of habitat quality is different, and the difference between most factors has always been significant regardless of the variation of grid-scales. The superimposed impact of two factors on the spatial distribution of habitat quality is greater than the impact of the single factor. (3) Combined with the research results and the local conditions of Zhengzhou, we put forward some directions of habitat protection around adjusting urban land use structure, applying nature-based solutions and establishing a systematic thinking model for multi-level urban habitat sustainability.

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Effects of non-representative sampling design on multi-scale habitat models: flammulated owls in the Rocky Mountains.

Cited by 14 publications

References 34 publications

Multi‐scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands

Multi‐scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands

Random forest modelling of multi‐scale, multi‐species habitat associations within KAZA transfrontier conservation area using spoor data

Driving Mechanism of Habitat Quality at Different Grid-Scales in a Metropolitan City

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