A relação entre a paisagem e a distribuição da ocorrência e abundância das espécies no espaço é uma das questões centrais em ecologia, com importantes aplicações diretas em tempos de intenso uso da terra e mudanças climáticas por atividades humanas. Contudo, para inferirmos sobre essas relações, temos que descrever a paisagem da forma mais próxima possível de como as espécies realmente a percebem. Uma paisagem pode ser descrita em diferentes níveis hierárquicos de organização do ambiente (e.g. quantidade de um mineral no solo, número de plantas em uma parcela, área de cobertura de floresta...), e cada nível pode ser descrito em diferentes escalas (resolução e extensão de descrição). Os níveis e escalas com maior poder de previsão da ocorrência/abundância de uma espécie são chamados de nível de efeito e escala (extensão) de efeito. Nesta tese, utilizo armadilhas fotográficas e modelos hierárquicos para avaliar relações entre a paisagem e espécies de mamíferos e aves. Meus objetivos são: (1) avaliar se existe relação entre nível e extensão de efeito para prever a abundância de espécies e a área de vida ou massa das mesmas; (2) investigar características do solo que possam ser determinantes da distribuição de mamíferos e aves que consumem solo (geofagia); e (3) a partir de ralações espécie-paisagem, estimar a distribuição da densidade de uma espécie, o veado-catingueiro, para diferentes datas. Não há suporte para que a área de vida ou a massa de uma espécie sejam relevantes para o nível ou extensão em que uma paisagem deva ser descrita a fim de prever a abundância de uma espécie. Isso implica na importância de se avaliar diferentes níveis e extensões de uma paisagem quando na busca por relações espécie-paisagem. Fatores locais, como a argila ou minerais do solo, podem ser importantes para algumas espécies. Descobri que o veado-mateiro e o caititu selecionam solos para consumo com base na quantidade e tipo de argila. O caititu também seleciona solos com base na concentração de microminerais, assim como a juriti-azul, a arara-azul-grande, o mutum, o aracuã e a pomba-galega. Uma descrição da paisagem em nível de composição do solo pode ser relevante para avaliar a distribuição destas e outras espécies. Relações espécie-paisagem podem ser usadas para prever a abundância de espécies no espaço. Estimei a densidade do veado-catingueiro em 1992 (2,07 ind/km2) e em 2011 (7,31 ind/km2), para uma região de pecuária extensiva no nordeste do Pantanal onde foi criada uma reserva em 1997. A densidade desta espécie aumentou 3,5 vezes entre 1992 e 2011, com o fim da pecuária no local. Investigações multinível e multiescala de relações espécie-paisagem ainda são incipientes, embora importantes aplicações destas relações já venham sendo feitas há décadas. / The relationship between the landscape and the distribution of species’ occurrence and abundance is one of the main questions in ecology, with important applications for the current period of intense land use and climate change. However, to infer about these relationships, we have to describe the landscape as closely as possible to how species actually realize it. We can describe landscapes at different hierarchical levels of the environment (e.g. mineral amount in soil, number of plants in a plot, forest cover area…), and each level can be described at different scales (resolution and extent). The best levels and scales to predict species’ occurrence/abundance are known as level of effect and scale (extent) of effect. In this PhD thesis, I use camera traps and hierarchical models to assess relationships between the landscape and mammals or birds. My goals are: (1) to evaluate possible relationships between the level and extent of effect to predict species’ abundance and species’ home range or mass; (2) to investigate soil features important to the distribution of mammals and birds engaged on geophagy (soil consumption); and (3) from species-landscape relations, to estimate the density of a species, the Gray Brocket in space for different dates. There is no support for species’ home range or mass as relevant traits related to the level and scale that a landscape should be described in order to predict species’ abundance. This highlight the importance of evaluating different levels and scales of a landscape when searching for species-landscape relationships. Local factors such as clay or minerals may be important for some species. I found that the Red Brocket and Collared Peccary select soils for consumption based on clay amount and type. The Collared Peccary also selects soil based on the concentration of trace minerals, as well as Blue Ground-dove, Hyacinth Macaw, Bare-faced Curassow, Chaco Chachalaca, and Pale-vented Pigeon. Describing the landscape at soil composition level may be important to evaluate the distribution of these and another species. Species-landscape relationships can be used to predict the abundance of species in space at different dates. I estimated the density of the Gray Brocket in 1992 (2.07 ind/km2) and 2011 (7.31 ind/km2), in a livestock region in northeastern Brazilian Pantanal where a reserve was established in 1997. Gray Brocket density increased 3.5 times between 1992 and 2011, after livestock ban. Multi-level and multi-scale approaches to investigate species-landscape relationships are still emerging, though important applications of such relationships have been done for decades.

