Improved abundance prediction from presence–absence data

Conlisk, Erin; Conlisk, John; Enquist, Brian J.; Thompson, Jill; Harte, John

doi:10.1111/j.1466-8238.2008.00427.x

Cited by 41 publications

(44 citation statements)

References 30 publications

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“…Once it is calibrated at fine resolutions across multiple species, one could then predict the number of total species present inside the entire study region. Many different models have been proposed to upscale species richness from fine to coarse areas (see e.g., Ulrich and Ollik 2005, Shen and He 2008, Conlisk et al 2009). This approach provides an alternative method, defined within a unified and coherent framework, that may complement such predictions.…”

Section: Discussionmentioning

confidence: 99%

Downscaling species occupancy from coarse spatial scales

Azaele

Cornell

Kunin

2012

Ecological Applications

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Abstract. The measurement and prediction of species' populations at different spatial scales is crucial to spatial ecology as well as conservation biology. An efficient yet challenging goal to achieve such population estimates consists of recording empirical species' presence and absence at a specific regional scale and then trying to predict occupancies at finer scales. So far the majority of the methods have been based on particular species' distributional features deemed to be crucial for downscaling occupancy. However, only a minority of them have dealt explicitly with specific spatial features. Here we employ a wide class of spatial point processes, the shot noise Cox processes (SNCP), to model species occupancies at different spatial scales and show that species' spatial aggregation is crucial for predicting population estimates at fine scales starting from coarser ones. These models are formulated in continuous space and locate points regardless of the arbitrary resolution that one employs to study the spatial pattern. We compare the performances of nine models, calibrated at regional scales and demonstrate that a very simple class of SNCP, the Thomas process, is able to outperform other published models in predicting occupancies down to areas four orders of magnitude smaller than the ones employed for the parameterization. We conclude by explaining the ability of the approach to infer spatially explicit information from spatially implicit measures, the potential of the framework to combine niche and spatial models, and the possibility of reversing the method to allow upscaling.

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Section: Discussionmentioning

confidence: 99%

Downscaling species occupancy from coarse spatial scales

Azaele

Cornell

Kunin

2012

Ecological Applications

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“…However, the assumption of habitat homogeneity is unrealistic at regional scales. Recently, Conlisk et al (2009) noted the limitation of OAR models and extended them by incorporating an additional spatial autocorrelation parameter. Even though the rationale of Conlisk et al's (2009) model is analogous to the BEM (Hui et al 2006), it still belongs to the OAR model category, and its performance in predicting abundance from broadscale presence-absence data requires further investigation.…”

Section: Discussionmentioning

confidence: 99%

Extrapolating population size from the occupancy–abundance relationship and the scaling pattern of occupancy

Hui

McGeoch

Reyers

et al. 2009

Ecological Applications

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Abstract. The estimation of species abundances at regional scales requires a cost-efficient method that can be applied to existing broadscale data. We compared the performance of eight models for estimating species abundance and community structure from presenceabsence maps of the southern African avifauna. Six models were based on the intraspecific occupancy-abundance relationship (OAR); the other two on the scaling pattern of species occupancy (SPO), which quantifies the decline in species range size when measured across progressively finer scales. The performance of these models was examined using five tests: the first three compared the predicted community structure against well-documented macroecological patterns; the final two compared published abundance estimates for rare species and the total regional abundance estimate against predicted abundances. Approximately two billion birds were estimated as occurring in South Africa, Lesotho, and Swaziland. SPO models outperformed the OAR models, due to OAR models assuming environmental homogeneity and yielding scale-dependent estimates. Therefore, OAR models should only be applied across small, homogenous areas. By contrast, SPO models are suitable for data at larger spatial scales because they are based on the scale dependence of species range size and incorporate environmental heterogeneity (assuming fractal habitat structure or performing a Bayesian estimate of occupancy). Therefore, SPO models are recommended for assemblagescale regional abundance estimation based on spatially explicit presence-absence data.

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“…Many problems have been reported in the literature. For example, there may be only partial information available (Conlisk et al, 2009) or substantial error arising from locational uncertainty (Freeman and Moisen, 2008;Johnson and Gillingham, 2008;Graham et al, 2008;Osborne and Leitao, in press). Indeed a common problem is associated with data on the absence of species (Lobo, 2007;Jimenez-Valverde and Lobo, 2007;Graham et al, 2008).…”

Section: Accuracy and Comparisonmentioning

confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

114

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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Improved abundance prediction from presence–absence data

Cited by 41 publications

References 30 publications

Downscaling species occupancy from coarse spatial scales

Downscaling species occupancy from coarse spatial scales

Extrapolating population size from the occupancy–abundance relationship and the scaling pattern of occupancy

An overview of recent remote sensing and GIS based research in ecological informatics

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