Submitted by Milena Rubi (milenarubi@ufscar.br) on 2017-02-15T14:11:09Z No. of bitstreams: 1 FERREIRA_Larissa_2015.pdf: 46221411 bytes, checksum: ae8a0358ebf5e33024f58e5c75dae037 (MD5) / Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-02-15T14:11:21Z (GMT) No. of bitstreams: 1 FERREIRA_Larissa_2015.pdf: 46221411 bytes, checksum: ae8a0358ebf5e33024f58e5c75dae037 (MD5) / Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-02-15T14:11:31Z (GMT) No. of bitstreams: 1 FERREIRA_Larissa_2015.pdf: 46221411 bytes, checksum: ae8a0358ebf5e33024f58e5c75dae037 (MD5) / Made available in DSpace on 2017-02-15T14:11:38Z (GMT). No. of bitstreams: 1 FERREIRA_Larissa_2015.pdf: 46221411 bytes, checksum: ae8a0358ebf5e33024f58e5c75dae037 (MD5) Previous issue date: 2015-11-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Some conservation actions require the knowledge of the geographical distribution of species, however, this knowledge is far from being achieved for most species. The species distribution models (SDMs) have proved a useful tool to predict the distribution of species and guide field research to find new records. The SDMs using field data occurrence and environmental variables to indicate potential sites for the occurrence of a species. The quality and quantity of the data used are important to a successful result prediction models and application to conservation. The choice of environmental data and the algorithm and their settings are important for the development of models, the choice of these variables have directly influences to the quality of the models. Another very important step in modeling is the quality assessment and validation of the model, is that it may decrease the risk of accepting as true models with gross errors. The objective of this study is to evaluate the applicability of models generated by MaxEnt to find new populations of plants considering different data configurations used. For this, considering that the field validation is the most appropriate in the literature, but the most costly, the first chapter proposes a validation methodology of the models as easy application field. The methodology was able to find new records in the field, therefore, indicated for the validation of models. In the second chapter, knowing of the existence of a wide variety of variables that influence the performance of the models, the aim was to test the influence of the sample size, the spatial bias, the set of climate data and settings available for the MaxEnt algorithm in the areas of prediction potential distribution. The results demonstrated that the use of sampling and climate data restricted to the limit of the study area and also the use of soil data generate more accurate models. / Algumas ações conservacionistas necessitam do conhecimento da distribuição geográfica das espécies, porém, esse conhecimento está longe de ser alcançado para a maioria das espécies. Os modelos de distribuição de espécies (MDEs) têm se mostrado uma ferramenta útil para prever a distribuição das espécies e guiar pesquisas de campo para encontrar novos registros. Os MDEs utilizam dados de ocorrência e variáveis ambientais para indicar locais potenciais para a ocorrência de uma espécie. A precisão e quantidade dos dados utilizados são importantes para um bom resultado de predição dos modelos e aplicação à conservação. A escolha dos dados ambientais e do algoritmo e suas configurações são essenciais para o desenvolvimento dos modelos, pois influenciam diretamente na qualidade dos mesmos. Outra etapa bastante importante na modelagem é a validação do modelo, pois é ela que diminui o risco de aceitar como verdadeiros modelos que possuem erros grosseiros. O objetivo principal deste estudo é avaliar a aplicabilidade de modelos gerados pelo MaxEnt para encontrar populações de plantas, considerando diferentes configurações dos dados utilizados. Para isso o primeiro capítulo propõe uma metodologia de validação dos modelos em campo de fácil aplicação, uma vez que a validação em campo é a mais indicada pela literatura. A metodologia proposta no capítulo um é uma adaptação ao método de “caminhamento” ou método expedito de levantamento e caracterização da vegetação. A metodologia proposta foi eficaz para a localização das espécies em campo e mostrou que a caracterização da vegetação é uma etapa importante para a interpretação dos resultados, uma vez que explicou a ausência de duas espécies em áreas onde o modelo havia previsto presença. Apresenta como principal desvantagem a necessidade de pessoas experientes para o reconhecimento das espécies de plantas para a sua aplicação de forma agilizada. No segundo capítulo, foi testada a influência da área de amostragem, do conjunto de dados climáticos e das configurações do algoritmo Maxent na predição de áreas potenciais de distribuição. Os resultados obtidos demonstraram que o uso de dados amostrais e climáticos restritos aos limites da área de interesse para a busca das espécies e a inclusão de dados de solo geram modelos mais acurados. Mostrou também que as diferentes configurações do Maxent geraram modelos muito similares.