Relevant bibliographies by topics / Joint species distribution models

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  2. Dissertations / Theses
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Academic literature on the topic 'Joint species distribution models'

Author: Grafiati
Published: 30 November 2024

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Journal articles on the topic "Joint species distribution models"

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Zurell, Damaris, Niklaus E. Zimmermann, Helge Gross, Andri Baltensweiler, Thomas Sattler, and Rafael O. Wüest. "Testing species assemblage predictions from stacked and joint species distribution models." Journal of Biogeography 47, no. 1 (June 5, 2019): 101–13. http://dx.doi.org/10.1111/jbi.13608.

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Wilkinson, David P., Nick Golding, Gurutzeta Guillera‐Arroita, Reid Tingley, and Michael A. McCarthy. "A comparison of joint species distribution models for presence–absence data." Methods in Ecology and Evolution 10, no. 2 (November 3, 2018): 198–211. http://dx.doi.org/10.1111/2041-210x.13106.

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Yong, Juan, Guangshuang Duan, Shaozhi Chen, and Xiangdong Lei. "Environmental Response of Tree Species Distribution in Northeast China with the Joint Species Distribution Model." Forests 15, no. 6 (June 13, 2024): 1026. http://dx.doi.org/10.3390/f15061026.

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The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R2 = 0.2326) > model ENV (AUC = 0.7664, Tjur R2 = 0.1454) > model SPACE (AUC = 0.7297, Tjur R2 = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China.
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Ovaskainen, Otso, David B. Roy, Richard Fox, and Barbara J. Anderson. "Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models." Methods in Ecology and Evolution 7, no. 4 (December 18, 2015): 428–36. http://dx.doi.org/10.1111/2041-210x.12502.

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D’Acunto, Laura E., Leonard Pearlstine, and Stephanie S. Romañach. "Joint species distribution models of Everglades wading birds to inform restoration planning." PLOS ONE 16, no. 1 (January 28, 2021): e0245973. http://dx.doi.org/10.1371/journal.pone.0245973.

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Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.
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Hogg, Stephanie Elizabeth, Yan Wang, and Lewi Stone. "Effectiveness of joint species distribution models in the presence of imperfect detection." Methods in Ecology and Evolution 12, no. 8 (July 9, 2021): 1458–74. http://dx.doi.org/10.1111/2041-210x.13614.

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König, Christian, Rafael O. Wüest, Catherine H. Graham, Dirk Nikolaus Karger, Thomas Sattler, Niklaus E. Zimmermann, and Damaris Zurell. "Scale dependency of joint species distribution models challenges interpretation of biotic interactions." Journal of Biogeography 48, no. 7 (April 2021): 1541–51. http://dx.doi.org/10.1111/jbi.14106.

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Gavin, Daniel G., Matthew C. Fitzpatrick, Paul F. Gugger, Katy D. Heath, Francisco Rodríguez-Sánchez, Solomon Z. Dobrowski, Arndt Hampe, et al. "Climate refugia: joint inference from fossil records, species distribution models and phylogeography." New Phytologist 204, no. 1 (July 16, 2014): 37–54. http://dx.doi.org/10.1111/nph.12929.

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Tikhonov, Gleb, Nerea Abrego, David Dunson, and Otso Ovaskainen. "Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context." Methods in Ecology and Evolution 8, no. 4 (April 2017): 443–52. http://dx.doi.org/10.1111/2041-210x.12723.

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Wagner, Tyler, Gretchen J. A. Hansen, Erin M. Schliep, Bethany J. Bethke, Andrew E. Honsey, Peter C. Jacobson, Benjamen C. Kline, and Shannon L. White. "Improved understanding and prediction of freshwater fish communities through the use of joint species distribution models." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 9 (September 2020): 1540–51. http://dx.doi.org/10.1139/cjfas-2019-0348.

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Two primary goals in fisheries research are to (i) understand how habitat and environmental conditions influence the distribution of fishes across the landscape and (ii) make predictions about how fish communities will respond to environmental and anthropogenic change. In inland, freshwater ecosystems, quantitative approaches traditionally used to accomplish these goals largely ignore the effects of species interactions (competition, predation, mutualism) on shaping community structure, potentially leading to erroneous conclusions regarding habitat associations and unrealistic predictions about species distributions. Using two contrasting case studies, we highlight how joint species distribution models (JSDMs) can address the aforementioned deficiencies by simultaneously quantifying the effects of abiotic habitat variables and species dependencies. In particular, we show that conditional predictions of species occurrence from JSDMs can better predict species presence or absence compared with predictions that ignore species dependencies. JSDMs also allow for the estimation of site-specific probabilities of species co-occurrence, which can be informative for generating hypotheses about species interactions. JSDMs provide a flexible framework that can be used to address a variety of questions in fisheries science and management.
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Dissertations / Theses on the topic "Joint species distribution models"

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Valiquette, Samuel. "Sur les données de comptage dans le cadre des valeurs extrêmes et la modélisation multivariée." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS028.

