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Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation:

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TLDR
SDMs have a great potential to support biodiversity conservation in the tropics, by supporting the development of conservation strategies and plans, identifying knowledge gaps, and providing a tool to examine the potential impacts of environmental change, but for this potential to be fully realized, problems of data quality and availability need to be overcome.
Abstract
In this paper we aim to investigate the problems and potentialities of species distribution modeling (SDM) as a tool for conservation planning and policy development and implementation in tropical regions. We reviewed 123 studies published between 1995 and 2007 in five of the leading journals in ecology and conservation, and examined two tropical case studies in which distribution modeling is currently being applied to support conservation planning. We also analyzed the characteristics of data typically used for fitting models within the specific context of modeling tree species distribution in Central America. The results showed that methodological papers outnumbered reports of SDMs being used in an applied context for setting conservation priorities, particularly in the tropics. Most applications of SDMs were in temperate regions and biased towards certain organisms such as mammals and birds. Studies from tropical regions were less likely to be validated than those from temperate regions. Unpublished data from two major tropical case studies showed that those species that are most in need of conservation actions, namely those that are the rarest or most threatened, are those for which SDM is least likely to be useful. We found that only 15% of the tree species of conservation concern in Central America could be reliably modelled using data from a substantial source (Missouri Botanical Garden VAST database). Lack of data limits model validation in tropical areas, further restricting the value of SDMs. We concluded that SDMs have a great potential to support biodiversity conservation in the tropics, by supporting the development of conservation strategies and plans, identifying knowledge gaps, and providing a tool to examine the potential impacts of environmental change. However, for this potential to be fully realized, problems of data quality and availability need to be overcome. Weaknesses in current biological datasets need to be systematically addressed, by increasing collection of field survey data, improving data sharing and increasing structural integration of data sources. This should include use of distributed databases with common standards, referential integrity, and rigorous quality control. Integration of data management with SDMs could significantly add value to existing data resources by improving data quality control and enabling knowledge gaps to be identified.

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Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes

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Global priorities for an effective information basis of biodiversity distributions.

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Accounting for uncertainty when mapping species distributions: The need for maps of ignorance

TL;DR: In this paper, the main sources of uncertainty are reviewed and a code of good practices is proposed to minimize their effects. But the authors do not consider the effect of the quality and bias of raw distributional data, the process of map building and the dynamic nature of species distributions themselves.
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taxonstand: An r package for species names standardisation in vegetation databases

TL;DR: Taxonstand, a nr package to automatically standardise plant names using The Plant List is presented, which greatly facilitates the preparation of large vegetation databases prior to their analyses, particularly when they cover broad geographical areas or contain data from regions with rich floras where taxonomic problems have not been resolved for many of their taxa.
References
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Journal ArticleDOI

Biodiversity hotspots for conservation priorities

TL;DR: A ‘silver bullet’ strategy on the part of conservation planners, focusing on ‘biodiversity hotspots’ where exceptional concentrations of endemic species are undergoing exceptional loss of habitat, is proposed.
Journal ArticleDOI

Maximum entropy modeling of species geographic distributions

TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Journal ArticleDOI

Predictive habitat distribution models in ecology

TL;DR: A review of predictive habitat distribution modeling is presented, which shows that a wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management.
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