Where will species go? Incorporating new advances in climate modelling into projections of species distributions

By Linda Beaumont, Andrew J. Pitman, Michael Poulsen, Lesley Hughes. 2007

214 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/086a06629fadbe5903842330c059c4d0536ac1b8

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Citations in the corpus

Impacts of climate change on the future of biodiversity (2012, background): “In addition, internal climate model variability could result in greater differences in projected speciesÕ distributions than variability between climate models (Beaumont et al. 2007).”
Climate change at the landscape scale: predicting fine‐grained spatial heterogeneity in warming and potential refugia for vegetation (2009, methodology): “However, it is already known that climate change predictions vary substantially according to variations in the GCM (Beaumont et al., 2007) and statistical technique (Araújo et al., 2005), and the results of SDMs vary when fine-grained spatial variability in temperature is considered (Ashcroft et al”
Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: Opportunities and challenges (2009, unknown): “Many studies do not describe how GCM data are downscaled for use in SDMs, but techniques vary from simple linear interpolation to more complex dynamic and statistical (empirical) techniques (Hewitson & Crane 2006; Beaumont et al. 2007; Charles et al. 2007).”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, background): “Due to this, SDMs developed using the same variables coming from different sources (e.g. different climate modelling procedures) provide very different results (Beaumont et al., 2007).”
A 40‐year, continent‐wide, multispecies assessment of relevant climate predictors for species distribution modelling (2014, background): “The greater the set of variables used in a SDM, the more likely is it that ecologically relevant predictors are also included, but unfortunately, this also increases the risk of overfitting (Beaumont et al., 2007).”
Identifying refugia from climate change (2010, background): “…is undoubtedly influenced by climatic stability, SDMs sometimes assume there is uniform warming or downscale global climate models in a manner that would result in nearly uniform warming at fine scales (see references in Beaumont et al., 2007; Trivedi et al., 2008; and VanDerWal et al., 2009).”
Effects of climate change on species distribution, community structure, and conservation of birds in protected areas in Colombia (2013, background): “Point locality data, current climate interpolations, future climate projections, statistical modeling methods, and dispersal assumptions have all associated uncertainties that will ultimately affect the quantitative estimates of distribution models (Beaumont et al. 2007; Heikkinen et al. 2006).”
Assessing the vulnerability of European butterflies to climate change using multiple criteria (2010, methodology): “…envelope models may vary based on the modelling technique adopted (Lawler et al. 2006; Pearson et al. 2006), the choice of future climate scenarios (Beaumont et al. 2007), and the life-history traits and geographical characteristics of the modelled species (Luoto et al. 2005; McPherson and Jetz…”
Ocean‐scale prediction of whale shark distribution (2012, background): “Predictions arising from this approach are essential to determine the likely effects of habitat change on persistence probability or community structure (Araújo & Williams, 2000; Beaumont et al., 2007).”
Modelling the potential impact of global warming on Ips typographus voltinism and reproductive diapause (2011, background): “However, data from other scenarios, other combinations of global and regional climate models, and data from model runs realized with altered initial conditions will produce results with different magnitudes (Beaumont et al. 2007; Déqué et al. 2007; IPCC 2007).”
Predicted impact of climate change on European bats in relation to their biogeographic patterns (2010, methodology): “When studying species’ responses to future climate change, it is important to model several future scenarios, in order to cover how each species responds to a range of climate change projections (Araújo et al., 2006; Beaumont et al., 2007).”
The Mediterranean Sea as a ‘cul‐de‐sac’ for endemic fishes facing climate change (2010, background): “In terrestrial ecosystems, numerous environmental variables (temperature, rainfall, soil structure, and geochemistry, etc.) are required to make satisfactory predictions of species occurrences (e.g., Beaumont et al., 2007).”
Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change (2009, unknown): “For example, AOGCMs can give different predictions in regions with a particular environmental characteristic, whereas all methods for SDM can provide similar solutions in a given region and differ in others (Beaumont et al. 2007).”
Fine‐resolution (25 m) topoclimatic grids of near‐surface (5 cm) extreme temperatures and humidities across various habitats in a large (200 × 300 km) and diverse region (2011, background): “In contrast, seasonal averages cannot be separated into individual weather patterns, and therefore, spatial variations in climate change due to changing weather patterns are uncertain and often idealized as uniform warming across a landscape (some examples are given in Beaumont et al., 2007).”
Modelling changes in species’ abundance in response to projected climate change (2012, methodology): “To date, climate envelope approaches have mostly been applied to presence-only or presence/absence distribution data, and as such have been used to project changes in species’ distributions (Bakkenes et al., 2002; Scott et al., 2002; Hilbert et al., 2004; Skov & Svenning, 2004; Thomas et al., 2004; ”