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Cette thèse s’intéresse à certains aspects théoriques de la modélisation des données de comptage. Deux cadres distincts sont abordés : celui des valeurs extrêmes et celui de la modélisation multivariée. Notre première contribution explore, en termes des comportements extrêmes, les liens existants entre le mélange Poisson et sa loi de mélange. Ce travail permet de caractériser et séparer plusieurs familles de lois de mélanges Poisson selon leur comportement en queue. Bien que ce travail soit théorique, nous discutons de son utilité d’un point de vue pratique, notamment pour le choix de la loi de mélange. Notre deuxième contribution porte sur une nouvelle classe de modèles multivariés dénommée Tree Pólya Splitting. Celle-ci repose sur une modélisation hiérarchique et suppose qu’une quantité aléatoire est répartie successivement selon une loi de Pólya à travers une structure d’arbre de partition. Dans ce travail, nous caractérisons les lois marginales univariées et multivariées, les moments factoriels, ainsi que les structures de dépendance (covariance/corrélation) qui en découlent. Nous mettons en évidence, à l’aide d’un jeu de données correspondant à l’abondance de trichoptères, l’intérêt de cette classe de modèles en comparant nos résultats à ceux obtenus, par exemple, avec des modèles de type Poisson log-normale multivariée. Nous concluons cette thèse en présentant diverses perspectives de recherche
This thesis focuses on certain theoretical aspects of counting data modeling. Two distinct frameworks are addressed: extreme values and multivariate modeling. Our first contribution explores, in terms of extreme behaviors, the existing connections between the Poisson mixture and its mixing distribution. This work allows us to characterize and discriminate several families of Poisson mixture according to their tail behavior. Although this work is theoretical, we discuss its practical utility, particularly regarding the choice of the mixing distribution. Our second contribution focuses on a new class of multivariate models called Tree Pólya Splitting. This class is based on hierarchical modeling and assumes that a random quantity is successively divided according to a Pólya distribution through a partition tree structure. In this work, we characterize univariate and multivariate marginal distributions, factorial moments, as well as the resulting dependency structures (covariance/correlation). Using a dataset corresponding to the abundance of Trichoptera, we highlight the interest of this class of models by comparing our results to those obtained, for example, with multivariate Poisson-lognormal models. We conclude this thesis by presenting various perspectives
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Venne, Simon. "Can Species Distribution Models Predict Colonizations and Extinctions?" Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38465.

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Aim MaxEnt, a very popular species distribution modelling technique, has been used extensively to relate species’ geographic distributions to environmental variables and to predict changes in species’ distributions in response to environmental change. Here, we test its predictive ability through time (rather than through space, as is commonly done) by modeling colonizations and extinctions. Location Continental U.S. and southern Canada. Time period 1979-2009 Major taxa studied Twenty-one species of passerine birds. Methods We used MaxEnt to relate species’ geographic distributions to the variation in environmental conditions across North America. We then modelled site-specific colonizations and extinctions between 1979 and 2009 as functions of MaxEnt-estimated previous habitat suitability and inter- annual change in habitat suitability and neighborhood occupancy. We evaluated whether the effects were in the expected direction, we partitioned model’s explained deviance, and we compared colonization and extinction model’s accuracy to MaxEnt’s AUC. Results IV Colonization and extinction probabilities both varied as functions of previous habitat suitability, change in habitat suitability, and neighborhood occupancy, in the expected direction. Change in habitat suitability explained very little deviance compared to other predictors. Neighborhood occupancy accounted for more explained deviance in colonization models than in extinction models. MaxEnt AUC correlates with extinction models’ predictive ability, but not with that of colonization models. Main conclusions MaxEnt appears to sometime capture a real effect of the environment on species’ distributions since a statistical effect of habitat suitability is detected through both time and space. However, change in habitat suitability (which is much smaller through time than through space) is a poor predictor of change in occupancy. Over short time scales, proximity of sites occupied by conspecifics predicts changes in occupancy just as well as MaxEnt. The ability of MaxEnt models to predict spatial variation in occupancy (as measured by AUC) gives little indication of transferability through time. Thus, the predictive value of species distribution models may be overestimated when evaluated through space only. Future prediction of species’ responses to climate change should make a distinction between colonization and extinction, recognizing that the two processes are not equally well predicted by SDMs.
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Mugodo, James, and n/a. "Plant species rarity and data restriction influence the prediction success of species distribution models." University of Canberra. Resource, Environmental & Heritage Sciences, 2002. http://erl.canberra.edu.au./public/adt-AUC20050530.112801.

