京都大学 / 新制・課程博士 / 博士(理学) / 甲第24826号 / 理博第4975号 / 新制||理||1710(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)准教授 中野, 智之, 教授 朝倉, 彰, 教授 森, 哲 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

The global increase in demand for wood products, calls for a more sustainable management of forests to optimize both the production of wood and the conservation of forest biodiversity. In this paper, we evaluate the status and future trends of forest birds in Central European forests, assuming different forest management scenarios that to a varying degree respond to the demand for wood production. To this end, we use niche models (Boosted Regression Trees and Generalized Linear Models) to model the responses of 15 forest bird species to predictors related to forest stand (e.g., stand volume of specific tree species) and landscape structure (e.g., percentage cover), and to climate (bioclimatic variables). We then define five distinct forest management scenarios, ranging from set-aside to productivity-driven scenarios, project them 100 years into the future, and apply our niche models into these scenarios to assess the birds’ responses to different forest management alternatives. Our models show that the species’ responses to management vary reflecting differences in their ecological niches, and consequently, no single management practice can benefit all species if applied across the whole landscape. Thus, we conclude that in order to promote the overall forest bird species richness in the study region, it is necessary to manage the forests in a multi-functional way, e.g., by spatially optimizing the management practices in the landscape.

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Spruce-Fir forests are relicts from the Pleistocene and can only be found within the Southern Appalachians. Analyzing the relationships between species distribution, climatic parameters, topography, and biotic interactions through ecological niche modeling creates prediction maps for conservation efforts. Maxent, Boosted Regression, and Random Forest were utilized to compare which model and variable combinations best approximate the unique mountain forest environment. Maxent with a bias file produced optimal results and was used to examine distributional changes that may occur in the future and how these changes compare to paleo-environmental distributions. Fraser fir has shown evidence of being influenced by changing climates based on historical data and in future predictions. These findings show areas of decline in 2050 and 2070. When combined with weather, climate, genetics, and ecological studies, this is a useful tool for resource allocation to areas that are predicted to be resilient in the face of climate change.

Distributional ecology is a branch of ecology which aims to reconstruct and predict the geographic range of free-living and symbiotic organisms in terrestrial and aquatic ecosystems. More recently, distributional ecology has been used to map disease transmission risk. The implementation of distributional ecology for disease transmission has, however, been erroneous in many cases. The inaccurate representation of disease distribution is detrimental to effective control and prevention. Furthermore, ecological niche modeling experiments are generally developed and tested using data from terrestrial organisms, neglecting aquatic organisms in case studies. Both disease and aquatic systems are often data limited, and current modeling methods are often insufficient. There is, therefore, a need to develop data-driven models that perform accurately even when only limited amounts of data are available or when there is little to no knowledge of the species' natural history to be modeled. Here, I propose a data-driven ecological niche modeling method that requires presence-only data (i.e., absence, pseudoabsence, or background records are not needed for model calibration). My method is expected to reconstruct environmental conditions where data-limited aquatic organisms are more likely to be present, based on a density-based clustering algorithm as a proxy of the realized niche (i.e., abiotic, and biotic environmental conditions occupied by the organism). Supported by ecological theories and methods, my central hypothesis is that because density-based clustering machine-learning modeling prevents extrapolation and interpolation, it can robustly reconstruct the realized niche of a data-limited aquatic organism. First, I assembled a comprehensive dataset of abiotic (temperature) and biotic (phytoplankton) environmental conditions and presence reports using Vibrio cholerae, a well-understood aquatic bacterium species in coastal waters globally (Chapter 2). Second, using V. cholerae as a model system, I developed detailed parameterizations of density-based clustering models to determine the parameter values with the best capacities to reconstruct and predict the species' distribution in global seawaters (Chapter 3). Finally, I compared the performance of density-based clustering modeling against traditional, correlative machine-learning ecological niche modeling methods (Chapter 4). Density-based clustering models, when assessed based on model fit and prediction, had comparable performance to traditional 'data-hungry' machine-learning correlative methods used in modern applications of ecological niche modeling. Modeling the environmental and geographic ranges of V. cholerae, an aquatic organism of free-living and parasitic ecologies, is a novel approach itself in distributional ecology. Ecological niche modeling applications to pathogens, such as V. cholerae, provide an opportunity to further the knowledge of directly-transmitted emerging diseases for which only limited data are available. Density-based clustering ecological niche modeling is termed here as Marble, honoring a previous, experimental version of this analytical approach, and is expected to provide new opportunities to understand how an ecological niche modeling method influences estimates of the distribution of data-limited organisms of complex ecology. These are lessons applicable to novel, rare, and cryptic aquatic organisms, such as emerging diseases, endangered fishes, and elusive aquatic species. / Master of Science / Distributional ecology is a branch of ecology which aims to reconstruct and predict the geographic distribution of land and water organisms. In the case of diseases, a correct representation of their geographic distributions is key for successful management. Previous studies highlight the need to develop new models that perform accurately even when limited amounts of data are available and there is little to no knowledge of the organisms' ecology. This thesis proposes a data-driven method, originally termed Marble. Marble is expected to help reconstruct environmental conditions where data-limited aquatic organisms are more likely to be found. Supported by ecological theories and methods, my hypothesis is that because Marble prevents under- and over-fitting, this method will produce results which better fit the data. Using V. cholerae, an aquatic organism, as a model system, I compared the performance of Marble against other traditional modeling algorithms. I found that Marble, in terms of model fit, performed similarly to traditional methods used in distributional ecology. Modeling the ecology of V. cholerae is a new approach in and of itself in ecological modeling. Furthermore, modeling pathogens provides an opportunity to further the knowledge of directly transmitted diseases, and Marble is expected to provide opportunities to understand how algorithm selection can reconstruct (or not) the distribution of data-limited aquatic organisms of diverse ecologies.

