scholarly journals Variation in habitat suitability does not always relate to variation in species' plant functional traits

2009 ◽  
Vol 6 (1) ◽  
pp. 120-123 ◽  
Author(s):  
Wilfried Thuiller ◽  
Cécile H. Albert ◽  
Anne Dubuis ◽  
Christophe Randin ◽  
Antoine Guisan

Habitat suitability models, which relate species occurrences to environmental variables, are assumed to predict suitable conditions for a given species. If these models are reliable, they should relate to change in plant growth and function. In this paper, we ask the question whether habitat suitability models are able to predict variation in plant functional traits, often assumed to be a good surrogate for a species' overall health and vigour. Using a thorough sampling design, we show a tight link between variation in plant functional traits and habitat suitability for some species, but not for others. Our contrasting results pave the way towards a better understanding of how species cope with varying habitat conditions and demonstrate that habitat suitability models can provide meaningful descriptions of the functional niche in some cases, but not in others.

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e101196 ◽  
Author(s):  
Ophelia Wang ◽  
Luke J. Zachmann ◽  
Steven E. Sesnie ◽  
Aaryn D. Olsson ◽  
Brett G. Dickson

2021 ◽  
Vol 36 (2) ◽  
pp. 455-474
Author(s):  
Eric Ash ◽  
David W. Macdonald ◽  
Samuel A. Cushman ◽  
Adisorn Noochdumrong ◽  
Tim Redford ◽  
...  

Abstract Context Species habitat suitability models rarely incorporate multiple spatial scales or functional shapes of a species’ response to covariates. Optimizing models for these factors may produce more robust, reliable, and informative habitat suitability models, which can be beneficial for the conservation of rare and endangered species, such as tigers (Panthera tigris). Objectives We provide the first formal assessment of the relative impacts of scale-optimization and shape-optimization on model performance and habitat suitability predictions. We explored how optimization influences conclusions regarding habitat selection and mapped probability of occurrence. Methods We collated environmental variables expected to affect tiger occurrence, calculating focal statistics and landscape metrics at spatial scales ranging from 250 m to 16 km. We then constructed a set of presence–absence generalized linear models including: (1) single-scale optimized models (SSO); (2) a multi-scale optimized model (MSO); (3) single-scale shape-optimized models (SSSO) and (4) a multi-scale- and shape-optimized model (MSSO). We compared performance and resulting prediction maps for top performing models. Results The SSO (16 km), SSSO (16 km), MSO, and MSSO models performed equally well (AUC > 0.9). However, these differed substantially in prediction and mapped habitat suitability, leading to different ecological understanding and potentially divergent conservation recommendations. Habitat selection was highly scale-dependent and the strongest relationships with environmental variables were at the broadest scales analysed. Modelling approach had a substantial influence in variable importance among top models. Conclusions Our results suggest that optimization of the scale of resource selection is crucial in modelling tiger habitat selection. However, in this analysis, shape-optimization did not improve model performance.


2003 ◽  
Author(s):  
Michael A. Larson ◽  
William D. Dijak ◽  
Frank R. III Thompson ◽  
Joshua J. Millspaugh

Author(s):  
Ruiyu Fu ◽  
Zhonghua Zhang ◽  
Cong Hu ◽  
Xingbing Peng ◽  
Shaonuan Lu ◽  
...  

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Nabaz R. Khwarahm

Abstract Background The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species, Q. aegilops forest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI, Q. aegilops has been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions of Q. aegilops is minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records of Q. aegilops, and environmental variables. Results The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges of Q. aegilops would be reduced by 3.6% (1849.7 km2) and 3.16% (1627.1 km2), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km2) and 1.7% (848.0 km2), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes. Conclusions The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.


2021 ◽  
Author(s):  
Qifang He ◽  
Kai Jiang ◽  
Weicheng Hou ◽  
Yang Zhao ◽  
Xinhang Sun ◽  
...  

2021 ◽  
Author(s):  
Francesco Cerasoli ◽  
Aurélien Besnard ◽  
Marc‐Antoine Marchand ◽  
Paola D'Alessandro ◽  
Mattia Iannella ◽  
...  

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