A relação entre a paisagem e a distribuição da ocorrência e abundância das espécies no espaço é uma das questões centrais em ecologia, com importantes aplicações diretas em tempos de intenso uso da terra e mudanças climáticas por atividades humanas. Contudo, para inferirmos sobre essas relações, temos que descrever a paisagem da forma mais próxima possível de como as espécies realmente a percebem. Uma paisagem pode ser descrita em diferentes níveis hierárquicos de organização do ambiente (e.g. quantidade de um mineral no solo, número de plantas em uma parcela, área de cobertura de floresta...), e cada nível pode ser descrito em diferentes escalas (resolução e extensão de descrição). Os níveis e escalas com maior poder de previsão da ocorrência/abundância de uma espécie são chamados de nível de efeito e escala (extensão) de efeito. Nesta tese, utilizo armadilhas fotográficas e modelos hierárquicos para avaliar relações entre a paisagem e espécies de mamíferos e aves. Meus objetivos são: (1) avaliar se existe relação entre nível e extensão de efeito para prever a abundância de espécies e a área de vida ou massa das mesmas; (2) investigar características do solo que possam ser determinantes da distribuição de mamíferos e aves que consumem solo (geofagia); e (3) a partir de ralações espécie-paisagem, estimar a distribuição da densidade de uma espécie, o veado-catingueiro, para diferentes datas. Não há suporte para que a área de vida ou a massa de uma espécie sejam relevantes para o nível ou extensão em que uma paisagem deva ser descrita a fim de prever a abundância de uma espécie. Isso implica na importância de se avaliar diferentes níveis e extensões de uma paisagem quando na busca por relações espécie-paisagem. Fatores locais, como a argila ou minerais do solo, podem ser importantes para algumas espécies. Descobri que o veado-mateiro e o caititu selecionam solos para consumo com base na quantidade e tipo de argila. O caititu também seleciona solos com base na concentração de microminerais, assim como a juriti-azul, a arara-azul-grande, o mutum, o aracuã e a pomba-galega. Uma descrição da paisagem em nível de composição do solo pode ser relevante para avaliar a distribuição destas e outras espécies. Relações espécie-paisagem podem ser usadas para prever a abundância de espécies no espaço. Estimei a densidade do veado-catingueiro em 1992 (2,07 ind/km2) e em 2011 (7,31 ind/km2), para uma região de pecuária extensiva no nordeste do Pantanal onde foi criada uma reserva em 1997. A densidade desta espécie aumentou 3,5 vezes entre 1992 e 2011, com o fim da pecuária no local. Investigações multinível e multiescala de relações espécie-paisagem ainda são incipientes, embora importantes aplicações destas relações já venham sendo feitas há décadas. / The relationship between the landscape and the distribution of species’ occurrence and abundance is one of the main questions in ecology, with important applications for the current period of intense land use and climate change. However, to infer about these relationships, we have to describe the landscape as closely as possible to how species actually realize it. We can describe landscapes at different hierarchical levels of the environment (e.g. mineral amount in soil, number of plants in a plot, forest cover area…), and each level can be described at different scales (resolution and extent). The best levels and scales to predict species’ occurrence/abundance are known as level of effect and scale (extent) of effect. In this PhD thesis, I use camera traps and hierarchical models to assess relationships between the landscape and mammals or birds. My goals are: (1) to evaluate possible relationships between the level and extent of effect to predict species’ abundance and species’ home range or mass; (2) to investigate soil features important to the distribution of mammals and birds engaged on geophagy (soil consumption); and (3) from species-landscape relations, to estimate the density of a species, the Gray Brocket in space for different dates. There is no support for species’ home range or mass as relevant traits related to the level and scale that a landscape should be described in order to predict