Spatial filtering to reduce sampling bias can improve the performance of ecological niche models

Boria, Robert A.; Olson, Link E.; Goodman, Steven M.; Anderson, Robert P.

doi:10.1016/j.ecolmodel.2013.12.012

Cited by 1,089 publications

(846 citation statements)

References 31 publications

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“…When local adaptation in niche-relevant dimensions has occurred, correlative models cast at the level of the species will overestimate the niche of any single population (whereas mechanistic models based on single populations will tend to underestimate niches in the same circumstances), and conversely niches estimated for single populations or subsets of species' overall distributions may underestimate the full breadth of the niche (Peterson & Holt 2003;Strubbe et al 2015) or indeed fail to achieve a predictive model at all (Owens et al 2013). Finally, very clearly, the vagaries of the sampling of biodiversity (e.g., spatial bias to accessible areas, incomplete sampling across an area, incomplete detectability of individuals) will have considerable potential to translate into new biases and problems in model outcomes (Hijmans 2012;Kramer-Schadt et al 2013;Boria et al 2014).In sum, returning to the question of what is being estimated, 'niches' estimated by correlational approaches are complicated in terms of their interpretation. Correlational models will generally identify some suite of environmental conditions that fall in between fundamental and realised niches.…”

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confidence: 99%

Mechanistic and Correlative Models of Ecological Niches

Peterson¹,

Papeş²,

Soberón³

2015

European Journal of Ecology

160

129

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The suite of factors that drives where and under what conditions a species occurs has become the focus of intense research interest. Three general categories of methods have emerged by which researchers address questions in this area: mechanistic models of species' requirements in terms of environmental conditions that are based on first principles of biophysics and physiology, correlational models based on environmental associations derived from analyses of geographic occurrences of species, and process-based simulations that estimate occupied distributional areas and associated environments from assumptions about niche dimensions and dispersal abilities. We review strengths and weaknesses of these sets of approaches, and identify significant advantages and disadvantages of each. Rather than identifying one or the other as 'better,' we suggest that researchers take great care to use the method best-suited to each specific research question, and be conscious of the weaknesses of any method, such that inappropriate interpretations are avoided. NICHES AND DISTRIBUTIONSThe many and diverse concepts associated with the term 'niche' have long been central in ecological thinking (Chase & Leibold 2003). After a period of stagnation (approximately 1980-1990), during which research about niches was minimal (McInerny & Etienne 2012a, b), the term has seen a vibrant rebirth in recent decades. This revival appears associated at least in part with the possibility of using large databases of primary biodiversity occurrence data in tandem with geospatial data sets summarising environmental variables to estimate coarse-grained aspects of species' niche dimensions. By extension, from such niche estimates, one can then estimate potential distributional areas of species as well ).This approach is the so-called 'correlative' approach to what has been termed ecological niche modelling (hereafter 'ENM'), in situations in which focus is on understanding conditions that allow persistence of species' populations, or species distribution modelling ('SDM') in situations in which focus is on predicting the geographic distribution of the species (Peterson & Soberón 2012). Other approaches to these challenges exist as well, however, such as 'mechanistic' niche modelling (Kearney & Porter 2009), that aims to understand, using detailed biophysical modelling approaches, the environmental requirements that make up the fundamental niche of a species; and 'process-oriented' or 'hybrid' distribution modelling , wherein hypotheses about niche, dispersal, and (in some cases) biotic interactions are integrated in models and simulations of the entire set of processes leading to the occupation of areas by a species.Some authors prefer to blur distinctions between these approaches , and clearly the three do overlap and interlink (e.g., correlational or mechanistic niche estimates being used in process-oriented approaches). How-ever, here, we posit that the distinctions between mechanistic, correlative, and process-oriented approaches are more than just...

