Luis Cayuela

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 Unpublished data from two major tropical case studies showed that those species that are most in need of conservation actions, namely those that are the rarest or most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

Classification of a complex landscape using Dempster-Shafer theory of evidence

The landscape of the Highlands of Chiapas, southern Mexico, is covered by a highly complex mosaic of anthropogenic, natural and semi‐natural vegetation. This complexity challenges land cover classification based on remotely sensed data alone. Spectral signatures do not always provide the basis for an unambiguous separation of pixels into classes. Expert knowledge does, however, provide additional lines of evidence that can be employed to modify the belief that a pixel belongs to a certain coverage class. We used Dempster–Shafer (DS) weight of evidence modelling to incorporate this information into the classification process in a formal manner. Expert knowledge‐based variables were related to: (1) altitude, (2) slope, (3) distance to known human settlements and (4) landscape perceptions regarding dominance of vegetation types. The results showed an improvement of classification results compared with traditional classifiers (maximum likelihood) and context operators (modal filters), leading to better discrimination between categories and (i) a decrease in errors of omission and commission for almost all classes and (ii) a decrease in total error of around 7.5%. The DS approach led not only to a more accurate classification but also to a richer description of the inherent uncertainty surrounding it.

Is ground cover vegetation an effective biological control enhancement strategy against olive pests

Ground cover vegetation is often added or allowed to generate to promote conservation biological control, especially in perennial crops. Nevertheless, there is inconsistent evidence of its effectiveness, with studies reporting positive, nil or negative effects on pest control. This might arise from differences between studies at the local scale (e.g. orchard management and land use history), the landscape context (e.g. presence of patches of natural or semi-natural vegetation near the focal orchard), or regional factors, particularly climate in the year of the study. Here we present the findings from a long-term regional monitoring program conducted on four pest species (Bactrocera oleae, Prays oleae, Euphyllura olivina, Saissetia oleae) in 2,528 olive groves in Andalusia (Spain) from 2006 to 2012. Generalized linear mixed effect models were used to analyze the effect of ground cover on different response variables related to pest abundance, while accounting for variability at the local, landscape and regional scales. There were small and inconsistent effects of ground cover on the abundance of pests whilst local, landscape and regional variability explained a large proportion of the variability in pest response variables. This highlights the importance of local and landscape-related variables in biological control and the potential effects that might emerge from their interaction with practices, such as groundcover vegetation, implemented to promote natural enemy activity. The study points to perennial vegetation close to the focal crop as a promising alternative strategy for conservation biological control that should receive more attention.

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.

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.

Effects of Land use on Nocturnal Birds in a Mediterranean Agricultural Landscape

Abstract. Knowledge on the effects of land use on community composition and species abundance is crucial for designing realistic conservation strategies, particularly in highly dynamic systems such as Mediterranean agricultural mosaics that are subjected to intensive cultivation. We investigated these effects on the nocturnal bird species occurring in the study area (Stone Curlew Burhinus oedicnemus, Red-necked Nightjar Caprimulgus ruflcollis, Barn Owl Tyto alba, Eurasian Scops Owl Otus scops, Little Owl Athene noctua, Tawny Owl Strix aluco, Long-eared Owl Asio otus, Short-eared Owl Asio flammeus and Eagle Owl Bubo bubo) across an agricultural-natural habitat mosaic in Central Spain for three consecutive years. Shares of vineyards, scrubland, herbaceous cropland, water bodies, and roads significantly affected the composition of the nocturnal bird community. Herbaceous cropland and olive groves, which covered 50% of the study area, proved to be neutral for all species. Remnant patches of natural and semi-natural scrubland (around 10% of the study area) and water bodies (only 1.5% of the study area) showed a positive effect on Eagle Owls, Eurasian Scops Owls, Long-eared Owls, and Red-necked Nightjars. Vineyard (35% of the study area) had a negative influence on Eagle Owls, Long-eared Owls, and Eurasian Scops Owls. Our results indicate, first, that the relative extent of land use types was apparently not related with the presence of nocturnal bird species and, second, that natural scrublands and water bodies are key habitats for assuring the persistence of nocturnal birds in agricultural Mediterranean landscapes. Current land planning focused toward land use intensification will likely increase the areas of habitats that are neutral or have adverse effects on nocturnal birds.

Effects of climate change on subtropical forests of South America.

Premontane forest in northern Argentina and southern Bolivia represents a conservation priority due to its biological values, role of connectivity among different forest types, and precious timber resources. Premontane forest distribution has fluctuated in correspondence to habitat use and changes in climatic conditions. The objective of this study was to determine current and future distributions of premontane forest and of six distinctive tree species in response to climate change, and to relate distribution changes to the current system of protected areas. Using the Maxent program, we developed species distribution models at the community and species levels. We used future climate scenarios available at WorldClim, in its original version and calibrated with local data. Future models determined a retraction of premontane forest of about 40% and a general tendency of this environment to migrate toward higher altitudes. Future distribution of individual species showed a similar response although concentrated at some particular areas, suggesting a shift in tree species composition of premontane forest in the future. The Yungas Biosphere Reserve represents a stable protection area for premontane forest.