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-07-17T13:30:07Z No. of bitstreams: 1 MarcioJoaquimDaSilva_TESE.pdf: 6572053 bytes, checksum: 24e9484e2537c31f4a72339d474f1faa (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-07-20T11:14:03Z (GMT) No. of bitstreams: 1 MarcioJoaquimDaSilva_TESE.pdf: 6572053 bytes, checksum: 24e9484e2537c31f4a72339d474f1faa (MD5) / Made available in DSpace on 2017-07-20T11:14:04Z (GMT). No. of bitstreams: 1 MarcioJoaquimDaSilva_TESE.pdf: 6572053 bytes, checksum: 24e9484e2537c31f4a72339d474f1faa (MD5) Previous issue date: 2017-04-12 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / Os ecossistemas aqu?ticos dulc?colas est?o entre os mais ricos, em termos de n?mero de esp?cies, e amea?ados por altera??es antr?picas no mundo. Impactos como a introdu??o de esp?cies n?o nativas e as transposi??es de ?guas entre bacias distintas (como ? o caso do Projeto de Transposi??o do rio S?o Francisco-PISF) amea?am a conserva??o das esp?cies. A preocupa??o com a conserva??o das esp?cies fomentou a cria??o das chamadas Unidades de Conserva??o (UCs - No Brasil foram criadas a partir de 1930 e tem n?veis de restri??o de usos diversos). Provavelmente, estes mecanismos t?m sido insuficientes na conserva??o dos peixes, pois mesmo ap?s suas cria??es, a contribui??o das esp?cies n?o nativas nas comunidades naturais s? tem crescido ao longo dos anos e ? apontada como a segunda causa de extin??o de esp?cies do planeta. Nesse contexto, o presente estudo buscou estabelecer uma padroniza??o da nomenclatura das esp?cies nas bacias envolvidas no PISF, antes da conex?o artificial, evidenciando o atual n?vel de conhecimento da ictiofauna e construir uma linha de base para detectar futuros impactos da obra. Al?m disso, objetivamos avaliar a efetividade das UCs em proteger os peixes das bacias envolvidas no projeto e modelar o risco de invas?o de esp?cies exclusivas da bacia doadora nas receptoras. Para tanto, foram utilizados registros prim?rios e secund?rios das esp?cies. Os resultados apontam para baixa similaridade entre a composi??o de esp?cies das bacias doadora e receptoras do PISF, al?m de indicar a import?ncia das UCs para conserva??o dos peixes da regi?o, que mesmo com tamanho reduzido (~1% da Caatinga) abrigam porcentagem significativa da fauna associada (entre 24 e 31% das esp?cies de cada bacia). Ademais, foi obtido que as bacias receptoras do PISF, apresentam adequabilidade para 11 esp?cies (sete fam?lias e tr?s ordens) exclusivas da bacia doadora (Leporinus friderici, Megaleporinus obtusidens, Pamphorichthys hollandi? Pimelodus maculatus, Moenkhausia sanctaefilomenae, Hemigrammus brevis, Pimelodella laurenti, Cichlasoma sanctifranciscense, Centromochlus bockmanni, Conorhynchos conirostris e Pseudoplatystoma corruscans, ordem decrescente de adequabilidade geral). Por fim, refor?amos a necessidade da cria??o/amplia??o das UCs nas bacias envolvidas, para que estas cumpram melhor o seu objetivo conservacionista e, corroboramos a necessidade do monitoramento constante da invas?o de esp?cies nas bacias receptoras das ?guas do PISF, a fim de garantir a preserva??o das comunidades ?cticas nativas. / Freshwater ecosystems are within the richest in terms of species number and are threatened by anthropic transformations worldwide. Impacts such as non-native species introduction and interbasin water transfer (like S?o Francisco River Interbasin Water Transfer Project ? SFIBWT, PISF ? in Portuguese) put species conservation in peril. The concern with this matter promoted the creation of Conservation Unities (UCs ? created from 1930?s onward in Brazil and have distinct restriction levels). Possibly, these mechanisms have been insufficient in species conservation, because even after their creation, non-native species contribution to natural communities has only grown over years and it is pointed as the second main cause of planet?s species extinction. Regarding this issue, we hereby tried to establish a species nomenclatural pattern in basins encompassed by PISF, previous to the artifitial connection, demonstrating the current ichthyofaunal knowledge level and building a baseline for future identification of the project?s impacts. Besides, we aim to evaluate the effectivity of UCs in protecting fish of the basins included in the project and model the invasiveness risk of exclusive species of donor basins to receptor ones. Therefore, we used primary and secondary records of species. The results showed a low similarity among species composition of donor and receptor basins of PISF, besides indicating the UCs? importance for regional fish species conservation. Even though these UCs have a small size (~1% of Caatinga), they contain a significant percentage of associated fauna (between 24 and 31% of each basin?s species). Lastly, we noticed the PISF receptor basins show adequability to 11 species (seven families and three orders) which were exclusive to donor basin (Leporinus friderici, Megaleporinus obtusidens, Pamphorichthys hollandi? Pimelodus maculatus, Moenkhausia sanctaefilomenae, Hemigrammus brevis, Pimelodella laurenti, Cichlasoma sanctifranciscense, Centromochlus bockmanni, Conorhynchos conirostris e Pseudoplatystoma corruscans, decending order of general suitability). Within this context, we strengthen the need of creation/enlargement of UCs in encompassed basins so these can better fulfill their conservational goals, and we also corroborate the need of constant monitoring of invasive species in receptor basins of PISF?s water in order to guarantee the preservation of native ichthyc communities.

