Accuracy of resource selection functions across spatial scales

Meyer, Carolyn B.; Thuiller, Wilfried

doi:10.1111/j.1366-9516.2006.00241.x

Cited by 141 publications

(155 citation statements)

References 77 publications

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“…For example, modeling methods have been developed that use presenceonly data [11]; the impact of limited sample size in modeling have been studied [5,12]; and solutions have been proposed for the problem of incorrectly located species records [13], uneven sampling effort [14][15][16], spatial autocorrelation [17][18][19][20], and scales [21,22]. However, not all deficiencies in species data sets have been fully studied.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data

Costa

Foody

Jiménez

et al. 2015

IJGI

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Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data "contaminated" with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species' niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling.

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Section: Introductionmentioning

confidence: 99%

Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data

Costa

Foody

Jiménez

et al. 2015

IJGI

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“…More generally, Schneider et al (1987) found that mobile species often show decoupling from the environment at finer scales, and habitat association at coarser scales, compared to finer-scale coupling with habitat by sessile species. Meyer & Thuiller (2006) reported that the majority of species respond to habitat characteristics at more than one scale at the same time. Despite that, a response measured at one particular scale cannot always be used to predict habitat use at another scale (VanderWerf 1993, Apps et al 2001.…”

Section: Scale In Ecologymentioning

confidence: 99%

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Lecours¹,

Devillers²,

Schneider³

et al. 2015

Mar. Ecol. Prog. Ser.

148

123

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Understanding the effects of scale is essential to the understanding of natural ecosystems, particularly in marine environments where sampling is more limited and sporadic than in terrestrial environments. Despite its recognized importance, scale is rarely considered in benthic habitat mapping studies. Lack of explicit statement of scale in the literature is an impediment to better characterization of seafloor pattern and process. This review paper highlights the importance of incorporating ecological scaling and geographical theories in benthic habitat mapping. It reviews notions of ecological scale and benthic habitat mapping, in addition to the way spatial scale influences patterns and processes in benthic habitats. We address how scale is represented in geographic data, how it influences their analysis, and consequently how it influences our understanding of seafloor ecosystems. We conclude that quantification of ecological processes at multiple scales using spatial statistics is needed to gain a better characterization of species−habitat relationships. We offer recommendations on more effective practices in benthic habitat mapping, including sampling that covers multiple spatial scales and that includes as many environmental variables as possible, adopting continuum-based habitat characterization approaches, using statistical analyses that consider the spatial nature of data, and explicit statement of the scale at which the research was conducted. We recommend a set of improved standards for defining benthic habitat. With these standards benthic habitats can be defined as 'areas of seabed that are (geo)statistically significantly different from their surroundings in terms of physical, chemical and biological characteristics, when observed at particular spatial and temporal scales'.

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“…Many SDMs provide general predictions of distribution based on coarse-grain metrics often over broad spatial extents (e.g., patch size) that can reflect coarse patterns of habitat use and provide predictions for broad spatial extents for a variety of conservation and management goals (Betts et al 2007, Collier et al 2012. However, habitat use and resulting distributions can be driven by habitat characteristics assessed by organisms at multiple spatial extents and grains, including fine-grained conditions across relatively small spatial extents (Orians and Wittenberger 1991, Meyer and Thuiller 2006, Chalfoun and Martin 2007. Ecological research commonly investigates the role of fine-grained vegetation metrics over small spatial extents when investigating responses such as territory selection or nest site selection (Misenhelter and Rotenberry 2000).…”

Section: Introductionmentioning

confidence: 99%

Using LiDAR‐derived vegetation metrics for high‐resolution, species distribution models for conservation planning

et al. 2013

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. Using LiDAR-derived vegetation metrics for high-resolution, species distribution models for conservation planning. Ecosphere 4(3):42. http://dx.doi.org/10.1890/ES12-000352.1Abstract. Advances in remotely sensed data for characterizing habitat have enabled development of spatially explicit predictive species distribution models (SDM) that can be essential tools for management. SDMs commonly use coarse-grain metrics, such as forest patch size or patch connectivity, over broad spatial extents. However, species distributions are likely driven in part by local, fine-grained habitat conditions. Conservation and management are often planned and applied locally, where coarse predictions may be uninformative or not sufficiently precise. We investigated the integration of high-resolution LiDAR (Light Detection and Ranging) with avian point sampling data to develop a detection-corrected occupancy model to quantify habitat-occurrence relationships for two species with different habitats: the endangered golden-cheeked warbler (Setophaga chrysoparia) and black-capped vireo (Vireo atricapilla) on a military installation in central Texas. We compared occupancy models that used only the more conventional, coarse remotely sensed metrics to models that also incorporated high-resolution LiDAR-derived metrics for vegetation height and canopy cover, to assess their use for predicting distributions. Models including LiDAR-derived vegetation height and canopy cover metrics were competitive for both species, and models without LiDAR-derived vegetation height had substantially lower model weights and explanatory strength. Area under curve estimates for the highest ranked models were high for warblers (0.864) and moderate for vireos (0.746). Using the best supported models for each species, we predicted the occurrence distribution for both species. The resulting predictions provide a decision support tool that enables assessment of the status, impacts, and mitigation of impacts to endangered species habitat on the installation due to land management and military training activities that is more standardized and accurate than current assessment approaches based on visual gestalt of habitat and expert opinion. Additionally, although previous species distribution models have been created for our focal species, most fail to match the grain and extent of most management actions or include local, fine-grained metrics that influence distributions. In contrast, we demonstrate that use of LiDAR with species occurrence data can provide precision and resolution at a scale that is relevant ecologically and to the operational scale of most conservation and management actions.

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Accuracy of resource selection functions across spatial scales

Cited by 141 publications

References 77 publications

Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data

Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Using LiDAR‐derived vegetation metrics for high‐resolution, species distribution models for conservation planning

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