Can gypsophytes distinguish different types of gypsum habitats?

Calvente, María Encarna Merlo; Carrasco, Carlos Gil de; Gómez, Ana Jose Sola; Jiménez-Sánchez, María L.; Rodríguez-Tamayo, María Luisa; Poveda, Juan Francisco Mota

doi:10.1080/12538078.2009.10516142

Cited by 18 publications

(9 citation statements)

References 34 publications

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“…RCP 8.5 assumes that the global CO 2 emissions would increase at a higher rate during the first half of the century and stabilize by 2100. However, the concentrations are three times those in 2000 (Calvente et al 2009; van Vuuren et al 2011; Wayne 2013; Rogelj 2012).…”

Section: Future Climatic Datamentioning

confidence: 80%

Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

2020

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Background The habitat resources are structured across different spatial scales in the environment, and thus animals perceive and select habitat resources at different spatial scales. Failure to adopt the scale-dependent framework in species habitat relationships may lead to biased inferences. Multi-scale species distribution models (SDMs) can thus improve the predictive ability as compared to single-scale approaches. This study outlines the importance of multi-scale modeling in assessing the species habitat relationships and may provide a methodological framework using a robust algorithm to model and predict habitat suitability maps (HSMs) for similar multi-species and multi-scale studies. Results We used a supervised machine learning algorithm, random forest (RF), to assess the habitat relationships of Asiatic wildcat (Felis lybica ornata), jungle cat (Felis chaus), Indian fox (Vulpes bengalensis), and golden-jackal (Canis aureus) at ten spatial scales (500–5000 m) in human-dominated landscapes. We calculated out-of-bag (OOB) error rates of each predictor variable across ten scales to select the most influential spatial scale variables. The scale optimization (OOB rates) indicated that model performance was associated with variables at multiple spatial scales. The species occurrence tended to be related strongest to predictor variables at broader scales (5000 m). Multivariate RF models indicated landscape composition to be strong predictors of the Asiatic wildcat, jungle cat, and Indian fox occurrences. At the same time, topographic and climatic variables were the most important predictors determining the golden jackal distribution. Our models predicted range expansion in all four species under future climatic scenarios. Conclusions Our results highlight the importance of using multiscale distribution models when predicting the distribution and species habitat relationships. The wide adaptability of meso-carnivores allows them to persist in human-dominated regions and may even thrive in disturbed habitats. These meso-carnivores are among the few species that may benefit from climate change.

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Section: Future Climatic Datamentioning

confidence: 80%

Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

2020

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“…Microreserves must be established in areas with high biodiversity and they should include plant communities or habitats listed in the Directive 92/43/EEC (Laguna et al, 2004). This flora could be less vulnerable than other types to the effects of climate change and consequently there are implicit benefits from its preservation, as suggested by ecophysiological studies of Gypsophila struthium L., whose photosynthetic activity is hardly affected under conditions that severely affect other species (Merlo Calvente et al, 2009). In addition, it would complement the protected area network in enhancing ecological awareness and promoting the natural values of these arid zones, which support flexible and stress-tolerant taxa, particularly of endemic plants that have undergone dramatic environmental changes and population bottlenecks in the past (Allen et al, 1999).…”

Section: Discussionmentioning

confidence: 99%

Threatened plants of arid ecosystems in the Mediterranean Basin: a case study of the south-eastern Iberian Peninsula

Mendoza-Fernández

Pérez-García

Martínez-Hernández

et al. 2014

Oryx

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Networks of protected areas are one of the main strategies used to address the biodiversity crisis. These should encompass as many species and ecosystems as possible, particularly in territories with high biological diversity, such as the Spanish arid zones. We produce a priority ranking of the arid zones of south-east Spain according to the rarity and richness of their characteristic flora and the level of endangerment. The resulting hierarchy shows that optimal zones for the preservation of the flora are located outside the network of protected areas. In particular, it is important to extend the network and encourage the creation of microreserves in the depression of the River Guadiana Menor (Granada), where there is least protection. This river valley is a particularly important arid site because of its unique flora and fauna, and palaeontological and archaeological findings.This paper contains supplementary material that can be found online at

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“…The RCP 2.6 assumes that global CO 2 emissions would peak around 2020 and then fall to values around zero by 2080 and RCP 8.5 is regarded as the worst climatic scenario with higher predicted greenhouse gas emissions. RCP 8.5 assumes that the global CO 2 emissions would increase at a higher rate during the first half of the century and stabilize by 2100; the concentrations are however three times those in 2000 90 – 93 .…”

Section: Methodsmentioning

confidence: 99%

Multi-scale habitat modelling and predicting change in the distribution of tiger and leopard using random forest algorithm

Rather

Kumar

Khan

2020

Sci Rep

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tigers and leopards have experienced considerable declines in their population due to habitat loss and fragmentation across their historical ranges. Multi-scale habitat suitability models (HSM) can inform forest managers to aim their conservation efforts at increasing the suitable habitat for tigers by providing information regarding the scale-dependent habitat-species relationships. However the current gap of knowledge about ecological relationships driving species distribution reduces the applicability of traditional and classical statistical approaches such as generalized linear models (GLMs), or occupancy surveys to produce accurate predictive maps. This study investigates the multiscale habitat relationships of tigers and leopards and the impacts of future climate change on their distribution using a machine-learning algorithm random forest (Rf). the recent advancements in the machine-learning algorithms provide a powerful tool for building accurate predictive models of species distribution and their habitat relationships even when little ecological knowledge is available about the species. We collected species occurrence data using camera traps and indirect evidence of animal presences (scats) in the field over 2 years of rigorous sampling and used a machine-learning algorithm random forest (Rf) to predict the habitat suitability maps of tiger and leopard under current and future climatic scenarios. We developed niche overlap models based on the recently developed statistical approaches to assess the patterns of niche similarity between tigers and leopards. tiger and leopard utilized habitat resources at the broadest spatial scales (28,000 m). Our model predicted a 23% loss in the suitable habitat of tigers under the RCP 8.5 Scenario (2050). Our study of multi-scale habitat suitability modeling provides valuable information on the species habitat relationships in disturbed and human-dominated landscapes concerning two large felid species of conservation importance. these areas may act as refugee habitats for large carnivores in the future and thus should be the focus of conservation importance. this study may also provide a methodological framework for similar multi-scale and multi-species monitoring programs using robust and more accurate machine learning algorithms such as random forest. Tigers and leopards are two large carnivore species of conservation importance occurring in sympatry across much of their range in India. The nationwide tiger census conducted by Govt. of India after every 4 years has shown a gradual increase in the tiger population across many protected areas. However, a significant proportion of the tiger population still occurs in fragmented landscapes outside the conventional protected areas 1,2. Smallsized protected areas, increased habitat fragmentation, and high anthropogenic pressure on the remaining intact habitats increase the likelihood of tiger populations becoming more isolated and thereby restricting the potential dispersal opportunities 3. Tigers and leopards are wide-rangi...

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Can gypsophytes distinguish different types of gypsum habitats?

Cited by 18 publications

References 34 publications

Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

Threatened plants of arid ecosystems in the Mediterranean Basin: a case study of the south-eastern Iberian Peninsula

Multi-scale habitat modelling and predicting change in the distribution of tiger and leopard using random forest algorithm

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