Coffea arabica is a species with far-reaching impacts on the global economy. Nevertheless, climate-related challenges threaten the coffee industry at its source: its growing regions. The coffee industry is a significant economic driver in Guatemala, but farmers are increasingly reporting losses in crop yield and arable land due to climate-related challenges. Ecological niche modeling (ENM) can be employed to make predictions about the current and future suitability of regions for a species by identifying significant biotic or abiotic indicators. An ENM was used to project suitable land into the future using climate change projection models known as representative concentration pathways (RCPs), for the coffee plant and a number of other species. Due to the potential of shade trees to lessen heat stress on coffee plants, common shade trees for the region were modeled. Additionally, a fungus species responsible for detrimental coffee leaf rust was modeled. Results of these models indicated potential for substantial climate-related habitat losses for the coffee plant in the coming decades. Examination of model predictions allow for greater understanding of the climate-related variables affecting the ecology of the coffee plant, and the potential risks to the industry, in a changing climate. Additionally, ENM models for coffee rust and shade trees can help Guatemalan farmers make informed decisions about farm management.

Coffea arabica is a species with far-reaching impacts on the global economy. Nevertheless, climate-related challenges threaten the coffee industry at its source: its growing regions. The coffee industry is a significant economic driver in Guatemala, but farmers are increasingly reporting losses in crop yield and arable land due to climate-related challenges. Ecological niche modeling (ENM) can be employed to make predictions about the current and future suitability of regions for a species by identifying significant biotic or abiotic indicators. An ENM was used to project suitable land into the future using climate change projection models known as representative concentration pathways (RCPs), for the coffee plant and a number of other species. Due to the potential of shade trees to lessen heat stress on coffee plants, common shade trees for the region were modeled. Additionally, a fungus species responsible for detrimental coffee leaf rust was modeled. Results of these models indicated potential for substantial climate-related habitat losses for the coffee plant in the coming decades. Examination of model predictions allow for greater understanding of the climate-related variables affecting the ecology of the coffee plant, and the potential risks to the industry, in a changing climate. Additionally, ENM models for coffee rust and shade trees can help Guatemalan farmers make informed decisions about farm management.

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第25390号 / 人博第1132号 / 新制||人||263(附属図書館) / 京都大学大学院人間・環境学研究科相関環境学専攻 / (主査)教授 西川 完途, 教授 市岡 孝朗, 教授 瀬戸口 浩彰, 教授 本川 雅治 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