species’ abundance. This highlight the importance of evaluating different levels and scales of a landscape when searching for species-landscape relationships. Local factors such as clay or minerals may be important for some species. I found that the Red Brocket and Collared Peccary select soils for consumption based on clay amount and type. The Collared Peccary also selects soil based on the concentration of trace minerals, as well as Blue Ground-dove, Hyacinth Macaw, Bare-faced Curassow, Chaco Chachalaca, and Pale-vented Pigeon. Describing the landscape at soil composition level may be important to evaluate the distribution of these and another species. Species-landscape relationships can be used to predict the abundance of species in space at different dates. I estimated the density of the Gray Brocket in 1992 (2.07 ind/km2) and 2011 (7.31 ind/km2), in a livestock region in northeastern Brazilian Pantanal where a reserve was established in 1997. Gray Brocket density increased 3.5 times between 1992 and 2011, after livestock ban. Multi-level and multi-scale approaches to investigate species-landscape relationships are still emerging, though important applications of such relationships have been done for decades.

In Peru, research and practices that contribute to Andean natural resources’ sustainable management are needed. The Nor Yauyos Cochas Landscape Reserve is home to a wild vicugna population, which can be viewed as a key resource for conservation and sustainable development. However, some activities of Tanta Peasant Community impact negatively on vicugna’s habitat. The research aimed to determine spatial relations and impacts of Tanta’s activities on vicugna’s habitat and distribution over communal territory.A participatory mapping workshop was applied to determine vicugna’s actual distribution and local activities that could influence vicugna’s habitat. The species’ potential habitat was estimated with a species distribution model named Maxent. Spatial relations between vicugna’s actual distribution, its potential habitat and communal activities were analysed. Results indicate that potential habitat is determined by environmental conditions, while human presence and domestic livestock determine vicugna’s actual distribution. Based on the research process, recommendations relating vicugna’s sustainable management in the study area are given.The results are valuable to local community and conservation agents. Main contributions consist in generating a space for exchanging knowledge during the workshop, as well as the integration of analysis methods in physical and human geography. / En el Perú son necesarias investigación y prácticas que contribuyan al manejo sostenible de los recursos alto-andinos. La Reserva Paisajística Nor Yauyos Cochas alberga una población silvestre de vicuñas, que pueden ser clave para la conservación y desarrollo sostenible. Sin embargo, en la Comunidad Campesina de Tanta algunas actividades impactan negativamente en el hábitat de la vicuña. La investigación buscó determinar las relaciones espaciales e impactos de las actividades de la Comunidad Campesina de Tanta sobre el hábitat y la distribución de la vicuña en el territorio comunal. Mediante un taller de mapeo participativo se determinaron la distribución actual de las vicuñas y las actividades comunales que pueden influir sobre su hábitat. El hábitat potencial de la especie se estimó con el modelo de distribución de especies Maxent. Se analizaron las relaciones espaciales entre la distribución actual de la vicuña, su hábitat potencial y las actividades comunales. Los resultados indican que el hábitat potencial está determinado por condiciones ambientales, mientras que la distribución actual está determinada por la presencia humana y del ganado doméstico. En base al proceso de investigación se incluyen recomendaciones en relación al manejo sostenible de la vicuña en el área de estudio. Los resultados son de interés para la comunidad local y agentes de conservación. Aportes principales consisten en la generación de un espacio de intercambio de conocimientos en el taller, así como la integración de métodos de análisis en geografía física y humana.

