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Relevant bibliographies by topics / Bioclimatic modelling

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Journal articles

Academic literature on the topic 'Bioclimatic modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Bioclimatic modelling"

1

Heikkinen, Risto K., Miska Luoto, Miguel B. Araújo, Raimo Virkkala, Wilfried Thuiller, and Martin T. Sykes. "Methods and uncertainties in bioclimatic envelope modelling under climate change." Progress in Physical Geography: Earth and Environment 30, no. 6 (2006): 751–77. http://dx.doi.org/10.1177/0309133306071957.

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Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic ‘envelope’models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non-climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.

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2

Barber, Xavier, David Conesa, Antonio López-Quílez, and Javier Morales. "Multivariate Bioclimatic Indices Modelling: A Coregionalised Approach." Journal of Agricultural, Biological and Environmental Statistics 24, no. 2 (2019): 225–44. http://dx.doi.org/10.1007/s13253-018-00345-z.

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3

Braidwood, David, and Christopher J. Ellis. "Bioclimatic equilibrium for lichen distributions on disjunct continental landmasses." Botany 90, no. 12 (2012): 1316–25. http://dx.doi.org/10.1139/b2012-103.

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Bioclimatic models assume that species distributions reflect their sensitivity to macroclimate, the so-called bioclimatic equilibrium. This has proven to be a controversial assumption. Here we perform a new test in the hypothesis of climatic equilibrium by comparing species’ bioclimatic space between two independently derived spatial distributions in Britain and North America. A presence-only statistical model (MAXENT) was used to construct bioclimatic response surfaces for 25 lichens in North America. These models were then projected onto British climate space. We tested the following: (1) the statistical congruence between likelihood values for North American bioclimatic space projected onto Britain and species’ observed British distributions, and (2) the extent to which the projection for a species matched its observed British distribution pattern better than the distributions for an alternative suite of species. In general, there is good evidence for bioclimatic equilibrium when comparing species distributions in North America and Britain. However, bioclimatic test 1 (statistical congruence) and bioclimatic test 2 (spatial matching) were failed by six (24% of cases) and four (16% of cases) species, respectively. Although there is general support for bioclimatic modelling in lichens, the species that failed a test of equilibrium would have been difficult to predict based on prior knowledge; however it may be explained by taxonomic uncertainty and (or) the existence of multiple correlated environmental drivers.

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4

Gavin, Daniel G., and Feng Sheng Hu. "Bioclimatic modelling using Gaussian mixture distributions and multiscale segmentation." Global Ecology and Biogeography 14, no. 5 (2005): 491–501. http://dx.doi.org/10.1111/j.1466-822x.2005.00171.x.

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5

Metcalf, Jessica L., Stefan Prost, David Nogués-Bravo, et al. "Integrating multiple lines of evidence into historical biogeography hypothesis testing: a Bison bison case study." Proceedings of the Royal Society B: Biological Sciences 281, no. 1777 (2014): 20132782. http://dx.doi.org/10.1098/rspb.2013.2782.

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One of the grand goals of historical biogeography is to understand how and why species' population sizes and distributions change over time. Multiple types of data drawn from disparate fields, combined into a single modelling framework, are necessary to document changes in a species's demography and distribution, and to determine the drivers responsible for change. Yet truly integrated approaches are challenging and rarely performed. Here, we discuss a modelling framework that integrates spatio-temporal fossil data, ancient DNA, palaeoclimatological reconstructions, bioclimatic envelope modelling and coalescence models in order to statistically test alternative hypotheses of demographic and potential distributional changes for the iconic American bison ( Bison bison ). Using different assumptions about the evolution of the bioclimatic niche, we generate hypothetical distributional and demographic histories of the species. We then test these demographic models by comparing the genetic signature predicted by serial coalescence against sequence data derived from subfossils and modern populations. Our results supported demographic models that include both climate and human-associated drivers of population declines. This synthetic approach, integrating palaeoclimatology, bioclimatic envelopes, serial coalescence, spatio-temporal fossil data and heterochronous DNA sequences, improves understanding of species' historical biogeography by allowing consideration of both abiotic and biotic interactions at the population level.

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6

Back, Yannick, Peter Marcus Bach, Alrun Jasper-Tönnies, Wolfgang Rauch, and Manfred Kleidorfer. "A rapid fine-scale approach to modelling urban bioclimatic conditions." Science of The Total Environment 756 (February 2021): 143732. http://dx.doi.org/10.1016/j.scitotenv.2020.143732.

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7

Assis, Jorge, Lennert Tyberghein, Samuel Bosch, Heroen Verbruggen, Ester A. Serrão, and Olivier De Clerck. "Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling." Global Ecology and Biogeography 27, no. 3 (2017): 277–84. http://dx.doi.org/10.1111/geb.12693.

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8

Zhidekulova, G., Zh Mustafayev, and A. Kozykeyeva. "Regulation of irrigation: Modelling of bioclimatic coefficients of agricultural cultures." Research on Crops 19, no. 1 (2018): 132. http://dx.doi.org/10.5958/2348-7542.2018.00022.0.

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9

Tytar, V. M., and Ya R. Oksentyuk. "Modelling the Bioclimatic Niche of a Cohort of Selected Mite Species (Acari, Acariformes) Associated with the Infestation of Stored Products." Vestnik Zoologii 53, no. 5 (2019): 399–416. http://dx.doi.org/10.2478/vzoo-2019-0036.

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Abstract In this study an attempt is made to highlight important variables shaping the current bioclimatic niche of a number of mite species associated with the infestation of stored products by employing a species distribution modeling (SDM) approach. Using the ENVIREM dataset of bioclimatic variables, performance of the most robust models was mostly influenced by: 1) indices based on potential evapotranspiration, which characterize ambient energy and are mostly correlated with temperature variables, moisture regimes, and 2) strong fluctuations in temperature reflecting the severity of climate and/or extreme weather events. Although the considered mite species occupy man-made ecosystems, they remain more or less affected by the surrounding bioclimatic environment and therefore could be subjected to contemporary climate change. In this respect investigations are needed to see how this will affect future management targets concerning the safety of food storages.

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10

Boreux, J. J., C. Gadbin-Henry, J. Guiot, and L. Tessier. "Radial tree-growth modelling with fuzzy regression." Canadian Journal of Forest Research 28, no. 8 (1998): 1249–60. http://dx.doi.org/10.1139/x98-088.

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A so-called fuzzy linear regression is used in dendroecology to model empirically tree growth as a function of a bioclimatic index representing the water stress, i.e., the ratio of actual evapotranspiration to potential evapotranspiration. The response function predicts tree growth as (fuzzy) intervals, narrow in the domain where the bioclimatic index is most limiting and becoming progressively larger elsewhere. The method is tested with a population of Pinus pineaL. from the Provence region in France. It is shown that fuzzy linear regression gives results comparable with those obtained using a linear response function. The interval of credibility given by the fuzzy regression suggests that more precise expected growth is obtained for high water stress, which is typical of Mediterranean climate. Fuzzy linear regression can be also a method to test different hypotheses on several potential predictors when any further experimental approach is quite impossible as it is for trees in their natural environment. To sum up, fuzzy regression could be a first step before the construction of a kind of growth simulator adapted to different environments of a given species. In environmental sciences, the fuzzy response function thus appears to be an approach between the mechanistic and the statistical descriptive approaches.

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