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confidence: 99%

Mechanistic and Correlative Models of Ecological Niches

Peterson¹,

Papeş²,

Soberón³

2015

European Journal of Ecology

160

129

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“…The resulting data were composed of 23 unique distribution records which were then used for building the model. This filtering method can maximize the number of spatially independent localities and can enhance model performance by removing over-fit of the models towards environmental bias (Veloz 2009;Hijmans 2012;Boria et al 2014).…”

Section: Target Species and Occurrence Datamentioning

confidence: 99%

PREDICTING THE IMPACT OF CLIMATE CHANGE ON THE DISTRIBUTION OF Flindersia pimenteliana F. Muell. IN INDONESIAN PAPUA AND PAPUA NEW GUINEA

Robiansyah

2017

BIOTROPIA

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Population of Flindersia pimenteliana (Maple Silkwood) in Indonesian Papua and Papua New Guinea is severely fragmented and experiencing a continuing decline due to habitat destruction and illegal logging. This species is very susceptible to environmental changes and at greater risk of extinction due to its small and fragmented geographic ranges and low abundance. Using maximum entropy (MaxEnt) method, the present study predicted the impact of climate change on the distribution of the species across its native distribution area. Elevation and 19 bioclimatic variables commonly used in species distribution modeling were used as predictors. The prediction model of the 2 current potential distribution identified a total area of 156,214 km in Indonesian Papua and Papua New Guinea (18% of total land area) as suitable habitat for F. pimenteliana. Elevation and precipitation of the wettest, coldest and warmest quarters contributed most to the model. Based on the average of HadGEM2-ES and MIROC-ESM models, potential distribution projections under RCP8.5 scenario suggested a habitat gain of 16% for 2050 and 8% for 2070 in the species distribution. Whereas under RCP4.5, an average habitat gain of 7% was predicted for both 2050 and 2070. The newly suitable habitats were predicted to be found mainly in Southern and Western Highland of Papua New Guinea. Protection of these areas from habitat destruction and land use change is needed to assist F. pimenteliana find the most suitable climate for its survival.

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“…Specifically, we filtered the final dataset to obtain the maximum number of samples that were at least 10 km apart (see Anderson and Raza [51]). This radius was chosen because of the topographic and environmental heterogeneity of this system, following Boria et al [50], who predicted that mountainous regions would require a spatial filter that is smaller than that for regions having more homogenous environments.…”

Section: Sampling Biasmentioning

confidence: 99%

“…For these reasons, we filtered occurrence records with a linear distance ≤10 km to neighboring records using QGIS [49]. This distance was not chosen to approximate the species dispersal capabilities, but rather to reduce the inherent geographic biases associated with collection data (see Boria et al [50]). Specifically, we filtered the final dataset to obtain the maximum number of samples that were at least 10 km apart (see Anderson and Raza [51]).…”

Section: Sampling Biasmentioning

confidence: 99%

Modeling the distribution of rare and interesting moss species of the family Orthotrichaceae (Bryophyta) in Tajikistan and Kyrgyzstan

2017

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Bryological research carried out from 2008 in Tajikistan and Kyrgyzstan brought interesting data on the occurrence of epiphytic bryophytes which have not been recorded yet there. One of the species was recently described as a new (Orthotrichum pamiricum) and some of the other newly recorded species are considered as rare or endangered in the region of Middle Asia. To make detailed field monitoring of the species with the aim of mapping their distribution in a wild and complex mountainous terrain, it was necessary in the first instance to identify the area with suitable conditions for the occurrence of these species. We present an innovative modeling program MaxEnt (maximum entropy modeling), which have not previously been used for modeling either epiphytic bryophytes or in the Middle Asia region. Using 205 samples (presence-only data), percent tree cover, and seven uncorrelated bioclimatic variables, regions suitable for the occurrence of the studied species were identified. Distribution models for eight most interesting species of Orthotrichum are presented here (O. affine, O. anomalum, O. crenulatum, O. cupulatum, O. pallens, O. pamiricum, O. pumilum, and O. speciosum). They indicated appropriate areas for the most probable occurrence of the species in western Tajikistan, and southwestern and northeastern Kyrgyzstan. These results could serve as guides for future survey expeditions, and aid in the conservation of target species and our understanding of their ecology. Different environmental variables for various species were selected as the most important for modeling. However, for most species higher minimum temperatures and higher precipitation in the wettest month and mean diurnal range were the variables with the greatest contribution to the models.

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Spatial filtering to reduce sampling bias can improve the performance of ecological niche models

Cited by 1,089 publications

References 31 publications

Mechanistic and Correlative Models of Ecological Niches

Mechanistic and Correlative Models of Ecological Niches

PREDICTING THE IMPACT OF CLIMATE CHANGE ON THE DISTRIBUTION OF Flindersia pimenteliana F. Muell. IN INDONESIAN PAPUA AND PAPUA NEW GUINEA

Modeling the distribution of rare and interesting moss species of the family Orthotrichaceae (Bryophyta) in Tajikistan and Kyrgyzstan

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