abstract: This dissertation investigates spatial and temporal changes in land cover and plant species distributions on Cyprus in the past, present and future (1973-2070). Landsat image analysis supports inference of land cover changes following the political division of the island of Cyprus in 1974. Urban growth in Nicosia, Larnaka and Limasol, as well as increased development along the southern coastline, is clearly evident between 1973 and 2011. Forests of the Troodos and Kyrenia Ranges remain relatively stable, with transitions occurring most frequently between agricultural land covers and shrub/herbaceous land covers. Vegetation models were constructed for twenty-two plant species of Cyprus using Maxent to predict potentially suitable areas of occurrence. Modern vegetation models were constructed from presence-only data collected by field surveys conducted between 2008 and 2011. These models provide a baseline for the assessment of potential species distributions under two climate change scenarios (A1b and A2) for the years 2030, 2050, and 2070. Climate change in Cyprus is likely to influence habitat availability, particularly for high elevation species as the relatively low elevation mountain ranges and small latitudinal range prevent species from shifting to areas of suitable environmental conditions. The loss of suitable habitat for some species may allow the introduction of non-native plant species or the expansion of generalists currently excluded from these areas. Results from future projections indicate the loss of suitable areas for most species by the year 2030 under both climate regimes and all four endemic species (Cedrus brevifolia, Helianthemum obtusifolium, Pterocephalus multiflorus, and Quercus alnifolia) are predicted to lose all suitable environments as soon as 2030. As striking exceptions Prunus dulcis (almond), Ficus carica (fig), Punica granatum (pomegranate) and Olea europaea (olive), which occur as both wild varieties and orchard cultigens, will expand under both scenarios. Land cover and species distribution maps are evaluated in concert to create a more detailed interpretation of the Cypriot landscape and to discuss the potential implications of climate change for land cover and plant species distributions. / Dissertation/Thesis / Ph.D. Geography 2013

No description available.

Rigorous modeling of the spatial species distributions is critical in biogeography, conservation, resource management, and assessment of climate change. The goal of chapter 2 of this dissertation was to evaluate the potential of using historical samples to develop high-resolution species distribution models (SDMs) of stream fishes of the United States. I explored the spatial transferability and temporal transferability of stream–fish distribution models in chapter 3 and chapter 4 respectively. Chapter 2 showed that the discrimination power of SDMs for 76 non-game fish species depended on data quality, species' rarity, statistical modeling technique, and incorporation of spatial autocorrelation. The area under the Receiver-Operating-Characteristic curve (AUC) in the cross validation tended to be higher in the logistic regression and boosted regression trees (BRT) than the presence-only MaxEnt models. AUC in the cross validation was also higher for species with large geographic ranges and small local populations. Species prevalence affected discrimination power in the model training but not in the validation. In chapter 3, spatial transferability of SDMs was low for over 70% of the 21 species examined. Only 24% of logistic regression, 12% of BRT, and 16% of MaxEnt had AUC > 0.6 in the spatial transfers. Friedman's rank sum test showed that there was no significant difference in the performance of the three modeling techniques. Spatial transferability could be improved by using spatial logistic regression under Lasso regularization in the training of SDMs and by matching the range and location of predictor variables between training and transfer regions. In chapter 4, testing of temporal SDM transfer on independent samples resulted in discrimination power of the moderate to good range, with AUC > 0.6 for 80% of species in all three types of models. Most cool water species had good temporal transferability. However, biases and misspecified spread occurred frequently in the temporal model transfers. To reduce under- or over-estimation bias, I suggest rescaling the predicted probability of species presence to ordinal ranks. To mitigate inappropriate spread of predictions in the climate change scenarios, I recommended to use large training datasets with good coverage of environmental gradients, and fine-tune predictor variables with regularization and cross validation. / Ph. D.