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There is a growing need for accurate distribution data for both common and rare plant species for conservation planning and ecological research purposes. A database of more than 500 observations for nine tree species with different ecological and geographical distributions and a range of frequencies of occurrence in south-eastern New South Wales (Australia) was used to compare the predictive performance of logistic regression models, generalised additive models (GAMs) and classification tree models (CTMs) using different data restriction regimes and several model-building strategies. Environmental variables (mean annual rainfall, mean summer rainfall, mean winter rainfall, mean annual temperature, mean maximum summer temperature, mean minimum winter temperature, mean daily radiation, mean daily summer radiation, mean daily June radiation, lithology and topography) were used to model the distribution of each of the plant species in the study area. Model predictive performance was measured as the area under the curve of a receiver operating characteristic (ROC) plot. The initial predictive performance of logistic regression models and generalised additive models (GAMs) using unrestricted, temperature restricted, major gradient restricted and climatic domain restricted data gave results that were contrary to current practice in species distribution modelling. Although climatic domain restriction has been used in other studies, it was found to produce models that had the lowest predictive performance. The performance of domain restricted models was significantly (p = 0.007) inferior to the performance of major gradient restricted models when the predictions of the models were confined to the climatic domain of the species. Furthermore, the effect of data restriction on model predictive performance was found to depend on the species as shown by a significant interaction between species and data restriction treatment (p = 0.013). As found in other studies however, the predictive performance of GAM was significantly (p = 0.003) better than that of logistic regression. The superiority of GAM over logistic regression was unaffected by different data restriction regimes and was not significantly different within species. The logistic regression models used in the initial performance comparisons were based on models developed using the forward selection procedure in a rigorous-fitting model-building framework that was designed to produce parsimonious models. The rigorous-fitting modelbuilding framework involved testing for the significant reduction in model deviance (p = 0.05) and significance of the parameter estimates (p = 0.05). The size of the parameter estimates and their standard errors were inspected because large estimates and/or standard errors are an indication of model degradation from overfilling or effecls such as mullicollinearily. For additional variables to be included in a model, they had to contribule significantly (p = 0.025) to the model prediclive performance. An attempt to improve the performance of species distribution models using logistic regression models in a rigorousfitting model-building framework, the backward elimination procedure was employed for model selection, bul it yielded models with reduced performance. A liberal-filling model-building framework that used significant model deviance reduction at p = 0.05 (low significance models) and 0.00001 (high significance models) levels as the major criterion for variable selection was employed for the development of logistic regression models using the forward selection and backward elimination procedures. Liberal filling yielded models that had a significantly greater predictive performance than the rigorous-fitting logistic regression models (p = 0.0006). The predictive performance of the former models was comparable to that of GAM and classification tree models (CTMs). The low significance liberal-filling models had a much larger number of variables than the high significance liberal-fitting models, but with no significant increase in predictive performance. To develop liberal-filling CTMs, the tree shrinking program in S-PLUS was used to produce a number of trees of differenl sizes (subtrees) by optimally reducing the size of a full CTM for a given species. The 10-fold cross-validated model deviance for the subtrees was plotted against the size of the subtree as a means of selecting an appropriate tree size. In contrast to liberal-fitting logistic regression, liberal-fitting CTMs had poor predictive performance. Species geographical range and species prevalence within the study area were used to categorise the tree species into different distributional forms. These were then used, to compare the effect of plant species rarity on the predictive performance of logistic regression models, GAMs and CTMs. The distributional forms included restricted and rare (RR) species (Eucalyptus paliformis and Eucalyptus kybeanensis), restricted and common (RC) species (Eucalyptus delegatensis, Eucryphia moorei and Eucalyptus fraxinoides), widespread and rare (WR) species (Eucalyptus data) and widespread and common (WC) species (Eucalyptus sieberi, Eucalyptus pauciflora and Eucalyptus fastigata). There were significant differences (p = 0.076) in predictive performance among the distributional forms for the logistic regression and GAM. The predictive performance for the WR distributional form was significantly lower than the performance for the other plant species distributional forms. The predictive performance for the RC and RR distributional forms was significantly greater than the performance for the WC distributional form. The trend in model predictive performance among plant species distributional forms was similar for CTMs except that the CTMs had poor predictive performance for the RR distributional form. This study shows the importance of data restriction to model predictive performance with major gradient data restriction being recommended for consistently high performance. Given the appropriate model selection strategy, logistic regression, GAM and CTM have similar predictive performance. Logistic regression requires a high significance liberal-fitting strategy to both maximise its predictive performance and to select a relatively small model that could be useful for framing future ecological hypotheses about the distribution of individual plant species. The results for the modelling of plant species for conservation purposes were encouraging since logistic regression and GAM performed well for the restricted and rare species, which are usually of greater conservation concern.
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Rapacciuolo, Giovanni. "Predicting species' range shifts under global change : when can species distribution models be useful?" Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/18025.

