Climate Change and Biodiversity: Some Considerations in Forecasting Shifts in Species' Potential Distributions

By E. Martínez‐Meyer. 2005

141 citations recorded by the source corpus.

Global climate change and its broad spectrum of effects on human and natural systems has become a central research topic in recent years; biodiversity informatics tools?particularly ecological niche modeling (ENM)?have been used extensively to anticipate potential effects on geographic distributions of species. Misuse of these tools, however, is counterproductive, as biased conclusions might be reached. In this paper, I discuss some issues related to niche theory, geographic distributions, data quality, and algorithms, all of which are relevant when using ENM in climate change projections for biodiversity. This assortment of opinions and ideas is presented in the hope that ENM applications to climate change questions can be made more realistic and more predictive.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/e2753bbab025746dd02ccde174c71ffc93db2e98

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Citations in the corpus

KnowBR: An application to map the geographical variation of survey effort and identify well-surveyed areas from biodiversity databases (2018, background): “…geographic ranges, the extant databases are typically characterized by incompleteness and biases (e.g., Dennis and Hardy, 1999; Soberón et al., 2000; Zaniewski et al., 2002; Anderson, 2003; Martínez-Meyer, 2005; Dennis et al., 2006; Lobo et al., 2007; Hortal et al., 2008; Stropp et al., 2016).”
A comparative evaluation of presence‐only methods for modelling species distribution (2007, background): “, 1999) and it seems that such a combination of rainfall and temperature variables effectively represents correlates of physiological tolerance (Martinez-Meyer, 2005).”
Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data (2011, background): “(Dennis & Hardy 1999; Dennis et al. 1999; Dennis & Thomas 2000; Dennis et al. 2006; Zaniewski et al. 2002; Anderson 2003; Reutter et al. 2003; Graham et al. 2004; Soberón & Peterson 2004; Martínez-Meyer 2005).”
The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, methodology): “In the absence of a general rule, it is generally assumed that the greater the sample size, the more accurate the model (Cumming 2000, Olden and Jackson 2000, McPherson et al. 2004, Martínez-Meyer 2005, Reese et al. 2005).”
Taxonomist survey biases and the unveiling of biodiversity patterns (2009, background): “This is especially true for hyperdiverse groups of organisms such as invertebrates (more than 80% of global species richness), and for global species richness hotspots (May, 1990).”
A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, background): “Plant nomenclature follows Boulos (1999-2005).”
Predicting species responses to climate change: demography and climate microrefugia in California valley oak (Quercus lobata) (2012, background): “We found that saplings were more constricted around surface water than adults in the projected contracting area.”
More complex distribution models or more representative data (2008, background): “…a crude approach, may introduce an indeterminate number of false absences into models owing to the all-too-frequent sampling biases in biological information (see Dennis et al. 1999, Dennis & Thomas 2000, Zaniewski et al. 2002, Reutter et al. 2003, Graham et al. 2004, Martínez-Meyer 2005).”
Where will species go? Incorporating new advances in climate modelling into projections of species distributions (2007, background): “Climate model: Differences between predictions from various climate models make identification of the most appropriate models difficult (Martı́nez-Meyer, 2005).”
Biotic interactions influence the projected distribution of a specialist mammal under climate change (2012, background): “As a negative interaction, competition reveals only where a species may fail, as competition prevents the full occupation of the fundamental niche (Martinez-Meyer, 2005).”