Mapping the diversity of maize races in Mexico.

Traditional landraces of maize are cultivated throughout more than one-half of Mexico's cropland. Efforts to organize in situ conservation of this important genetic resource have been limited by the lack of knowledge of regional diversity patterns. We used recent and historic collections of maize classified for race type to determine biogeographic regions and centers of landrace diversity. We also analyzed how diversity has changed over the last sixty years. Based on racial composition of maize we found that Mexico can be divided into 11 biogeographic regions. Six of these biogeographic regions are in the center and west of the country and contain more than 90% of the reported samples for 38 of the 47 races studied; these six regions are also the most diverse. We found no evidence of rapid overall decline in landrace diversity for this period. However, several races are now less frequently reported and two regions seem to support lower diversity than in previous collection periods. Our results are consistent with a previous hypothesis for diversification centers and for migration routes of original maize populations merging in western central Mexico. We provide maps of regional diversity patterns and landrace based biogeographic regions that may guide efforts to conserve maize genetic resources

Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods

Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of NTFPs is represented in the model as the conversion of one form of capital asset into another, which is influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are determined by the availability of the five types of assets following commercialization. The model, implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes of success and failure in NTFP commercialization, and can be used to explore the potential impacts of policy options and other interventions on livelihoods. The potential value of this approach for the development of NTFP theory is discussed

Correction: Drivers of deforestation in the basin of the Usumacinta River: Inference on process from pattern analysis using generalised additive models.

[This corrects the article DOI: 10.1371/journal.pone.0222908.]

Drivers of deforestation in the basin of the Usumacinta River: Inference on process from pattern analysis using generalised additive models.

Quantifying patterns of deforestation and linking these patterns to potentially influencing variables is a key component of modelling and projecting land use change. Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Simplifications of cause-consequence relationships that are difficult to support empirically may influence environment and development policies because they suggest simple solutions to complex problems. Deforestation is a complex process driven by multiple proximate and underlying factors and a range of scales. In this study we use a multivariate statistical analysis to provide contextual explanation for deforestation in the Usumacinta River Basin based on partial pattern matching. Our approach avoided testing trivial null hypotheses of lack of association and investigated the strength and form of the response to drivers. As not all factors involved in deforestation are easily mapped as GIS layers, analytical challenges arise due to lack of a one to one correspondence between mappable attributes and drivers. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. We developed a series of informative generalised additive models based on combinations of layers that corresponded to hypotheses regarding processes. The importance of the variables representing accessibility was emphasised by the analysis. We provide evidence that land tenure is a critical factor in shaping the decision to deforest and that direct beam insolation has an effect associated with fire frequency and intensity. The effect of winter insolation was found to have many applied implications for land management. The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. It was an informative approach, thus allowing the construction of a comprehensive understanding of its causes

Towards a Global Tree Assessment

Although trees have high economic, cultural and ecological value, increasing numbers of species are potentially at risk of extinction because of forest loss and degradation as a result of human activities, including overharvesting, fire and grazing. Emerging threats include climate change and its interaction with the spread of pests and diseases. The impact of such threats on the conservation status of trees is poorly understood. Here we highlight the need to conduct a comprehensive conservation assessment of the world's tree species, building on previous assessments undertaken for the IUCN Red List. We suggest that recent developments in plant systematics, online databases, remote sensing data and associated analytical tools offer an unprecedented opportunity to conduct such an assessment. We provide an overview of how a Global Tree Assessment could be achieved in practice, through participative, open-access approaches to data sharing and evaluation

Species distribution modeling in the tropics: problems, potentialities, and the role of biological data for effective species conservation

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

Toward integrated analysis of human impacts on forest biodiversity: lessons from Latin America.