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Predicting how species’ distributions will change in response to environmental changes is fundamental for many aspects of agriculture, ecosystem service provision, human and animal health, and wildlife conservation. Correlative species distribution models (SDMs) are the primary tool for making such predictions; however, assessing their predictive accuracy is notoriously difficult, since predicted events are yet to occur. In this thesis, I tested the temporal transferability of widely-used SDMs based on coarse climate and land cover variables by validating these against records of recently-observed distribution changes in British vascular plants, birds, and butterflies. When transferred to a new time period, SDMs were generally accurate at discriminating between presence and absence across large portions of species’ ranges that had remained unchanged through time; however, their discrimination ability over portions of species ranges which had been observed to change occupancy status in time was no better than random. When considering the probabilistic nature of predictions, this lack of discrimination ability over dynamic portions of species’ ranges translated into significant deviations between values of predicted and observed probabilities of species’ gain and loss at given sites. Nonetheless, species’ gains and losses were more likely to be observed at sites with higher predicted probabilities of gain and loss, respectively. In addition, there was considerable variation in the temporal transferability of models with some models being more transferable than others. Differences amongst species were a major determinant of this variation. In particular, models of species found across a distinct and narrow range of environments, which also occurred across most geographical sites satisfying these requirements, were more transferable over time than others. Other factors leading to significant variation in temporal transferability were the choice of modelling framework and the geographical area over which a model was projected. My findings suggest that SDMs are unlikely to generate projections of single species’ range shifts that are accurate enough to provide a solid basis for local-scale conservation and management. However, they may be useful in pinpointing species and areas that are particularly vulnerable to environmental change and should thus be monitored closely. Clearly, more processes will need to be explicitly accounted for in our predictive models if we want to significantly increase their biological realism in the face of environmental change.
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Marshall, Charlotte Emily. "Species distribution modelling to support marine conservation planning." Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/1176.

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This thesis explores some important practical considerations concerning the use of species distribution models in marine conservation planning. Using geo-referenced gorgonian distribution data, together with explanatory environmental variables, predictive models have been used to map the spatial distribution of suitable gorgonian (sea fan) habitat in two study sites; Hatton Bank, in the Northeast Atlantic, and Lyme Bay on the south coast of Devon. Generalized Linear Models (GLMs), Generalized Additive Models (GAMs) and a Maximum Entropy (Maxent) model have been used to support critical investigation into important model considerations that have received inadequate attention in the marine environment. The influence of environmental data resolution on model performance has been explored with specific reference to available datasets in the nearshore and offshore environments. The transferability of deep-sea models has been similarly appraised, with recommendations as to the appropriate use of transferred models. Investigating these practical issues will allow managers to make informed decisions with respect to the best and most appropriate use of existing data. This study has also used novel approaches and investigated their suitability for marine conservation planning, including the use of model classification error in the spatial prioritisation of monitoring sites, and the adaptation of an existing presence-only modelling method to include absence data. Together, these studies contribute both practical recommendations for marine conservation planning and novel applications within the wider species distribution modelling discipline, and consider the implications of these developments for managers, to ensure the ongoing improvement and development of models to support conservation planning.
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ONGARO, SILVIA. "Ecology of Mediterranean lichens and plants: application of species distribution models." Doctoral thesis, Università degli Studi di Trieste, 2019. http://hdl.handle.net/11368/2962383.

