Padrões geográficos e temporais na riqueza de espécies de quirópteros: mecanismos ecológico-evolutivos e incertezas

Submitted by Cássia Santos ([email protected]) on 2017-05-17T10:46:35Z No. of bitstreams: 2 Tese - Davi Mello Cunha Crescente Alves - 2017.pdf: 2840371 bytes, checksum: c91a17080359e0a7e6cf67884df853ff (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira ([email protected]) on 2017-05-17T11:46:21Z (GMT) No. of bitstreams: 2 Tese - Davi Mello Cunha Crescente Alves - 2017.pdf: 2840371 bytes, checksum: c91a17080359e0a7e6cf67884df853ff (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2017-05-17T11:46:21Z (GMT). No. of bitstreams: 2 Tese - Davi Mello Cunha Crescente Alves - 2017.pdf: 2840371 bytes, checksum: c91a17080359e0a7e6cf67884df853ff (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-04-10Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESnão consta.Nessa tese nós tentamos entender quais são os fatores ecológicos e evolutivos responsáveis por explicar a variação na riqueza de espécies de morcegos tanto entre regiões quanto ao longo do tempo profundo. No primeiro capítulo nós avaliamos como diferentes propriedades do ambiente - i.e. energia, heterogeneidade ambiental e sazonalidade - explicam a riqueza de espécies de morcegos em diferentes regiões da Terra. Nós encontramos que as contribuições contribuições por esses determinantes ambientais para explicar os gradientes geográficos de morcegos são mais importantes do que as suas contribuições específicas. Com o objetivo de entender mais especificamente como processos históricos explicam a diferença de riqueza de morcegos entre regiões, nós avaliamos no segundo capítulo a diferença de diversificação e dispersão biogeográfica entre regiões tropicais e extratropicais. Além disso, nós avaliamos como a incerteza nos dados e erros estatísticos associados aos modelos evolutivos que estimam esses processos históricos podem afetar as nossas conclusões sobre o padrão geográfico dos morcegos. Nós concluímos que esse padrão é extremamente afetado por esses dois artefatos. No terceiro capítulo nós exploramos como o nosso conhecimento sobre as taxas de diversificação estimadas por esses modelos evolutivos pode ser aprofundado se nós levarmos em consideração a hierarquia biológica. Mais precisamente, nós propomos um modelo conceitual para discutir se os padrões de diversificação são mais determinados por processos evolutivos ocorrendo ao nível dos indivíduos que compõem as espécies ou ao nível das próprias espécies. Já no último capítulo, nós avaliamos quais são os principais fatores responsáveis por explicar a variação na riqueza de espécies de morcegos ao longo do tempo profundo. Nós encontramos que a competição entre linhagens de morcegos por nichos vagos ao longo do Cenozóico foi mais importante do que o efeito direto de processos ambientais ocorrendo em grandes escalas geográficas, como mudanças climáticas ou soerguimento de cadeias de montanhas. Finalizando, nós concluímos que entre diferentes regiões, a sinergia entre processos ambientais é mais importante em explicar a riqueza de espécies de morcegos do que o efeito específico de cada um. Já, ao longo do tempo profundo, a competição por nichos vagos entre linhagens do mesmo clado é mais importante que o efeito direto de diferentes processos ambientais. Além disso, nós também encontramos que problemas associados aos dados e modelos evolutivos, assim como a falta de conhecimento dos mecanismos ecológico-evolutivos subjacentes as esses modelos, podem afetar drasticamente as nossas conclusões a respeito dos padrões de riqueza de espécies

The potencial impacto of the white-nose syndrome on the conservation status of north american bats

Submitted by Luciana Ferreira ([email protected]) on 2014-12-18T12:34:27Z No. of bitstreams: 1 Dissertação - Davi Mello Cunha Crescente Alves - 2013.pdf: 853772 bytes, checksum: 72911b50f56ac854e4084c11c9191154 (MD5)Approved for entry into archive by Luciana Ferreira ([email protected]) on 2014-12-18T14:28:20Z (GMT) No. of bitstreams: 1 Dissertação - Davi Mello Cunha Crescente Alves - 2013.pdf: 853772 bytes, checksum: 72911b50f56ac854e4084c11c9191154 (MD5)Made available in DSpace on 2014-12-18T14:28:20Z (GMT). No. of bitstreams: 1 Dissertação - Davi Mello Cunha Crescente Alves - 2013.pdf: 853772 bytes, checksum: 72911b50f56ac854e4084c11c9191154 (MD5) Previous issue date: 2013-12-18Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESThe White-Nose syndrome is an emergent infectious disease that had already killed almost six millions North American bats and spread more than two thousand kilometers. Even so, studies about their possible impacts upon hosts are still lacking, principally upon all the susceptible North American bats. We predicted the consequences of the WNS spread in the North American hosts by generating an environmental suitability map for the disease, and then, we overlaid with the extension of occurrence of all hibernating bats in North America. We assumed that all intersection localities will somehow negatively affect bat’s local populations, and we reassessed their conservation status based on their potential population reduction. 16% of the North American hibernating bat fauna were considered threatened under this WNS potential spread. We believe our results could contribute with governments conservation actions.(Sem resumo

