Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions

Beaumont, Linda J.; Hughes, Lesley; Poulsen, Michael

doi:10.1016/j.ecolmodel.2005.01.030

Cited by 444 publications

(401 citation statements)

References 59 publications

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“…Changes in MAP in the SW US are less certain than projections of MAT [7], however, evaporative demand is expected to rise with increases in MAT potentially resulting in massive mortality events [4]. While the link between climate and vegetation has been successfully used to model species distributions [26][27][28], part of the difficulty in modeling particular stand-level responses to climate change results from the lack of high-resolution, spatial data on historic climate variables [17].…”

Section: Introductionmentioning

confidence: 99%

Carbon Stocks and Climate Change: Management Implications in Northern Arizona Ponderosa Pine Forests

Bagdon

Huang

2014

Forests

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Abstract:Researchers have observed climate-driven shifts of forest types to higher elevations in the Southwestern US and predict further migration coupled with large-scale mortality events proportional to increases in radiative forcing. Range contractions of forests are likely to impact the total carbon stored within a stand. This study examines the dynamics of Pinus ponderosa stands under three climate change scenarios in Northern Arizona using the Climate Forest Vegetation Simulator (Climate-FVS) model to project changes in carbon pools. A sample of 90 stands were grouped according to three elevational ranges; low-(1951 to 2194 m), mid-(2194 to 2499 m), and high-(2499 to 2682 m.) elevation stands. Growth, mortality, and carbon stores were simulated in the Climate-FVS over a 100 year timespan. We further simulated three management scenarios for each elevational gradient and climate scenario. Management included (1) a no-management scenario, (2) an intensive-management scenario characterized by thinning from below to a residual basal area (BA) of 18 m 2 /ha in conjunction with a prescribed burn every 10 years, and (3) a moderate-management scenario characterized by a thin-from-below treatment to a residual BA of 28 m 2 /ha coupled with a prescribed burn every 20 years. Results indicate that any increase in aridity due to climate change will produce substantial mortality throughout the elevational range of ponderosa pine stands, with lower elevation stands projected to experience the most devastating effects. Management was only effective for the intensive-management scenario; stands receiving this treatment schedule maintained moderately consistent levels of basal area and demonstrated a higher level of resilience to climate change relative to the two other management scenarios. The results of this study indicate that management can improve OPEN ACCESSForests 2014, 5 621 resiliency to climate change, however, resource managers may need to employ more intensive thinning treatments than currently proposed to achieve the best results.

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Section: Introductionmentioning

confidence: 99%

Carbon Stocks and Climate Change: Management Implications in Northern Arizona Ponderosa Pine Forests

Bagdon

Huang

2014

Forests

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“…Furthermore, addition of numerous redundant covariates increases the probability of overfitting. This is of particular concern because many niche-based models use automated fitting methods with minimal or no selection of variables: it is not unusual for models to be built with ca 13 highly correlated covariates [45,46]. This problem is only one form of statistical model misspecification, all forms of which reduce prediction accuracy, while exaggerating precision.…”

Section: Niche-based Distribution Modelsmentioning

confidence: 99%

Incorporating uncertainty in predictive species distribution modelling

Beale

Lennon

2012

Phil. Trans. R. Soc. B

255

232

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Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and processbased models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

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“…''Fill'' refers to the total fit of the matrix. Columns ''NODFcolumns'' and ''NODFrows'' refer to the NODF as measured only according to matrix columns and rows, respectively, while ''NODF'' refers to the total NODF variables, but a minimum set of available parameters have been chosen to avoid overfitting, which in turn may result in artifacts (Beaumont et al 2005;Heikkinen et al 2006;Araújo and Peterson 2012). The fact that the total contribution of the direct abiotic variables (i.e., soil, landscape and climate variables) in the model reaches 10.8 %, whereas bare soil and plant cover attain 14.1 and 0.1 %, respectively, also indicates that patterns resulting from disturbance and biotic interactions may also be important.…”

Section: Probability Of Occurrence and Biotic Interactionsmentioning

confidence: 99%

Fine-scale spatial patterns of the Tertiary relict Zelkova abelicea (Ulmaceae) indicate possible processes contributing to its persistence to climate changes

Bosque¹,

Adamogianni²,

Bariotakis³

et al. 2013

Reg Environ Change

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In this paper, the fine-scale spatial patterns of the Tertiary relict Zelkova abelicea (Lam.) Boiss. were studied (1) to reveal processes that contributed to its persistence to climate changes and (2) to assist future conservation planning, with the purpose of shifting the attention of conservation practitioners from patterns to processes. Results of the fine-scale spatial patterns of Z. abelicea indicate that the species tolerates disturbance and/or tracks changes resulting from disturbance in the range of its distribution through morphological and reproductive plasticity. In addition, our study indicates that Z. abelicea populations are conserved in the absence of metapopulation structure and that the species participates in plant-plant interactions through facilitation processes. Hence, the persistence of the species to climate changes seems to be more complicated and multifactorial than a linear and plain view of species survival in climate refugial areas, and therefore calls for a consideration of the processes revealed in this paper in future conservation planning.

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Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions

Cited by 444 publications

References 59 publications

Carbon Stocks and Climate Change: Management Implications in Northern Arizona Ponderosa Pine Forests

Carbon Stocks and Climate Change: Management Implications in Northern Arizona Ponderosa Pine Forests

Incorporating uncertainty in predictive species distribution modelling

Fine-scale spatial patterns of the Tertiary relict Zelkova abelicea (Ulmaceae) indicate possible processes contributing to its persistence to climate changes

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