Ecological niche modelling is a method designed to describe and predict the geographic distribution of an organism. This procedure aims to quantify the species-environment relationship by describing the association between the organism’s occurrence records and the environmental characteristics at these points. More simply, these models attempt to capture the ecological niche that a particular organism occupies. A popular application of ecological niche models is to predict the potential distribution of invasive alien species in their introduced range. From a biodiversity conservation perspective, a pro-active approach to the management of invasions would be to predict the potential distribution of the species so that areas susceptible to invasion can be identified. The performance of ecological niche models and the accuracy of the potential range predictions depend on the quality of the data that is used to calibrate and evaluate the models. Three different types of input data can be used to calibrate models when producing potential distribution predictions in the introduced range of an invasive alien species. Models can be calibrated with native range occurrence records, introduced range occurrence records or a combination of records from both ranges. However, native range occurrence records might suffer from geographical bias as a result of biased sampling or incomplete sampling. When occurrence records are geographically biased, the underlying environmental gradients in which a species can persist are unlikely to be fully sampled, which could result in an underestimation of the potential distribution of the species in the introduced range. I investigated the impact of geographical bias in native range occurrence records on the performance of ecological niche models for 19 invasive plant species by simulating two geographical bias scenarios (six different treatments) in the native range occurrence records of the species. The geographical bias simulated in this study was sufficient to result in significant environmental bias across treatments, but despite this I did not find a significant effect on model performance. However, this finding was perhaps influenced by the quality of the testing dataset and therefore one should be wary of the possible effects of geographical bias when calibrating models with native range occurrence records or combinations there of. Secondly, models can be calibrated with records obtained from the introduced range of a species. However, when calibrating models with records from the introduced range, uncertainties in terms of the equilibrium status and introduction history could influence data quality and thus model performance. A species that has recently been introduced to a new region is unlikely to be in equilibrium with the environment as insufficient time will have elapsed to allow it to disperse to suitable areas, therefore the occurrence records available would be unlikely to capture its full environmental niche and therefore underestimate the species’ potential distribution. I compared model performance for seven invasive alien plant species with different simulated introduction histories when calibrated with native range records, introduced range records or a combination of records from both ranges. A single introduction, multiple introduction and well established scenario was simulated from the introduced range records available for a species. Model performance was not significantly different when compared between models that were calibrated with datasets representing these three types of input data under a simulated single introduction or multiple introduction scenario, indicating that these datasets probably described enough of the species environmental niche to be able to make accurate predictions. However, model performance was significantly different for models calibrated with introduced range records and a combination of records from both ranges under the well established scenario. Further research is recommended to fully understand the effects of introduction history on the niche of the species. Copyright / Dissertation (MSc)--University of Pretoria, 2009. / Zoology and Entomology / unrestricted

Department of Ecology and Resource Management / MENVSC

The field of vector-borne disease research uses multidisciplinary approaches to help understand complicated interactions. This dissertation, covers three different aspects of tick-borne disease research which all focus on exploring tick-borne diseases in the non-endemic areas of Denton, County Texas and the state of Texas with a focus on Rickettsia spp. These aspects include tick sampling, testing ticks for the presence of Rickettsia spp., and creating species distribution maps of the Rickettsia spp. Rickettsia amblyommatis and tick species Amblyomma americanum.