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Many studies include Species Distribution Modelling (SDM) techniques to define spatial biogeographic patterns of species and to explore ecological variability. As first, a study on the ecology of infrageneric taxa of genus Solenopsora is presented. The genus includes 25 species of foliose, rupicolous lichens, mostly occurring in temperate and subtropical regions. In Europe, only some taxa range from central Europe (with continental/Atlantic climate) to the Mediterranean, while others are restricted to Mediterranean area. The aim was to explore ecological variability of infrageneric taxa (with diverse distributions), for understanding whether the responses to environmental factors are species-specific. SDMs is used to predict taxa distributions using future climatic scenarios, which take in account climate change effects. It is a fact that climate trends nowadays are anomalous if compared with previous climatic oscillations of the Earth. Climate change already generated distributional range shifts for several species, and increased risk of extinction, or of invasion, disrupting ecosystem dynamics and services. Policy and decision-making can be supported by SDMs, thus improving the effectiveness of global change mitigation strategies. Since conservation efforts can be expensive, and time consuming, SDMs provide a way for optimizing and better addressing resources. The second section host a study on current and future (2070) distribution models of some Sardinian orchids. The Mediterranean is one of the most important biodiversity hot-spots in the world, labelled also as a climate change hot-spot. Plants distribution in the Mediterranean area was strongly influenced by its ecological complexity. In this area, climate changes could heavily impact communities, and the most relevant effect could be an increased occurrence of taxa adapted to dry and hot conditions, leading extinction of endemic taxa. In Europe, Sardinia is one of orchids major biodiversity hotspot (60 taxa). Sardinian ecosystems are changing in consequence of climate change, and while some species are reducing their habitat, others are widening their distribution. Future climate change scenarios for Sardinia depict a situation in which the average monthly temperatures increase, and precipitations increase by 20–50% in the cold season, and decrease of 20–50% in the rest of the year. The third study is on SDMs used for investigating taxonomic questions. The case of Pterygiopsis affinis, a rare crustose cyanolichen, adapted to the Mediterranean climate, is investigated. P. affinis is known to reproduce by apothecia. But, a sorediate morphotype was discovered in S Italy: its identity was never deeply investigated before. SDMs, together with morphological analysis, are used here to understand if this morphotype is an ecological variation of the species, or a new taxonomic entity. Most primitive lichens had a crustose, unorganized thallus, without an actual stratification, with blue algae as photobiont. These primitive lichens, still occurring in many environments, are normally crustose pioneer cyanolichens. They have an extreme phenotypic plasticity, ranging from “typical” to very uncommon forms, such as chimeras. This variability can also affect the reproductive strategies: some lichens can switch to asexual reproductive strategies, if driven by a limited availability of free-living algae. Taxonomy of lichens is traditionally based on anatomy and morphology of the fruiting bodies. Taxa which reproduce by vegetative structures only are normally treated separately from sexually reproducing ones. But, according to the hypothesis of species pairs, the same taxon can adopt different reproductive strategies in different ecological conditions. For all these reasons, the collocation of some taxa of cyanolichens is difficult, and species distribution modelling can be helpful in depicting the niche, and supporting taxonomical delimitation in complex situations.
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Wong, Po-shing, and 黃寶誠. "Some mixture models for the joint distribution of stock's return and trading volume." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210065.

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Wong, Po-shing. "Some mixture models for the joint distribution of stock's return and trading volume /." [Hong Kong] : University of Hong Kong, 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13009485.

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Scott-Hayward, Lindesay Alexandra Sarah. "Novel methods for species distribution mapping including spatial models in complex regions." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4514.

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Species Distribution Modelling (SDM) plays a key role in a number of biological applications: assessment of temporal trends in distribution, environmental impact assessment and spatial conservation planning. From a statistical perspective, this thesis develops two methods for increasing the accuracy and reliability of maps of density surfaces and provides a solution to the problem of how to collate multiple density maps of the same region, obtained from differing sources. From a biological perspective, these statistical methods are used to analyse two marine mammal datasets to produce accurate maps for use in spatial conservation planning and temporal trend assessment. The first new method, Complex Region Spatial Smoother [CReSS; Scott-Hayward et al., 2013], improves smoothing in areas where the real distance an animal must travel (`as the animal swims') between two points may be greater than the straight line distance between them, a problem that occurs in complex domains with coastline or islands. CReSS uses estimates of the geodesic distance between points, model averaging and local radial smoothing. Simulation is used to compare its performance with other traditional and recently-developed smoothing techniques: Thin Plate Splines (TPS, Harder and Desmarais [1972]), Geodesic Low rank TPS (GLTPS; Wang and Ranalli [2007]) and the Soap lm smoother (SOAP; Wood et al. [2008]). GLTPS cannot be used in areas with islands and SOAP can be very hard to parametrise. CReSS outperforms all of the other methods on a range of simulations, based on their fit to the underlying function as measured by mean squared error, particularly for sparse data sets. Smoothing functions need to be flexible when they are used to model density surfaces that are highly heterogeneous, in order to avoid biases due to under- or over-fitting. This issue was addressed using an adaptation of a Spatially Adaptive Local Smoothing Algorithm (SALSA, Walker et al. [2010]) in combination with the CReSS method (CReSS-SALSA2D). Unlike traditional methods, such as Generalised Additive Modelling, the adaptive knot selection approach used in SALSA2D naturally accommodates local changes in the smoothness of the density surface that is being modelled. At the time of writing, there are no other methods available to deal with this issue in topographically complex regions. Simulation results show that CReSS-SALSA2D performs better than CReSS (based on MSE scores), except at very high noise levels where there is an issue with over-fitting. There is an increasing need for a facility to combine multiple density surface maps of individual species in order to make best use of meta-databases, to maintain existing maps, and to extend their geographical coverage. This thesis develops a framework and methods for combining species distribution maps as new information becomes available. The methods use Bayes Theorem to combine density surfaces, taking account of the levels of precision associated with the different sets of estimates, and kernel smoothing to alleviate artefacts that may be created where pairs of surfaces join. The methods were used as part of an algorithm (the Dynamic Cetacean Abundance Predictor) designed for BAE Systems to aid in risk mitigation for naval exercises. Two case studies show the capabilities of CReSS and CReSS-SALSA2D when applied to real ecological data. In the first case study, CReSS was used in a Generalised Estimating Equation framework to identify a candidate Marine Protected Area for the Southern Resident Killer Whale population to the south of San Juan Island, off the Pacific coast of the United States. In the second case study, changes in the spatial and temporal distribution of harbour porpoise and minke whale in north-western European waters over a period of 17 years (1994-2010) were modelled. CReSS and CReSS-SALSA2D performed well in a large, topographically complex study area. Based on simulation results, maps produced using these methods are more accurate than if a traditional GAM-based method is used. The resulting maps identified particularly high densities of both harbour porpoise and minke whale in an area off the west coast of Scotland in 2010, that might be a candidate for inclusion into the Scottish network of Nature Conservation Marine Protected Areas.
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Galaiduk, Ronen. "Spatial ecology and ontogeny: incorporating fish size-classes into species distribution models." Thesis, Curtin University, 2016. http://hdl.handle.net/20.500.11937/51887.

