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Osipova L.,Clark University | Sangermano F.,Clark Labs
Journal for Nature Conservation | Year: 2016

The Amazon rainforest covers more than 60% of Bolivia's lowlands, providing habitat for many endemic and threatened species. Bolivia has the highest rates of deforestation of the Amazon biome, which degrades and fragments species habitat. Anthropogenic habitat changes could be exacerbated by climate change, and therefore, developing relevant strategies for biodiversity protection under global change scenarios is a necessary step in conservation planning. In this research we used multi-species umbrella concept to evaluate the degree of habitat impacts due to climate and land cover change in Bolivia. We used species distribution modeling to map three focal species (Jaguar, Lowland Tapir and Lesser Anteater) and assessed current protected area network effectiveness under future climate and land cover change scenarios for 2050. The studied focal species will lose between 70% and 83% of their ranges under future climate and land-cover change scenarios, decreasing the level of protection to 10% of their original ranges. Existing protected area network should be reconsidered to maintain current and future biodiversity habitats. © 2016 Elsevier GmbH


Sangermano F.,Clark Labs | Eastman J.R.,Clark Labs | Zhu H.,Clark Labs
Transactions in GIS | Year: 2010

Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance-based learning algorithm based on the logic of the K-Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi-Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform. © 2010 Blackwell Publishing Ltd.


Eastman J.R.,Clark Labs | Eastman J.R.,Clark University | Sangermano F.,Clark Labs | Sangermano F.,Clark University | And 3 more authors.
Remote Sensing | Year: 2013

A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period. © 2013 by the authors.


Simonti A.L.,Clark Labs | Eastman J.R.,Clark Labs
Journal of Geophysical Research: Oceans | Year: 2010

This study examined the effects of climate teleconnections on the massive Caribbean coral bleaching and mortality event of 2005. A relatively new analytical procedure known as empirical orthogonal teleconnection (EOT) analysis, based on a 26 year monthly time series of observed sea surface temperature (SST), was employed. Multiple regression analysis was then utilized to determine the relative teleconnection contributions to SST variability in the southern Caribbean. The results indicate that three independent climate teleconnections had significant impact on southern Caribbean anomalies in SST and that their interaction was a major contributor to the anomalously high temperatures in 2005. The primary and approximately equal contributors were EOT-5 and EOT-2, which correlate most strongly with the tropical North Atlantic (TNA) and Atlantic multidecadal oscillation (AMO) climate indices, respectively. The third, EOT-9, was most strongly related to the Atlantic meridional mode. However, although statistically significant, the magnitude of its contribution to southern Caribbean variability was small. While there is debate over the degree to which the recent AMO pattern represents natural variability or global ocean warming, the results presented here indicate that natural variability played a strong role in the 2005 coral bleaching conditions. They also argue for a redefinition of the geography of TNA variability. Copyright 2010 by the American Geophysical Union.


Machado-Machado E.A.,Clark University | Machado-Machado E.A.,Clark Labs
Applied Geography | Year: 2012

Dengue is considered the most important vector borne virus disease worldwide placing some 2.5 billion people at risk globally. Despite the public health concern about dengue fever, spatially explicit suitability assessments for this disease are limited due to data restrictions and the challenges posed by the complexity of the interactions among its risk factors, which involve social, economic, and ecological processes. This paper demonstrates an empirical approach to identify suitable areas for dengue fever using species distribution modeling and evaluates the relative contribution of climatic and socio-economic factors as dengue fever suitability determinants. Several models showing the potential distribution of dengue fever within all the Mexican municipalities are produced using different sets of predictor variables. The results suggest that at the scale of this study the climatic variables were more important determinants of suitability for dengue fever than the socio-economic variables considered in this study. All the models perform well (average testing AUC about 0.8) and show similar patterns. The model with the least number of variables and best performance includes the variables minimum temperature of the coldest month, mean temperature of the wettest quarter, and annual precipitation. However, there is not a high variability of AUC scores among the models generated. © 2011 Elsevier Ltd.


Chen H.,Clark Labs | Pontius Jr. R.G.,Clark University
Landscape Ecology | Year: 2010

This paper proposes a method to quantify the goodness-of-fit of a land change projection along a gradient of an explanatory variable, by classifying pixels as one of four types: null successes, false alarms, hits, and misses. The method shows: (1) how the correctness and error of a land change projection are distributed along the gradient of an explanatory variable, (2) how the gradient of the explanatory variable relates to the stationarity of the land transition processes, and (3) how to use the insights from the previous two points to search for additional explanatory variables. The paper illustrates the method through a case study that applies the model Geomod in Central Massachusetts, USA. Results reveal that the model predicts more than the observed amount of change on flat slopes and less than the observed amount of change on steep slopes. One reason for these types of errors is that the land change process during the calibration interval is different than the process during the prediction interval with respect to slope. The method allows modelers to use the validation step as a diagnostic tool to search for potentially influential missing variables and to gain insight into land transition processes. The technique is designed to be applicable to a variety of types of land change models. © 2010 Springer Science+Business Media B.V.


Sangermano F.,Clark Labs | Toledano J.,Clark Labs | Eastman R.,Clark Labs
Landscape Ecology | Year: 2012

Tropical deforestation is a major contributor to green house gas emissions in developing countries. Incentive mechanisms, such as reducing emissions from deforestation and forest degradation (REDD), are currently being considered as a possible emissions reduction and offset solution. Although REDD has expanded its scope to include co-benefits such as sustainable management of forests and biodiversity conservation (known as REDD+), current carbon-base methodologies do not specifically target projects for the parallel protection of these co-benefits. This study demonstrates the incorporation of both carbon and biodiversity benefits within REDD+ in the Bolivian Amazon, through the analysis of land cover change and future change scenario modeling to the year 2050. Current protected areas within the Bolivian Amazon were evaluated for REDD+ project potential by identifying concordant patterns of carbon content, species biodiversity and deforestation vulnerability. Biodiversity-based versus carbon-based protection schemes were evaluated and protected areas were prioritized using irreplaceability-vulnerability plots. Deforestation projection scenarios to the year 2050 varied depending on the historical period analyzed, producing low, intermediate and high deforestation scenarios. All scenarios showed increasing deforestation pressure in the northern region of Bolivia along with high levels of biodiversity loss. Expected reductions in the carbon pool ranged from 8 to 48%, for the low and high demand scenarios respectively. Some protected areas presented large numbers of endemic species, high concentrations of carbon and high deforestation vulnerability, demonstrating the potential for win-win REDD+ projects in Bolivia. © 2012 Springer Science+Business Media B.V.


Sangermano F.,Clark Labs | Eastman J.R.,Clark Labs
International Journal of Geographical Information Science | Year: 2012

The archives of species range polygons developed under comprehensive assessments of the conservation status of species, such as the IUCN's Global Assessments, are a significant resource in the analysis of biodiversity for conservation planning. Species range polygons obtained from these studies are known to exhibit omissions (because of knowledge gaps) and imprecision in their boundaries. In this work, we present a method to refine those species range polygons in order to create more realistic representations of species geographic ranges. Using range polygons of four species of mammals in South America and environmental variables at a 1 km resolution, combined with a set of GIS algorithms, a procedure was developed to map the confidence that sub-polygon elements belong to a logical species range. The confidence map is then used as a weight for a Mahalanobis typicality empirical modelling procedure to generate a map of species-weighted typicalities that is then thresholded to generate the refined species range map. Methods for variable selection and quality assessment of the refined range are also included in the procedure. Analysis using independent validation data shows the power of this methodology to redefine species ranges in a more biophysically reasonable way. The quality of the final-range map depends on the habitat suitability threshold used to define the species range. The report of quality assessment produced is useful for identifying not only the threshold that produces the highest match to the original expert range but also for flagging those ranges with higher discrepancies, facilitating the identification of ranges that need further revision. © 2012 Copyright Taylor and Francis Group, LLC.


Chen H.,Clark Labs | Pontius Jr. R.G.,Clark University
Environmental Modeling and Assessment | Year: 2011

It is important to know how the results from a land change model vary based on both the pixel resolution of the maps and the precision of the independent variables because subjective decisions or default values frequently determine these two factors. This paper presents an approach to measure the variation in model accuracy that is triggered by alteration of the pixel resolution and the precision of the independent variable, which are bins of distance to previously built area for our case study. We illustrate the principles with an application of the Geomod land change model contained in the Idrisi GIS, applied to simulate the gain of built land in central Massachusetts, USA. Results reveal four general principles: (1) change in pixel resolution using the majority-takes-all rule can influence quantity error, (2) change in bin width of an independent variable does not influence the quantity error, (3) resolution and bin width interact so that bin width does not have an effect on error when bin widths are smaller than the pixel resolution, and (4) researchers are wise to examine the implications of their subjective decisions by plotting clearly how the resolution and bin size influence the mathematical relationships that the model uses. We have found no universal, hard, and fast rules that dictate how to decide on an appropriate pixel resolution and bin width, but our method demonstrates how these decisions can be influential. These insights can offer scientists guidance in how to prepare data in an appropriate manner. © 2010 Springer Science+Business Media B.V.


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