Fig. 1 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 1. Global pattern of bat species richness. Legend corresponds to the number of bat species per 2◦ × 2◦ grid cell. Letters represent the zoogeographic realms (Holt et al. 2013): Na = Nearctic, P = Panamanian, Nt = Neotropical, Pa = Palearctic, Sa = Saharo-Arabian, At = Afrotropical, M = Madagascan, Or = Oriental, Au = Australian, Sj = Sino-Japanese, Oc = Oceanian.Published as part of Alves, Davi Mello Cunha Crescente, Diniz-Filho, José Alexandre Felizola, Souza, Kelly da Silva e, Gouveia, Sidney Feitosa & Villalobos, Fabricio, 2017, Geographic variation in the relationship between large-scale environmental determinants and bat species richness, pp. 1-8 in Basic and Applied Ecology 27 on page 2, DOI: 10.1016/j.baae.2017.12.002, http://zenodo.org/record/836445

Fig. 2 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 2. Distribution of coefficients of determination (R2) of GWR for the analysis of bat species richness regressed on the predictors of the three environmental hypotheses combined.Published as part of Alves, Davi Mello Cunha Crescente, Diniz-Filho, José Alexandre Felizola, Souza, Kelly da Silva e, Gouveia, Sidney Feitosa & Villalobos, Fabricio, 2017, Geographic variation in the relationship between large-scale environmental determinants and bat species richness, pp. 1-8 in Basic and Applied Ecology 27 on page 4, DOI: 10.1016/j.baae.2017.12.002, http://zenodo.org/record/836445

Fig. 3 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 3. Spatial non-stationarity in the effect (R2) of the predictors of the three environmental hypotheses combined to explain the global pattern of bat species richness.Published as part of Alves, Davi Mello Cunha Crescente, Diniz-Filho, José Alexandre Felizola, Souza, Kelly da Silva e, Gouveia, Sidney Feitosa & Villalobos, Fabricio, 2017, Geographic variation in the relationship between large-scale environmental determinants and bat species richness, pp. 1-8 in Basic and Applied Ecology 27 on page 4, DOI: 10.1016/j.baae.2017.12.002, http://zenodo.org/record/836445

Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Alves, Davi Mello Cunha Crescente, Diniz-Filho, José Alexandre Felizola, Souza, Kelly da Silva e, Gouveia, Sidney Feitosa, Villalobos, Fabricio (2017): Geographic variation in the relationship between large-scale environmental determinants and bat species richness. Basic and Applied Ecology 27: 1-8, DOI: 10.1016/j.baae.2017.12.00

Fig. 4 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 4. Spatial variation in the partial coefficients of determination (R2) for three environmental hypotheses and their shared effects. The coefficients are: energy (E), heterogeneity (H), seasonality (S), shared contribution between energy and heterogeneity (E: H), energy and seasonality (E: S), heterogeneity and seasonality (H: S), and the contribution shared among energy, heterogeneity and seasonality (E: H: S).Published as part of Alves, Davi Mello Cunha Crescente, Diniz-Filho, José Alexandre Felizola, Souza, Kelly da Silva e, Gouveia, Sidney Feitosa & Villalobos, Fabricio, 2017, Geographic variation in the relationship between large-scale environmental determinants and bat species richness, pp. 1-8 in Basic and Applied Ecology 27 on page 5, DOI: 10.1016/j.baae.2017.12.002, http://zenodo.org/record/836445

Unveiling geographical gradients of species richness from scant occurrence data

Aim: Despite longstanding investigation, the gradients of species richness remain unknown for most taxa because of shortfalls in knowledge regarding the quantity and distribution of species. Here, we explore the ability of a geostatistical interpolation model, regression-kriging, to recover geographical gradients of species richness. We examined the technique with an in silico gradient of species richness and evaluated the effect of different configurations of knowledge shortfalls. We also took the same approach for empirical data with large knowledge gaps, the infraorder Furnariides of suboscine birds. Innovation: Regression-kriging builds upon two cornerstones of geographical gradients of biodiversity, the spatial autocorrelation of species richness and the conspicuous association of species with environmental factors. With this technique, we recovered a simulated gradient of richness using < 0.01% of sampling sites across the region. The accuracy of the regression-kriging is higher when input samples are more evenly distributed throughout the geographical space rather than the environmental space of the target region. Moreover, the accuracy of this method is more sensitive to the sufficiency of sampling effort within cells than to the quantity of sampled localities. For Furnariides birds, regression-kriging provided a geographical gradient of species richness that resembles purported patterns of other groups and illustrated ubiquitous shortfalls of knowledge about bird diversity. Main conclusions: Geostatistical interpolation, such as regression-kriging, might be a useful tool to overcome shortfalls in knowledge that plague our understanding of geographical gradients of biodiversity, with many applications in ecology, palaeoecology and conservation. © 2020 John Wiley & Sons Lt