Duncan Golicher

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 Unpublished data from two major tropical case studies showed that those species that are most in need of conservation actions, namely those that are the rarest or most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.

Remote sensing and the future of landscape ecology

Landscape ecology focuses on the analysis of spatial pattern and its relationship to ecological processes. As a scientific discipline, landscape ecology has grown rapidly in recent years, supported by developments in GIS and spatial analysis techniques. Although remote sensing data are widely employed in landscape ecology research, their current and potential roles have not been evaluated critically. To provide an overview of current practice, 438 research papers published in the journal Landscape Ecology for the years 2004—2008 were examined for information about use of remote sensing. Results indicated that only 36% of studies explicitly mentioned remote sensing. Of those that did so, aerial photographs and Landsat satellite sensor images were most commonly used, accounting for 46% and 42% of studies, respectively. The predominant application of remote sensing data across these studies was for thematic mapping purposes. This suggests that landscape ecologists have been relatively slow to recognize the potential value of recent developments in remote sensing technologies and methods. The review also provided evidence of a frequent lack of key detail in studies recently published in Landscape Ecology , with 75% failing to provide any assessment of uncertainty or error relating to image classification and mapping. It is suggested that the role of remote sensing in landscape ecology might be strengthened by closer collaboration between researchers in the two disciplines, by greater integration of diverse remote sensing data with ecological data, and by increased recognition of the value of remote sensing beyond land-cover mapping and pattern description. This is illustrated by case studies drawn from Latin America (focusing on forest loss and fragmentation) and the UK (focusing on habitat quality for woodland birds). Such approaches might improve the analytical and theoretical rigour of landscape ecology, and be applied usefully to issues of outstanding societal interest, such as the impacts of environmental change on biodiversity and ecosystem services.

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.

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.

Pseudo-absences, pseudo-models and pseudo-niches: pitfalls of model selection based on the area under the curve

The area under the curve (AUC) of the receiver operator characteristic (ROC) graph is regarded as an objective measure of the discrimination accuracy of predictive models. AUC scores calculated from background values, or pseudo-absences, have been proposed as a method of model selection for species distribution models (SDMs) fitted to presence-only data. However, the utility of AUC as a measure of model performance when data on confirmed absence are unavailable has not been fully investigated. We fitted SDMs using informative climatic variables for 2000 species of Mesoamerican trees. As a reference, we also built ‘pseudo-models’ using Gaussian random fields with no biological meaning. AUC correctly selected SDMs fitted to single environmental variables over ‘pseudo-models’ fitted to single random fields in almost all cases. However, when all seven variables were included in the models, AUC erroneously selected complex pseudo-models over complex climate models in 17% of the cases. The spatial distribution patterns predicted by the pseudo-models differed from the results derived from climate-based models, even when overall AUC scores were similar. Both model and pseudo-model AUC values increased when presence points were few and spatially aggregated. The results show that AUC calculated from presence-only data can be an unreliable guide for model selection. Pseudo-absences have ill-defined properties that challenge the interpretation of AUC values. Inference on multidimensional niche spaces should not be supported by AUC values calculated using pseudo-absences.

Mapping the Diversity of Maize Races in Mexico

Traditional landraces of maize are cultivated throughout more than one-half of Mexicos 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.

Evidence of Incipient Forest Transition in Southern Mexico

Case studies of land use change have suggested that deforestation across Southern Mexico is accelerating. However, forest transition theory predicts that trajectories of change can be modified by economic factors, leading to spatial and temporal heterogeneity in rates of change that may take the form of the Environmental Kuznets Curve (EKC). This study aimed to assess the evidence regarding potential forest transition in Southern Mexico by classifying regional forest cover change using Landsat imagery from 1990 through to 2006. Patterns of forest cover change were found to be complex and non-linear. When rates of forest loss were averaged over 342 municipalities using mixed-effects modelling the results showed a significant (p<0.001) overall reduction of the mean rate of forest loss from 0.85% per year in the 1990–2000 period to 0.67% in the 2000–2006 period. The overall regional annual rate of deforestation has fallen from 0.33% to 0.28% from the 1990s to 2000s. A high proportion of the spatial variability in forest cover change cannot be explained statistically. However analysis using spline based general additive models detected underlying relationships between forest cover and income or population density of a form consistent with the EKC. The incipient forest transition has not, as yet, resulted in widespread reforestation. Forest recovery remains below 0.20% per year. Reforestation is mostly the result of passive processes associated with reductions in the intensity of land use. Deforestation continues to occur at high rates in some focal areas. A transition could be accelerated if there were a broader recognition among policy makers that the regional rate of forest loss has now begun to fall. The changing trajectory provides an opportunity to actively restore forest cover through stimulating afforestation and stimulating more sustainable land use practices. The results have clear implications for policy aimed at carbon sequestration through reducing deforestation and enhancing forest growth.

Lifting A Veil On Diversity: A Bayesian Approach To Fitting Relative‐Abundance Models

Bayesian methods incorporate prior knowledge into a statistical analysis. This prior knowledge is usually restricted to assumptions regarding the form of probability distributions of the parameters of interest, leaving their values to be determined mainly through the data. Here we show how a Bayesian approach can be applied to the problem of drawing inference regarding species abundance distributions and comparing diversity indices between sites. The classic log series and the lognormal models of relative- abundance distribution are apparently quite different in form. The first is a sampling distribution while the other is a model of abundance of the underlying population. Bayesian methods help unite these two models in a common framework. Markov chain Monte Carlo simulation can be used to fit both distributions as small hierarchical models with shared common assumptions. Sampling error can be assumed to follow a Poisson distribution. Species not found in a sample, but suspected to be present in the region or community of interest, can be given zero abundance. This not only simplifies the process of model fitting, but also provides a convenient way of calculating confidence intervals for diversity indices. The method is especially useful when a comparison of species diversity between sites with different sample sizes is the key motivation behind the research. We illustrate the potential of the approach using data on fruit-feeding butterflies in southern Mexico. We conclude that, once all assumptions have been made transparent, a single data set may provide support for the belief that diversity is negatively affected by anthropogenic forest disturbance. Bayesian methods help to apply theory regarding the distribution of abundance in ecological communities to applied conservation.

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 worlds 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.

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes.

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18–20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts.