Modelling tools to predict potential distribution of forest species : using Pico Island and the Azores as study case

Tese de Doutoramento, Biologia, 16 de novembro de 2018, Universidade dos Açores.Os modelos de distribuição de espécies (SDMs) têm sido aplicados em diferentes áreas da ecologia, nomeadamente para modelar a distribuição potencial de espécies invasoras, para avaliar espécies prioritárias no âmbito da conservação e para apoiar o planeamento florestal. Um SDM é uma descrição matemática da distribuição de uma espécie no espaço ambiental, a qual pode ser utilizada para prever a distribuição da espécie no espaço geográfico. O avanço ao nível da capacidade computacional disponibilizou uma diversidade de métodos estatísticos, que anteriormente não era possível utilizar. Esta diversidade de métodos reflete-se num número crescente de publicações direcionadas ao estudo e aplicação dos SDMs e também numa variedade crescente de métodos de modelação. Nos Açores, a abundância crescente de dados corológicos, a diversidade geomorfológica do arquipélago e os diferentes padrões espaciais que é possível encontrar em diferentes ilhas e em diferentes espécies, contribuem para que o arquipélago seja um bom modelo para a comparação de diferentes abordagens de modelação, bem como para testar possíveis constrangimentos inerentes ao processo de modelação. As perguntas de investigação a que pretendemos responder nesta tese foram as seguintes: (i) As abordagens de modelação, baseadas em diferentes fundamentos teóricos, originam resultados semelhantes, ao nível da distribuição potencial das espécies florestais estudadas? (ii) Existe alguma diferença relevante, entre o cálculo de Modelos Lineares Generalizados (GLMs) usando métodos de máxima verossimilhança ou métodos bayesianos? (iii) Existe alguma vantagem, no uso de um campo aleatório relativo à estrutura espacial dos dados, em comparação com os modelos que incluem apenas os efeitos fixos das variáveis ambientais? (iv) As diferentes abordagens de modelação originam resultados consistentes, em particular quando o número de variáveis ambientais utilizadas na modelação é reduzido? (v) As diferentes técnicas de modelação são afetadas de um modo relevante pela dimensão da amostra, pelo tipo de distribuição da espécie e pelas alterações no uso do solo? Para responder a estas questões, foram desenvolvidos três exercícios de modelação: (i) Uma comparação da Análise Fatorial do Nicho Ecológico (ENFA) e da modelação baseada na Máxima Entropia (MaxEnt), utilizando dados relativos à presença de três espécies (Pittosporum undulatum, Acacia melanoxylon e Morella faya) em três ilhas (Pico, Terceira e São Miguel), e incluindo o efeito da redução da dimensão da amostra; (ii) A comparação de modelos com efeitos fixos ou mistos, utilizando a plataforma R para o cálculo de GLMs e da aproximação de Laplace (INLA), permitindo o cálculo da estrutura espacial dos dados (função de covariância de Matérn), baseada em dados de duas ilhas (Pico e São Miguel) para duas espécies (P. undulatum e M. faya), e incluindo o efeito da redução da dimensão da amostra; e (iii) A comparação de GLMs e de uma seleção de algoritmos de autoaprendizagem (Machine Learning), usados para modelar as possíveis alterações nas áreas de distribuição de P. undulatum, A. melanoxylon e M. faya nas três ilhas, resultantes das alterações climáticas previstas para 2100. Em relação ao primeiro exercício, ambas as abordagens originaram cenários semelhantes, particularmente quando a quantidade de informação explicada pela ENFA era elevada; os resultados da modelação foram afetados pela redução do tamanho da amostra; os modelos com melhor capacidade de previsão incluíam um conjunto variado de variáveis ambientais (topográficas, climáticas e de uso do solo); e os modelos eram afetados pela transferência para um novo habitat (i.e. ilha). Os resultados do segundo exercício de modelação indicaram que os GLMs, calculados através de métodos de máxima verossimilhança ou métodos bayesianos originaram resultados similares, mesmo nos casos em que a dimensão da amostra era reduzida; e que a adição de um campo aleatório aumentou o ajustamento dos modelos, particularmente para a árvore menos abundante, M. faya, embora a estrutura do campo aleatório fosse claramente afetada pela dimensão da amostra. O terceiro exercício de modelação revelou que existem várias limitações quando se modela o efeito das alterações climáticas na distribuição das espécies, uma vez que os melhores modelos incluíram variáveis topográficas, demonstrando que a modelação baseada somente no clima poderá não ser fiável; verificou-se igualmente que o ajuste dos modelos variava de forma relevante entre as diferentes abordagens de modelação, e que o algoritmo Random Forest apresentou, em geral, os melhores resultados. De uma forma geral, os resultados desta investigação poderão ser aplicados como forma de apoio à gestão da floresta açoriana. Poderão ser replicados em outros sistemas insulares e noutras regiões florestais, não somente em projetos direcionados para a ecologia das espécies florestais, mas também em questões de investigação relacionadas com a previsão do sucesso e expansão das plantas invasoras, a deteção de áreas adequadas para projetos de restauro, a modelação baseada em dados de deteção remota e a modelação do efeito potencial das alterações climáticas.ABSTRACT: Species distribution models (SDMs) have been used in different areas within ecology, namely to model the potential spread of invasive species, to evaluate and manage priority species for conservation and to support forest management. An SDM is a mathematical description of the species distribution in the environmental space that can be used to predict the distribution of the species in the geographic space. The advances in computational capabilities have provided increasingly greater and more intensive statistical algorithms than was previously possible, as reflected by the increasing number of publications addressing SDMs and also the growing variety of modelling approaches. In the Azores, the growing abundance of the species distribution data, the diversity on island size and morphology, and the different spatial patterns that are possible among islands and species, make the archipelago a good model for the comparison of different modelling approaches and to test possible modelling constraints. Overall, the results of this research can be expanded to support Azorean forestry management, and could be replicated in other island systems and forest regions, not only in projects addressing the ecology of particular forest species, but also when handling research questions related with the prediction of plant invader success and expansion, the detection of areas potentially suited for restoration projects, modelling based on remote sense data, and modelling of the potential effect of climate change

Spatio-temporal risk assessment models for Lobesia botrana in uncolonized winegrowing areas

The objective of this work was to generate a series of equations to describe the voltinism of Lobesia botrana in the quarantine area of the main winemaking area of Argentina, Mendoza. To do this we considered an average climate scenario and extrapolatedthese equations to other winegrowing areas at risk of being invaded. A grid of 4 km2was used to generate statistics on L. botrana captures and the mean temperature accumulation for the pixel. Four sets of logistic regression were constructed using the percentage of accumulated trap catches/grid/week and the degree-day accumulation above7°C, from 1st July. By means of a habitat model, an extrapolation of the phenologicalmodel generated to other Argentine winemaking areas was evaluated. According to ourresults, it can be expected that 50% of male adult emergence for the first flight occurs at248.79 ± 4 degree-days (DD), in the second flight at 860.18 ± 4.1 DD, while in the thirdand the fourth flights, 1671.34 ± 5.8 DD and 2335.64 ± 4.3 DD, respectively. Subsequentclimatic comparison determined that climatic conditions of uncolonized areas of Cuyo Region have a similar suitability index to the quarantine area used to adjust the phenologicalmodel. The upper valley of Río Negro and Neuquén are environmentally similar. Valleys ofthe northwestern region of Argentina showed lower average suitability index and greatervariability among SI estimated by the algorithm considered. The combination of two models for the estimation of adult emergence time and potential distribution, can provide greater certainties in decision-making and risk assessment of invasive species.Fil: Heit, Guillermo Eugenio. Ministerio de Agricultura, Ganadería, Pesca y Alimento. Servicio Nacional de Sanidad y Calidad Agroalimentaria; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; ArgentinaFil: Aceñolaza, Pablo Gilberto. Universidad Nacional de Entre Ríos; Argentina. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentin

Defining Landscape Resistance Values in Least-Cost Connectivity Models for the Invasive Grey Squirrel: A Comparison of Approaches Using Expert-Opinion and Habitat Suitability Modelling

Least-cost models are widely used to study the functional connectivity of habitat within a varied landscape matrix. A critical step in the process is identifying resistance values for each land cover based upon the facilitating or impeding impact on species movement. Ideally resistance values would be parameterised with empirical data, but due to a shortage of such information, expert-opinion is often used. However, the use of expert-opinion is seen as subjective, human-centric and unreliable. This study derived resistance values from grey squirrel habitat suitability models (HSM) in order to compare the utility and validity of this approach with more traditional, expert-led methods. Models were built and tested with MaxEnt, using squirrel presence records and a categorical land cover map for Cumbria, UK. Predictions on the likelihood of squirrel occurrence within each land cover type were inverted, providing resistance values which were used to parameterise a leastcost model. The resulting habitat networks were measured and compared to those derived from a least-cost model built with previously collated information from experts. The expert-derived and HSM-inferred least-cost networks differ in precision. The HSM-informed networks were smaller and more fragmented because of the higher resistance values attributed to most habitats. These results are discussed in relation to the applicability of both approaches for conservation and management objectives, providing guidance to researchers and practitioners attempting to apply and interpret a leastcost approach to mapping ecological networks.This project was funded by the Forestry Commission GB and the National School of Forestry at the University of Cumbria. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Nonindigenous Aquatic Species

Online resource center, maintained by U.S.G.S., provides information, data, links about exotic plants, invertebrates, vertebrates, diseases and parasites. Central repository contains accurate and spatially referenced biogeographic accounts of alien aquatic species. Search for species by state, drainage area, citation in texts; find fact sheets, maps showing occurrence in the U.S. Or, for each taxon, review list of exotic species, find scientific, common name, photo, status; link to facts and distribution map. Educational levels: General public, High school

Predicting the current distribution of the chacoan peccary (catagonus wagneri) in the gran Chaco

The Chacoan peccary (Catagonus wagneri), or Tagua, an endemic species living in the Chaco eco¬region, is endangered by highly increasing deforestation rates across the region, particularly in the last decade. This situation highlights the need to better understand the current distribution of the species, as well as how environmental conditions affect habitat suitability. This study predicts the distribution of the Chacoan peccary and evaluates the current environmental conditions in the Chaco for this species. Using six environmental variables and 177 confirmed occurrence records (from 2000 to 2015) provided by researchers, we developed a Species Distribution Model (SDM) applying the Maxent algorithm. The final model was highly accurate and significant (p < 0.001; AUC 0.860 ± 0.0268; omission error 1.82 %; post¬hoc validation of omission error using independent presence¬only records 1.33 %), predicting that 46.24 % of the Chaco is suitable habitat for the Chacoan peccary, with the most important areas concentrated in the middle of Paraguay and northern Argentina. Land cover, isothermality and elevation were the variables that better explained the habitat suitability for the Chacoan peccary. Despite some portions of suitable areas occurring inside protected areas, the borders and the central portions of suitable areas have recently suffered from intensive deforestation and development, and most of the highly suitable areas for the species are not under protection. The results provide fundamental insights for the establishment of priority Chacoan peccary conservation areas within its rangeFil: Paschoaletto Micchi, Katia Maria. Universidade Do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz Esalq; Brasil. Conservation Breeding Specialist Group Brazilian network; BrasilFil: Silva Angelieri, Cintia Camila. Universidade Do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz Esalq; BrasilFil: Altrichter, Mariana. Prescott College; Estados UnidosFil: Desbiez, Arnaud. Royal Zoological Society of Scotland. Edimburgo; Reino Unido. Conservation Breeding Specialist Group Brazilian network; BrasilFil: Yanosky, Alberto. Asociación Guyra Paraguay. Asunción; ParaguayFil: Campos Krauer, Juan Manuel. Centro Chaqueño para la Conservación y la Investigación; ParaguayFil: Torres, Ricardo Jose. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Camino, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; ArgentinaFil: Cabral, Hugo. Asociación Guyra Paraguay. Asunción; ParaguayFil: Cartés, José. Asociación Guyra Paraguay. Asunción; ParaguayFil: Cuellar, Rosa Leny. Fundación Kaa Iya; BoliviaFil: Gallegos, Marcelo. Secretaría de Ambiente de la Provincia de Salta. Programa Guardaparques; ArgentinaFil: Giordano, Anthony J.. No especifica;Fil: Decarre, Julieta. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; ArgentinaFil: Maffei, Leonardo. Wildlife Conservation Society. Lima; PerúFil: Neris, Nora. Universidad Nacional de Asunción; ParaguayFil: Saldivar Bellassai, Silvia. Itaipu Binacional; ParaguayFil: Wallace, Robert. Wildlife Conservation Society. New York; Estados UnidosFil: Lizarraga, Leónidas. Delegación Regional Noroeste. Sistema de Información de Biodiversidad de la Administración de Parques Nacionales. Salta; ArgentinaFil: Thompson, Jeffrey. Universidad Nacional de Asunción; ParaguayFil: Velilla, Mariela. Universidad Nacional de Asunción; Paragua