Drosophila buzzatii é uma espécie cactófila associada a diferentes espécies de cactos e distribuída nos diferentes Domínios fitogeográficos da América do Sul. Baseado na diversidade de inversões cromossômicas e densidade populacional, o Chaco foi considerado por alguns autores como o centro de origem da espécie. Entretanto, trabalhos recentes, utilizando aloenzimas e DNA mitocondrial, apontaram para uma possível origem na Caatinga. Os objetivos deste trabalho foram delinear rotas de dispersão da espécie para explicar sua distribuição atual e possível distribuição durante o último período glacial, na América do Sul. Foram obtidas seqüências de 714 pb da COI do mtDNA de 132 indivíduos em 44 localidades, gerando 36 haplótipos. Foram calculados os índices de diversidade nucleotídica, o teste AMOVA, testes de neutralidade, a Mismatch Distribution, Baesyan Skyline Plot, NCPA e o sentido dos movimentos migratórios (Migrate N) a fim de determinar parte da história evolutiva da espécie. As diversidades nucleotídicas encontradas por Domínio foram de 0,0030 Caatinga; 0,0019 - Mata Atlântica; 0,0020 Cerrado; 0,0011 - Pampas e 0,0004 - Chaco. A AMOVA mostrou que 68,33% da variação é intra-populacional e que uma porção significativa da variação é devido a diferenças inter-regionais (ct = 0,07124 p = 0,00196). Os testes de Neutralidade (D de Tajima = -2,4150, p = 0,0317 e Fs de Fu = -28,6719, p = 0,00001), a forma em estrela da rede e haplótipos, e a Mismatch Distribution confirmam um evento de expansão populacional estimado em aproximadamente 494.257,3 anos atrás, segundo modelo de Rogers e Harpending (1992). Entretanto, a Baesyan Skiline Plot demonstrou que esse movimento de expansão parece ser mais antigo, cerca de 550.000 650.000 anos atrás. A NCPA demonstrou que há fluxo gênico restrito com isolamento por distância, confirmado pelo teste de Mantel e alguma dispersão a longa distância em alguns dos clados analisados. O resultado do programa Migrate N indicou um padrão complexo migratório entre os domínios, porém um padrão norte/sul pôde ser verificado. A estruturação genética pode ser explicada devido à grande área de distribuição da espécie, gerando isolamento por distância e pela presença de barreiras geográficas e climáticas (entre o Cerrado e a Caatinga) e no estado do Rio de Janeiro e Espírito Santo (ao longo da Mata Atlântica) onde há pouco ou nenhum indivíduo da espécie. Os eventos de expansão ocorreram no Quaternário durante o período glacial conhecido como Ilinioian e suas subdivisões. A partir dos resultados deste trabalho foi possível traçar diversas rotas de migração possíveis entre os domínios utilizados, sendo que o movimento mais antigo partiu da Caatinga o que vai contra a hipótese de que o Centro de Origem seja o Chaco. / The fruit-fly Drosophila buzzatii is a cactophilic species in association with cactus species distributed along the Phytogeographic Domains of Caatinga, Cerrado, Atlantic Forest, Pampas and Chaco. Based in the diversity of chromosomal inversion and populational density of the species, the Chaco Domain was considered the Center of Origin of the D. buzzatii. However, recent works, using allozymes and DNAmt, showed a possible origin of the D. buzzatii in the Caatinga. The objectives of this work were trace historical dispersion routes of D. buzzatii, current and ancient areas of distribution in the South America. We obtained DNA sequences in 132 samples in 44 localities with 714 bp length from the COI mtDNA gene, generating 36 haplotypes. The diversity indexes, AMOVA, neutrality tests, Mismatch Distribution, Baesyan Skyline Plot, NCPA and sense of migration movements was calculated, to describe part of the evolutionary history of the species. The nucleotide diversity was 0,0030 - Caatinga, 0,0019 - Mata Atlântica, 0,0020 - Cerrado, 0,0011 - Pampas and 0,0004 - Chaco. The AMOVA results, grouped by Domain showed that 68,33% of the variation is intra-population and a significant portion of the variation is due to inter-regional differences (ct = 0,07124 p = 0,00196). The Neutrality tests (Tajimas D = -2,4150, p = 0,0317 and Fus Fs = -28,6719, p = 0,00001), the star-shape of the haplotype network, and Mismatch Distribution showed population expansion signs, estimated in 494.257,3 ybp, according Rogers and Harpending model (1992). However, the BSP showed that the movement is ancient, estimated in 550.000 650.000 ybp. The NCPA showed restricted gene flow with isolation by distance, confirmed by the Mantel Test and some long distance dispersion. The results of the program Migrate N showed a complex pattern of migration between the domains, but a north/south pattern could be identified. The genetic structure can be explained to the widespread distribution of the species, that could generate isolation by distance and by the presence of geographic and climatic barriers (between Cerrado and Caatinga domains) and in the States of Rio de Janeiro and Espírito Santo (along the Atlantic Forest) when there is none or few individuals of the species D. buzzatii. The expansion movements occurred in the Quaternary Period during glaciations events in the Illinoian and their subdivisions, due to the decrease of the global moisture that generated favorable conditions to the expansion of the dry vegetation and associated species. Based on the results of this work it was possible to delineate many migration routes between the phytogeographic domains, and the more ancient movement started in the Caatinga and this result not support the hypothesis that Chaco was the Center of Origin from D. buzzatii.