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I use spatial modelling to investigate how relative abundance and body-length can be used to identify niche requirements and habitat partitioning between conspecific fishes. The models identified specific areas associated with juveniles and adults of same or multiple fish species. I investigate the performance of models using data collected from towed and baited video systems. The towed video models detected fine-scale environmental niche associations of fish, which could be missed by commonly used baited systems.
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Books on the topic "Joint species distribution models"

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Guidolin, Massimo. An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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Flood, Robert P. A model of the joint distribution of banking and exchange-rate crises. [Washington, D.C.]: International Monetary Fund, Research Department, 2001.

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Bio, Ana M. F. Does vegetation suit our models?: Data and model assumption and the assessment of species distribution in space. Utrecht: Royal Dutch Geographical Society, 2000.

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Bio, Ana M. F. Does vegetation suit our models?: Data and model assumptions and the assessment of species distribution in space. Utrecht: The Royal Dutch Geographical Society, 2000.

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Peters, Matthew P. Integrating fine-scale soil data into species distribution models: Preparing soil survey geographic (SSURGO) data from multiple counties. Newtown Square, PA: U.S. Dept. of Agriculture, Forest Service, Northern Research Station, 2013.

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Borooah, Vani K. Was there a regional dimension to changes in income inequality in the UK over 1982-92?: An analysis based on a joint decomposition of income inequality by region and by employment status. [Cambridge]: [University of Cambridge, Department of Applied Economics], 1995.

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Bodenheimer, Peter. Numerical studies of collapsing interstellar clouds: NASA/Ames-University of California, Santa Cruz, Joint Research Interchange NAG 2-368 : final technical report, May 1, 1987. [Washington, D.C: National Aeronautics and Space Administration, 1987.

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Kam-Pui, Lee, Gupta Roop N, and Langley Research Center, eds. Computer codes for the evaluation of thermodynamic properties, transport properties, and equilibrium constants of an 11-species air model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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C, Bridger Alison F., Haberle Robert M, and United States. National Aeronautics and Space Administration., eds. Mars Global Surveyor: Aerobraking and observations support using a Mars global circulation model : a NASA Ames Research Center Joint Research Interchange, final report : university consortium agreement NCC2-5148; project duration, 25 July 1995-24 October 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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C, Bridger Alison F., Haberle Robert M, and United States. National Aeronautics and Space Administration., eds. Mars Global Surveyor: Aerobraking and observations support using a Mars global circulation model : a NASA Ames Research Center Joint Research Interchange, final report : university consortium agreement NCC2-5148; project duration, 25 July 1995-24 October 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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Book chapters on the topic "Joint species distribution models"

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Ovaskainen, Otso. "Species distribution models." In Handbook of Environmental and Ecological Statistics, 275–98. Boca Raton : Taylor & Francis, 2018.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315152509-12.

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Miller, Jennifer A., and Janet Franklin. "Incorporating Spatial Autocorrelation in Species Distribution Models." In Handbook of Applied Spatial Analysis, 685–702. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03647-7_32.