No description available.

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Tick populations have been immigrating into northeast Tennessee from east Tennessee, Kentucky, Virginia, and North Carolina. Counties in states bordering northeast Tennessee harbor tick species associated with human illness. Human diseases transmitted by ticks include ehrlichiosis, spotted fever rickettsial group diseases, tularemia, anaplasmosis, babesiosis, Lyme disease, alpha-gal syndrome, Heartland virus, Powassan virus, and southern tick-associated rash illness (STARI). These diseases cause morbidity and mortality in human populations and may pose a high risk to individuals, wildlife, and livestock. The Cherokee National Forest covering the east Tennessee border provides a permissible environment for ticks to immigrate and thrive. Residents of northeast Tennessee frequently use the natural environment for a variety of purposes, creating exposure risk at the human-animal-environment interface. This study performed a scoping review and meta-analysis addressing topics informing epidemiological investigation of tick populations. The meta-analysis identified geography, climate, and Shannon-Wiener Diversity Index as the most significant variables associated with northeast Tennessee tick populations. Additionally, tick surveillance in northeast Tennessee counties was performed. These counties included Carter, Greene, Hancock, Hawkins, Johnson, Washington, and Unicoi. Primary tick species present in the summer included the American Dog tick (Dermacentor variabilis); the winter included the Blacklegged/Deer tick (Ixodes scapularis). Canonical correlation analysis was used to identify which environmental variables had the most influence, to what degree, and in a positive or negative direction. Altitude, total forest land, forest canopy, and fraction of surface water area were statistically significant. More altitude was correlated with more clinical cases; less total forest land, canopy, and fraction of surface water area was correlated with less clinical cases. Lastly, species distribution modeling of the invasive Asian longhorned tick was conducted. Study results indicate a low to moderate risk for tick-borne illness exposures among human populations, which is poised to increase. Species distribution modeling and clinical case data reports suggested an increasing exposure risk from improved habitat suitability. Increased risk is related to climate change and tick population growth in metropolitan areas. Finally, surveillance and control methods are summarized for integration into public health interventions.

The convergence of three young scientific disciplines (ecology, geospatial sciences, and remote sensing) has generated unique advancements in wildlife research by connecting ecological data with remote sensing data through the application of geospatial techniques. Ecological datasets may contain spatial and sampling biases. By using geospatial techniques, datasets may be useful in revealing landscape scale (e.g., statewide) trends for wildlife populations, such as population recovery and human-wildlife interactions. Specifically, black bear populations across North America vary greatly in their degree of distribution stability. The black bear population in Michigan may be considered stable or secure, whereas the population in Missouri is currently recolonizing. The focus of the research in this dissertation is to examine the ecological and anthropogenic impacts 1) on human-black bear interactions in Michigan (see Chapter 2) and 2) on black bear presence in Missouri (see Chapter 3), through the use of black bear reports provided by the public to the state wildlife agencies. By using generalized linear modeling (GLM) and maximum entropy (MaxEnt), I developed spatial distribution models of probability of occurrence/presence for the 2 study areas (Michigan and Missouri). For the Missouri study, I quantified the spatiotemporal shifts in the probability of bear presence statewide. The results from my statewide studies corroborate previous local-scale research based on rigorous data collection. Overall, human-black bear interactions (e.g., wildlife sightings, conflicts), while very dynamic, appear greatest in forested and rural areas where the preferred habitat for black bears (i.e., forest) intersects with low density anthropogenic activities. As both human and black bear populations continue to expand, it is reasonable to expect human-black bear interactions to spatiotemporally increase across both study areas. The results from my studies provide wildlife managers with information critical to management decisions such as harvest regulations and habitat conservation actions across the landscape and through time. The ability to detect and monitor ecological changes through the use of geospatial techniques can lead to insights about the stressors and drivers of population-level change, further facilitating the development of proactive causeocused management strategies.