Golden eagles (Aquila chrysaetos canadensis Linnaeus 1758) are declining in some areas throughout their Nearctic range (Sauer et al. 2011). This reduction is linked to changes in their habitat caused by human activities. Golden eagles inhabit an extensive range of environments (Watson 1997, Kochert et al. 2002). In the American Continent, the golden eagle's range encompasses Alaska, Canada, the United States and the Northern and Central portions of Mexico. Northern golden eagle populations migrate during winter to southern grounds, crossing international boundaries of Canada, the US and Mexico and therefore, their conservation is of trilateral concern. Golden eagles are protected by domestic laws in the three North American countries where they occur (FWCA 1997, BGEPA 1940, MBTA 1918, Lacey Act 1900, DOF 2002) and although the IUCN list the species as Least Concern, the A. c. canadensis subspecies has been protected by CITES since 1975 (Birdlife International 2012). While intensively studied in the United States, very little is known in Mexico about golden eagle ecology and their populations. As the national bird of Mexico, its conservation has been a priority for the Mexican government since its inclusion in the Endangered Species List in 1994 (SEDESOL 1994). Several threats jeopardize golden eagle populations throughout their range in North America: habitat alteration and fragmentation, electrocution, collisions with vehicles, collision with windmills and wires, poisoning from lead ingestion, drowning, shooting and trapping, and poaching for illegal wildlife trade. Mexican experts believe that a dramatic decline occurred over recent decades and that the remaining pairs have been restricted to remnant suitable habitat patches (SEMARNAP-INE 1999). Long-term survival of golden eagles largely depends on the effectiveness of current conservation efforts of habitat at a landscape level. Successful conservation and management requires accurate information on ecology of the species upon which decisions can be based. This study investigated habitat characteristics of the areas occupied by golden eagles and developed strategies for habitat management and protection to improve golden eagle viability in Chihuahua State. I surveyed a portion of Chihuahuan Desert Ecoregion in Mexico to locate golden eagle territories during 2009 and 2010. I located 30 golden eagle nesting territories and found similar composition of cover type, vegetation structure and prey indices between the territory cores and their buffer zones. Distance to most anthropogenic disturbance sources was similar between golden eagle sites and random areas (n=60). Grassland was the most common cover type, occurring in 100% of the nesting territories, and comprising 58% of the territories' area, suggesting a disproportionate use of this cover type compared to its overall availability (25% of the state area). I used landscape attributes such as topographic characteristics and human disturbances to model the probability of occurrence of golden eagles across the landscape. I used logistic regression to model the occurrence of golden eagles at two different landscape scales and selected the best model at a home range scale based on AIC values to develop a predictive map of golden eagle distribution in Chihuahua, Mexico. I found that at a home range scale, golden eagles' occurrence was positively related to open areas and terrain ruggedness and negatively to human settlements, while at a larger scale it was positively related to open areas and negatively related to forested areas. The results confirm that golden eagles are dependent on grasslands and rugged terrain. I developed predictive maps of golden eagle occurrence using a logistic regression and a Mahalanobis distance approach using the variables from the model chosen to compare the performance and output with logistic regression modeling. I analyzed the Mexican National Plan for Golden Eagle Recovery (PACE - Ã guila Real) and proposed a conservation strategy oriented to protect golden eagle populations and their habitat in Chihuahua, Mexico. This strategy integrates ecologic knowledge developed in the first two chapters and incorporates social participation of all stakeholders. The strategy recognizes the potential limitations of conservation implementation programs in Mexico and explores the potential opportunities to protect golden eagles populations and their habitat. / Ph. D.