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Wu, Ximing, Andreas Savvides, and Thanasis Stengos. "The Global Joint Distribution of Income and Health." In Recent Advances in Estimating Nonlinear Models, 249–79. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8060-0_12.

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Lawler, Josh J., Yolanda F. Wiersma, and Falk Huettmann. "Using Species Distribution Models for Conservation Planning and Ecological Forecasting." In Predictive Species and Habitat Modeling in Landscape Ecology, 271–90. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7390-0_14.

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Borba, Cleverton, and Pedro Luiz Pizzigatti Correa. "Application of Metadata Standards for Interoperability Between Species Distribution Models." In Communications in Computer and Information Science, 113–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24129-6_10.

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Deneu, Benjamin, Maximilien Servajean, Christophe Botella, and Alexis Joly. "Evaluation of Deep Species Distribution Models Using Environment and Co-occurrences." In Lecture Notes in Computer Science, 213–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28577-7_18.

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Eckstein, Felix, Beat Merz, and Christopher R. Jacobs. "Basis for the Design of the Computer Models." In Effects of Joint Incongruity on Articular Pressure Distribution and Subchondral Bone Remodeling, 7–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57184-8_2.

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Hudson, Irene L., Susan W. Kim, and Marie R. Keatley. "Modelling the Flowering of Four Eucalypt Species Using New Mixture Transition Distribution Models." In Phenological Research, 299–320. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3335-2_14.

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Johnson, Chris J., Michael Hurley, Eric Rapaport, and Michael Pullinger. "Using Expert Knowledge Effectively: Lessons from Species Distribution Models for Wildlife Conservation and Management." In Expert Knowledge and Its Application in Landscape Ecology, 153–71. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1034-8_8.

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Pinto-Ledezma, Jesús N., and Jeannine Cavender-Bares. "Using Remote Sensing for Modeling and Monitoring Species Distributions." In Remote Sensing of Plant Biodiversity, 199–223. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_9.

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AbstractInterpolated climate surfaces have been widely used to predict species distributions and develop environmental niche models. However, the spatial coverage and density of meteorological sites used to develop these surfaces vary among countries and regions, such that the most biodiverse regions often have the most sparsely sampled climatic data. We explore the potential of satellite remote sensing (S-RS) products—which have consistently high spatial and temporal resolution and nearly global coverage—to quantify species-environment relationships that predict species distributions. We propose several new environmental metrics that take advantage of high temporal resolution in S-RS data and compare these approaches to classic climate-only approaches using the live oaks (Quercus section Virentes) as a case study. We show that models perform similarly but for some species, particularly in understudied regions, show less precision in predicting spatial distribution. These results provide evidence supporting efforts to enhance environmental niche models and species distribution models (ENMs/SDMs) with S-RS data and, when combined with other approaches for species detection, will likely enhance our ability to monitor biodiversity globally.
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Conference papers on the topic "Joint species distribution models"

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Chen, Di, Yexiang Xue, Daniel Fink, Shuo Chen, and Carla P. Gomes. "Deep Multi-species Embedding." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/509.

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Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project eBird, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.
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Kong, Shufeng, Junwen Bai, Jae Hee Lee, Di Chen, Andrew Allyn, Michelle Stuart, Malin Pinsky, Katherine Mills, and Carla Gomes. "Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/603.

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A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to the absence of species for a large number of sites. The problem of tackling both issues simultaneously, which we refer to as the zero-inflated multi-target regression problem, has not been addressed by previous methods in statistics and machine learning. In this paper, we propose a novel deep model for the zero-inflated multi-target regression problem. To this end, we first model the joint distribution of multiple response variables as a multivariate probit model and then couple the positive outcomes with a multivariate log-normal distribution. By penalizing the difference between the two distributions’ covariance matrices, a link between both distributions is established. The whole model is cast as an end-to-end learning framework and we provide an efficient learning algorithm for our model that can be fully implemented on GPUs. We show that our model outperforms the existing state-of-the-art baselines on two challenging real-world species distribution datasets concerning bird and fish populations.
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Engdar, Ulf, Per Nilsson, and Jens Klingmann. "Investigation of Turbulence Models Applied to Premixed Combustion Using a Level-Set Flamelet Library Approach." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38331.