A multi-method approach to delineate and validate migratory corridors

Context: Managers are faced with numerous methods for delineating wildlife movement corridors, and often must make decisions with limited data. Delineated corridors should be robust to different data and models. Objectives: We present a multi-method approach for delineating and validating wildlife corridors using multiple data sources, which can be used conserve landscape connectivity. We used this approach to delineate and validate migration corridors for wildebeest (Connochaetes taurinus) in the Tarangire Ecosystem of northern Tanzania. Methods: We used two types of locational data (distance sampling detections and GPS collar locations), and three modeling methods (negative binomial regression, logistic regression, and Maxent), to generate resource selection functions (RSFs) and define resistance surfaces. We compared two corridor detection algorithms (cost-distance and circuit theory), to delineate corridors. We validated corridors by comparing random and wildebeest locations that fell within corridors, and cross-validated by data type. Results: Both data types produced similar RSFs. Wildebeest consistently selected migration habitat in flatter terrain farther from human settlements. Validation indicated three of the combinations of data type, modeling, and corridor detection algorithms (detection data with Maxent modeling, GPS collar data with logistic regression modeling, and GPS collar data with Maxent modeling, all using cost-distance) far outperformed the other seven. We merged the predictive corridors from these three data-method combinations to reveal habitat with highest probability of use. Conclusions: The use of multiple methods ensures that planning is able to prioritize conservation of migration corridors based on all available information

FRONTIERS IN INVASIVE SPECIES DISTRIBUTION MODELING (iSDM): ASSESSING EFFECTS OF ABSENCE DATA, DISPERSAL CONSTRAINTS, STAGE OF INVASION AND SPATIAL DEPENDENCE

Successful management of biological invasions depends heavily on our ability to predict their geographic ranges and potential habitats. Species distribution modeling (SDM) provides a methodological framework to predict spatial distributions of organisms but the unique aspects of modeling invasive species have been largely ignored in previous applications. Here, three unresolved challenges facing invasive species distribution modeling (iSDM) were examined in an effort to increase prediction accuracy and improve ecological understanding of actual and potential distributions of biological invasions. The effects of absence data and dispersal constraints, stage of invasion, and spatial dependence were assessed, using an extensive collection of field-based data on the invasive forest pathogen Phytophthora ramorum. Spatial analyses were based on a range of statistical techniques (generalized linear models, classification trees, maximum entropy, ecological niche factor analysis, multicriteria evaluation) and four groups of environmental parameters that varied in space and time: atmospheric moisture and temperature, topographic variability, abundance and susceptibility of host vegetation, and dispersal pressure. Results show that incorporating data on species absence and dispersal limitations is crucial not only to avoid overpredictions of the actual invaded range in a specific period of time but also for ecologically meaningful evaluation of iSDMs. When dispersal and colonization cannot be estimated explicitly, e.g. via dispersal kernels of propagule pressure, spatial dependence measured as spatial autocorrelation at multiple scales can serve as an important surrogate for dynamic processes that explain ecological mechanisms of invasion. If the goal is to identify habitats at potential risk of future spread, the stage of invasion should be considered because it represents the degree to which an organism is at equilibrium with its environment and limits the extent to which occurrence observations provide a sample of the species ecological niche. This research provides insight into several key principles of the SDM discipline, with implications for practical management of biological invasions

Changes in the distribution and predictive modeling of downy brome (Bromus tectorum L.) at high elevations

Department Head: N. LeRoy Poff.2010 Summer.Includes bibliographical references (pages 53-55).Downy brome (Bromus tectorum L.), an invasive winter annual grass, may be increasing in extent and abundance at high elevations in the western United States. This may pose a threat to high elevation plant communities and resources. Anecdotal information suggested this range expansion in the Rocky Mountains, but data to confirm it was limited. The initial goal of my project was to examine whether downy brome was increasing at elevations above its typical range of up to 2440 m by resampling prior field studies. I further expanded my goals to make predictions about future range expansion using Maxent, a habitat matching model. I also evaluated how well the model predicted the future distribution of downy brome through additional field sampling. Two vegetation surveys in Rocky Mountain National Park (RMNP) conducted in 1993 and 1999 were resampled in 2007. Although these surveys were not initially established to examine downy brome specifically, they were useful in tracking changes in downy brome presence, abundance, and distribution. Statistical analyses were used to examine presence and abundance of downy brome, while the predictive modeling explored the potential distribution throughout RMNP. Stratified random sampling throughout RMNP in 2008 was used to validate how well the model predicted the distribution of downy brome. Results of the studies confirm suspicions that downy brome is spreading within RMNP. Analyses of the field sampling indicate that expansion of downy brome is likely occurring both in abundance and frequency at elevations ranging from 2470 m to 3080 m. Predictive modeling also indicates that further range expansion is likely within RMNP as new incidence of downy brome tend to be found within areas with a high predicted probability of occurrence. The stratified random points sampled throughout RMNP confirmed that the model performed well over a larger spatial scale despite the limited extent of the initial samples. Because downy brome appears to be increasing, managers of high elevation lands may need to consider taking a more active role in preventing further spread. The accurate model predictions made with a relatively small sample size indicate that Maxent can be an extremely useful tool for land managers who have limited time and resources. Predictive models, however, are just one of many types of information to be considered in making management decisions and should be used in conjunction with other resources