<p>Human use of the oceans is increasingly in conflict with conservation of endangered species. Methods for managing the spatial and temporal placement of industries such as military, fishing, transportation and offshore energy, have historically been post hoc; i.e. the time and place of human activity is often already determined before assessment of environmental impacts. In this dissertation, I build robust species distribution models in two case study areas, US Atlantic (Best et al. 2012) and British Columbia (Best et al. 2015), predicting presence and abundance respectively, from scientific surveys. These models are then applied to novel decision frameworks for preemptively suggesting optimal placement of human activities in space and time to minimize ecological impacts: siting for offshore wind energy development, and routing ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff between conservation risk and industry profit with synchronized variable and map views as online spatial decision support systems.</p><p>For siting offshore wind energy development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined across species with weights of OWED sensitivity to collision and displacement and 10 km2 sites are compared against OWED profitability based on average annual wind speed at 90m hub heights and distance to transmission grid. A spatial decision support system enables toggling between the map and tradeoff plot views by site. A selected site can be inspected for sensitivity to a cetaceans throughout the year, so as to capture months of the year which minimize episodic impacts of pre-operational activities such as seismic airgun surveying and pile driving.</p><p>Routing ships to avoid whale strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem spatially. A cumulative cost surface is generated from density surface maps and conservation status of cetaceans, before applying as a resistance surface to calculate least-cost routes between start and end locations, i.e. ports and entrance locations to study areas. Varying a multiplier to the cost surface enables calculation of multiple routes with different costs to conservation of cetaceans versus cost to transportation industry, measured as distance. Similar to the siting chapter, a spatial decisions support system enables toggling between the map and tradeoff plot view of proposed routes. The user can also input arbitrary start and end locations to calculate the tradeoff on the fly.</p><p>Essential to the input of these decision frameworks are distributions of the species. The two preceding chapters comprise species distribution models from two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3), predicting presence and density, respectively. Although density is preferred to estimate potential biological removal, per Marine Mammal Protection Act requirements in the U.S., all the necessary parameters, especially distance and angle of observation, are less readily available across publicly mined datasets.</p><p>In the case of predicting cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship (n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed within space-time grid cells to harmonize effort between the two survey platforms. For each of 16 cetacean species guilds, I predicted the probability of occurrence from static environmental variables (water depth, distance to shore, distance to continental shelf break) and time-varying conditions (monthly sea-surface temperature). To generate maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used to define the optimal threshold that minimizes false positive and false negative error rates. I integrated model outputs, including tables (species in guilds, input surveys) and plots (fit of environmental variables, ROC curve), into an online spatial decision support system, allowing for easy navigation of models by taxon, region, season, and data provider.</p><p>For predicting cetacean density within the inner waters of British Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. Abundance estimates are provided on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams & Thomas 2007) by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.</p><p>Starting with marine animal observations at specific coordinates and times, I combine these data with environmental data, often satellite derived, to produce seascape predictions generalizable in space and time. These habitat-based models enable prediction of encounter rates and, in the case of density surface models, abundance that can then be applied to management scenarios. Specific human activities, OWED and shipping, are then compared within a tradeoff decision support framework, enabling interchangeable map and tradeoff plot views. These products make complex processes transparent for gaming conservation, industry and stakeholders towards optimal marine spatial management, fundamental to the tenets of marine spatial planning, ecosystem-based management and dynamic ocean management.</p> / Dissertation