Although sustainable forest management (SFM) has been widely adopted as a policy and management goal, high rates of forest loss and degradation are still occurring in many areas. Human activities such as logging, livestock husbandry, crop cultivation, infrastructural development, and use of fire are causing widespread loss of biodiversity, restricting progress toward SFM. In such situations, there is an urgent need for tools that can provide an integrated assessment of human impacts on forest biodiversity and that can support decision making related to forest use. This paper summarizes the experience gained by an international collaborative research effort spanning more than a decade, focusing on the tropical montane forests of Mexico and the temperate rain forests of southern South America, both of which are global conservation priorities. The lessons learned from this research are identified, specifically in relation to developing an integrated modeling framework for achieving SFM. Experience has highlighted a number of challenges that need to be overcome in such areas, including the lack of information regarding ecological processes and species characteristics and a lack of forest inventory data, which hinders model parameterization. Quantitative models are poorly developed for some ecological phenomena, such as edge effects and genetic diversity, limiting model integration. Establishment of participatory approaches to forest management is difficult, as a supportive institutional and policy environment is often lacking. However, experience to date suggests that the modeling toolkit approach suggested by Sturvetant et al. (2008) could be of value in such areas. Suggestions are made regarding desirable elements of such a toolkit to support participatory-research approaches in domains characterized by high uncertainty, including Bayesian Belief Networks, spatial multi-criteria analysis, and scenario planning.Most of the research described here was undertaken in three projects supported by the European Commission (INCO programme), namely SUCRE (ERBIC18CT970146), BIOCORES (ICA4- CT-2001-10095), and ReForLan (INCO-DEV-3 N° 032132), and three Darwin Initiative (DEFRA, UK Government) grants to the senior author. Additional funding was provided by a variety of sources within the partner countries. All sources of financial support are gratefully acknowledged

Toward integrated analysis of human impacts on forest biodiversity: lessons from Latin America.

Although sustainable forest management (SFM) has been widely adopted as a policy and management goal, high rates of forest loss and degradation are still occurring in many areas. Human activities such as logging, livestock husbandry, crop cultivation, infrastructural development, and use of fire are causing widespread loss of biodiversity, restricting progress toward SFM. In such situations, there is an urgent need for tools that can provide an integrated assessment of human impacts on forest biodiversity and that can support decision making related to forest use. This paper summarizes the experience gained by an international collaborative research effort spanning more than a decade, focusing on the tropical montane forests of Mexico and the temperate rain forests of southern South America, both of which are global conservation priorities. The lessons learned from this research are identified, specifically in relation to developing an integrated modeling framework for achieving SFM. Experience has highlighted a number of challenges that need to be overcome in such areas, including the lack of information regarding ecological processes and species characteristics and a lack of forest inventory data, which hinders model parameterization. Quantitative models are poorly developed for some ecological phenomena, such as edge effects and genetic diversity, limiting model integration. Establishment of participatory approaches to forest management is difficult, as a supportive institutional and policy environment is often lacking. However, experience to date suggests that the modeling toolkit approach suggested by Sturvetant et al. (2008) could be of value in such areas. Suggestions are made regarding desirable elements of such a toolkit to support participatory-research approaches in domains characterized by high uncertainty, including Bayesian Belief Networks, spatial multi-criteria analysis, and scenario planning.Most of the research described here was undertaken in three projects supported by the European Commission (INCO programme), namely SUCRE (ERBIC18CT970146), BIOCORES (ICA4- CT-2001-10095), and ReForLan (INCO-DEV-3 N° 032132), and three Darwin Initiative (DEFRA, UK Government) grants to the senior author. Additional funding was provided by a variety of sources within the partner countries. All sources of financial support are gratefully acknowledged

Author Roger Bivand [cre, aut],Nicholas Lewin-

Description Set of tools for manipulating and reading geographic data, in particular ESRI shapefiles; C code used from shapelib. It includes binary access to GSHHG shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as PB-Smapping, spatstat, maps, RArcInfo, Stata tmap, WinBUGS, Mondrian, and others. License GPL (> = 2