CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling

By Darren J. Kriticos, B. L. Webber, Agathe Leriche, Noboru Ota, I. Macadam, J. Bathols, J. Scott. 2012

697 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/ad1973694774b31264d0860b7d9163e60cbbde1a

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Citations in the corpus

Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment (2014, methodology): “The recently published CliMond CM10_ 1975H_V1 climatic dataset (Kriticos et al. 2012; available at http://www.climond.org) interpolated at 10 arc minute (;18 km) resolution was used for CLIMEX modeling.”
Effects of climate change on the distribution of indigenous species in oceanic islands (Azores) (2016, background): “…distance (Mahalanobis 1936); 2) Euclidean distance; 3) Gower distance; 4) Ecological Niche Factor Analysis (Hirzel et al. 2002); 5) BIOCLIM (Kriticos et al. 2012); 6) Maximum Entropy (Phillips et al. 2006); 7) Genetic Algorithm for Rule Prediction (Stockwell and Peters 1999); 8)…”
High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections (2023, methodology): “Table 1 presents the Köppen-Geiger climate classification used herein, which is identical to that used in Version 1 18 as well as several other studies 39,40 .”
Effect of the Maxent model’s complexity on the prediction of species potential distributions (2016, neutral): “…濒危物种迁地保护研究中; 后者以模拟物种的潜在分布为目的, 模型构建后需要转移, 这种转移以生态位的保守性为基础, 主要应用于入侵生物学、全球变化对物种分布的影响、以及谱系生物地理学中 (Jiménez-Valverde et al, 2011)。在以现实分布为目的时, 生态位模型有时也被称为物种分布模型。目前常用的生态位模型有约20种, 各自有不同的理论基础、分析方式和数据需求, 这些模型所模拟的物种分布分别处于现实分布和潜在分布之间。一般认为同时采用物种存在和不存在分布数据的模型所模拟的物种分布倾向于反映物种的现实分布, 仅采用…”
Will remote sensing shape the next generation of species distribution models? (2015, methodology): “CRU (New et al. 2002), WorldClim (Hijmans et al. 2005), CliMond (Kriticos et al. 2012) and PRISM (US only; Daly et al. 2002), are all examples of spatially explicit datasets of climatic conditions.”
Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models (2014, methodology): “Global climate data (in the form of ASCII grids) of the chosen covariates were obtained from the CliMond data 1152 Diversity and Distributions, 20, 1147–1159, ª 2014 John Wiley & Sons Ltd set (version 1.1; Kriticos et al., 2012) at a resolution of 10’ for current climate conditions (1975H).”
Predicting distributions of invasive species (2013, methodology): “Additional data can be added by users, and finer resolution gridded data are now available for use within CLIMEX (Kriticos & Leriche, 2010; Kriticos et al., 2012).”
Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications (2019, neutral): “H) (Kriticos et al., 2012).”
A physiological analogy of the niche for projecting the potential distribution of plants (2012, methodology): “Correlative models by contrast use temperature or drought indices (e.g. the biocli- matic indices developed by Hijmans et al., 2005; Sutherst et al., 2007; Kriticos et al., 2011) to describe the effects of seasonality on plant performance.”
The potential global distribution of the brown marmorated stink bug, Halyomorpha halys, a critical threat to plant biosecurity (2017, methodology): “1) climate dataset was used for model fitting (Kriticos et al. 2012).”
Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species (2018, methodology): “Mean annual solar radiation was obtained from CliMond (accessed 7 July 2015; https://www.climond.org/BioclimData.aspx; Kriticos et al., 2012).”
Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models (2011, background): “(subgenus Phyllodineae, Mimosoideae: Fabaceae; Miller et al., 2011) are native to Australia and major invasive species in South Africa, the Iberian Peninsula and California (Turpie et al., 2003; Gaertner et al., 2009; Le Maitre et al., 2011; Richardson & Rejmánek, 2011).”
Development characteristics of the box‐tree moth Cydalima perspectalis and its potential distribution in Europe (2014, methodology): “The CliMond 10 spatial resolution climate data set was used as input into the model (Kriticos et al. 2012).”