O Eragrostis plana Nees (capim-annoni-2 ou capim annoni) é uma gramínea exótica trazida da África do Sul nos anos cinquenta e atualmente tem presença em aproximadamente 10% da área total do bioma Pampa, sendo a espécie mais invasiva desse bioma. Tendo em conta a grande capacidade desta espécie para se estabelecer em uma ampla variedade de condições ambientais, os efeitos ambientais e econômicos negativos envolvidos, bem como sua dificuldade de erradicação, é importante identificar as áreas mais suscetíveis à invasão em um futuro próximo, para assim aprimorar os planos de manejo e evitar a expansão de áreas infestadas. O presente trabalho foi desenvolvido no município de Aceguá – RS, com o objetivo de identificar quais áreas são as mais suscetíveis à invasão. Foram aplicados os modelos de distribuição de espécies MAXENT e GARP tendo como dados de entrada as variáveis ambientais calculadas a partir imagens espectrais, modelo numérico de elevação, mapa de solos e mapa de vias. Como algumas variáveis originalmente têm resolução espacial de 250 m e outras de 30 m, foi feita uma reamostragem tanto a 30 m como a 250 m para comparar os resultados dos modelos nas duas resoluções espaciais. Para diminuir o número de variáveis de entrada foi feita uma análise de correlação para eliminar as variáveis com alta correlação. Também foi feito o teste Jackknife para avaliar quais variáveis contribuem mais na modelagem espacial da distribuição do capim annoni. Os dois modelos, tanto no treinamento como na validação, nas duas resoluções espaciais, apresentam valores médios de ajuste de AUC acima de 0,7, sendo considerado um bom ajuste. Foram empregados três métodos para calcular os limiares ótimos de corte para cada um dos modelos: 1) sensibilidade igual à especificidade; 2) soma entre a sensibilidade e a especificidade máxima; Os limiares obtidos foram 42 para MAXENT_250, 39 para MAXENT_30, 69 para GARP_250 e 68 para GARP_30. Após a aplicação dos limiares, verificou-se que o modelo GARP prediz uma área maior que o MAXENT, 33,20% em comparação com 24,60% na resolução espacial de 250 m, e 35,83% contra 27,17% na resolução espacial de 30 m. Verificou-se também que o GARP possui melhor capacidade de generalização, o qual é importante para modelar espécies invasoras. Os dois modelos predizem com presença uma área comum de 21,23% e 23,94% nas resoluções espaciais de 250 m e 30 m respectivamente. As pastagens são as classes de uso que apresentam uma maior suscetibilidade à invasão de capim anonni. Ao cruzar os resultados dos modelos de suscetibilidade à invasão de capim annoni, com resolução espacial de 30 m, e as áreas de pastagens que estão sob alta pressão de pastejo, verificou-se que o modelo MAXENT consegue predizer uma suscetibilidade à invasão em 24,51% das áreas e o modelo GARP prediz 37,95% de suscetibilidade à invasão. As comparações entre as duas resoluções espaciais demonstrou que não há muitas diferenças em termos de quantificação de área, sendo que o principal ganho foi o detalhamento espacial, o qual foi obtido com um alto custo computacional. / The Eragrostis plana Nees (South African lovegrass), is an exotic grassy plant originally from South Africa, introduced in the 50s and is currently present in approximately 10% of the total area of the Pampa biome, being the most invasive species in this biome. Considering the large capacity of the South African lovegrass establishing itself in a wide variety of environmental conditions, the negative effects, both environmental and economical that it involves, as well as its difficulty of eradication, it is important to identify the invasion most susceptible areas in the near future, in order to improve the management to prevent the spread of infested areas. This research was developed in the municipality of Aceguá – RS, with the objective of identifying which areas are most susceptible to invasion. The MAXENT and GARP models of distribution of species were applied, having as input data the environmental variables calculated from spectral images, digital elevation model, soil map and road map. As some variables originally had spatial resolution of 250m and others of 30m, a resample was done at both 30m and 250m in order to compare the models results in these two spatial resolutions. To reduce the input variables amount, a correlation analysis was performed to eliminate the high correlation variables. The Jackknife test was also used to evaluate which variables contribute most to the South African lovegrass distribution spatial modeling. Both models, at the two spatial resolutions, during the training and the validation steps, present mean values of AUC adjustment above 0.7, being considered a good fit. Three methods were used to calculate the optimal thresholds for each model: 1) the sensitivity equals to the specificity; 2) the sum between sensitivity and specificity is the maximum; 3) the distance between the ROC curve and left top corner is minimum. The calculated thresholds were 42 for MAXENT_250, 39 for MAXENT_30, 69 for GARP_250 and 68 for GARP_30. After applying these thresholds, it was verified that the GARP model predicts an area greater than MAXENT, 33.20% compared to 24.60% for the spatial resolution of 250m, and 35.83% against 27.17% in the spatial resolution of 30m. It was also verified that GARP has a better generalization capacity, which is important for modeling invasive species patterns. Both models predict a common area with susceptible to invasion of 21.23% and 23.94% in spatial resolutions of 250m and 30m respectively. The grasslands are the land cover that presents a South African lovegrass invasion greater susceptibility. Cross-referencing the susceptibility invasion models with the overgrazing areas at 30m of spatial resolution, it was verified that the model MAXENT can predict a susceptibility to invasion in 24.51% of the areas and the GARP model predicts 37.95% susceptibility to invasion. Comparisons between the two spatial resolutions showed that there are not many differences in terms of area quantification, where the main gain was spatial detailing, which was obtained with a high computational cost.

Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species’ distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this ‘virtual ecologist’ approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species’ response to altered environmental conditions and which should hence be considered when trying to project species’ distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species’ response to environmental change, identify key challenges for future research and discuss emerging developments. / Das Vorkommen von Arten wird zunehmend bedroht durch Klima- und Landnutzungswandel. Robuste Vorhersagen der damit verbundenen Arealveränderungen sind ausschlaggebend für die Erarbeitung dynamischer und nachhaltiger Naturschutzstrategien. Habitateignungsmodelle erstellen statistische Zusammenhänge zwischen dem Vorkommen einer Art und relevanten Umweltvariablen und erlauben zügige Einschätzungen potentieller Arealveränderungen. Dabei werden jedoch transiente Dynamiken weitgehend ignoriert sowie demographische Prozesse und biotische Interaktionen. Daher wurden Vorschläge laut, diese statistischen Modelle mit mechanistischeren Ansätzen zu koppeln. In der vorliegenden Arbeit zeige ich zwei verschiedene Möglichkeiten solcher Kopplung auf. (i) Ich beschreibe den sogenannten ‚Virtuellen Ökologen’-Ansatz als mächtiges Validierungswerkzeug, in dem mechanistische Modelle virtuelle Testflächen bieten zur Erforschung verschiedener Probenahmedesigns oder statistischer Methoden sowie spezifischer Fragestellungen. Auch verwende ich diesen Ansatz, um systematisch zu untersuchen wie sich transiente Dynamiken sowie Arteigenschaften und ökologische Prozesse auf die Vorhersagegüte von Habitateignungsmodellen auswirken. So kann ich entscheidende Prozesse identifizieren welche in zukünftigen Modellen Berücksichtigung finden sollten. (ii) Darauf aufbauend koppele ich Vorhersagen von Habitateignungsmodellen mit einem individuen-basierten Populationsmodell, um die Entwicklung des Schweizer Birkhuhnbestandes unter Klimawandel vorherzusagen. Durch die explizite Berücksichtigung der Wechselwirkungen zwischen Habitat und demographischer Prozesse lassen sich direktere Aussagen über Populationsentwicklung und damit verbundener Extinktionsrisiken treffen. Allerdings führen verschiedene Simulationen auch zu hoher Variabilität zwischen Vorhersagen, was die Bedeutung von Sensitivitätsanalysen unterstreicht, um Unsicherheiten und Robustheit von Vorhersagen einzuschätzen. Außerdem identifiziere ich Restriktionen in der Datenabdeckung des Umweltraumes als möglichen Grund für kontrastierende Vorhersagen verschiedener Habitateignungsmodelle. Wenn die Nische einer Art nicht vollständig durch Daten beschrieben ist, kann dies zu unrealistischen Vorhersagen der Art-Habitat-Beziehung führen. Insgesamt trägt meine Arbeit erheblich bei zu einem besseren Verständnis der Auswirkung verschiedenster Unsicherheitsfaktoren auf Vorhersagen von Arealveränderungen und zeigt Wege auf, mit diesen umzugehen. Abschließend erstelle ich einen vorläufigen Leitfaden für Vorhersagemodelle und identifiziere Kernpunkte für weitere Forschung auf diesem Gebiet.