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Most of the common modeling approaches to premixed combustion in engineering applications are either based on the assumption of infinitely fast chemistry or the flamelet assumption with simple chemistry. The level-set flamelet library approach (FLA) has shown great potential in predicting major species and heat release, as well as intermediate and minor species, where more simple models often fail. In this approach, the mean flame surface is tracked by a level-set equation. The flamelet libraries are generated by an external code, which employs a detailed chemical mechanism. However, a model for the turbulent flame speed is required, which, amongst other considerations, depends on the turbulence intensity, i.e. these models may show sensitivity to turbulence modeling. In this paper, the FLA model was implemented in the commercial CFD program Star-CD, and applied to a lean premixed flame stabilized by a triangular prism (bluff body). The objective of this paper has been to investigate the impact on the mean flame position, and hence on the temperature and species distribution, using three different turbulent flame speed models in combination with four different turbulence models. The turbulence models investigated are: the standard k-ε model, a cubic non-linear k-ε model, the standard k-ω model and the Shear Stress Transport (SST) k-ω model. In general, the computed results agree well with experimental data for all computed cases, although the turbulence intensity is strongly underestimated at the downstream position. The use of the non-linear k-ε model offers no advantage over the standard model, regardless of flame speed model. The k-ω based turbulence models predict the highest turbulence intensity with the shortest flame lengths as a consequence. The Mu¨ller flame speed model shows the least sensitivity to the choice of turbulence model.
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Abyaneh, M. H. J., and M. H. Saidi. "Velocity Distributions in (r,θ) Directions for Laminar Flow of a Film Around Horizontal Circular Tube." In ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/fedsm2006-98087.

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Velocity distributions in (r,θ) directions are evaluated by solving simultaneous simplified Navier-Stokes equations (NSE) and continuity equation (CE) in polar coordinate. The analysis is based on steady state laminar flow of thin falling liquid film on a horizontal circular tube, for cases in which traction on the film surface is considered negligible. It is a common geometry for part of engineering problems such as evaporator, condenser, absorber, generator of absorption chillers and other similar units in mechanical and chemical engineering. Knowledge of the velocity profiles is usually needed for: 1- solving governing energy and species equations 2- estimating the average and film surface velocity, and 3- evaluating film thickness distribution and its gradient. Two models of velocity distributions are considered, namely actual model and simplified model. Models are compared not only with each other but also with semi actual model in (x,y) coordinate given in the literatures. The average and film surface velocity profiles and film thickness distribution for these models have been shown in various conditions. The results clearly show that the larger flow rates and/or smaller tube diameter increases the calculation error.
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Chen, Lu, and Francine Battaglia. "Computational Study Comparing Reduced Chemical Mechanisms With the PDF Method in Non-Premixed Flames." In ASME 2016 Fluids Engineering Division Summer Meeting collocated with the ASME 2016 Heat Transfer Summer Conference and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/fedsm2016-7543.

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Reactive flow simulations involving turbulence-chemistry interactions can be very challenging because of the strong non-linear coupling between chemistry and fluid dynamics. Furthermore, the chemistry is described with hundreds of reactions, which is prohibitive to solve using computational fluid dynamics (CFD). Using a reduced set of mechanisms that contains a subset of the important species is more practical. However, the species modeled must capture the key combustion characteristics of interest, such as ignition, species distributions and major pollutant formation. Previously, the authors used the joint probability density function (PDF) to study the non-premixed turbulent flames, and continue the work here. New CFD simulations were conducted for a non-premixed turbulent syngas flame using four reduced mechanisms models (3-step, 8-step, 9-step and 12-step reactions) to assess the predictive capabilities in the calculation of turbulence-chemistry interactions. The performance of the different reduced mechanism models was assessed and compared with previous PDF model results and the experimental results of Correa and Gulati (1992, “Measurements and Modeling of a Bluff Body Stabilized Flame,” Combustion and Flame, 89(2)). The predictions of temperature and species from the reduced mechanisms of the 3-step and 8-step were found to have differences as large as 20%. It was also found that the reduced 12-step mechanism was able to represent the strong turbulence-chemistry interactions in the syngas flame and demonstrated good ability of predicting species distribution. Therefore, a simplified chemical mechanism model was successfully developed to simulate the non-premixed syngas flame. The 12-step reduced mechanism will guide other reduced mechanism models for syngas fuels. However, the PDF method still gives the best predictions of temperature and requires the smallest computational time.
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Arai, Norio, Takahisa Yamamoto, and Tomohiko Furuhata. "Numerical Simulation of Low Heating Value Fuel Turbulent Diffusion Combustion." In 2002 International Joint Power Generation Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/ijpgc2002-26113.

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In this study, in order to obtain fundamental data for designing the practical gas turbine combustors in the chemical gas turbine (Ch/GT) combined cycle system and/or other gas turbine systems which utilize low heating value fuel such as coal and biomass gasification syngas, we have simulated low heating value fuel–air turbulent diffusion combustion. The simulated results for the profiles of temperature and species concentrations have been compared with the measured ones. As a reaction model, the flamelet model has been applied to predict the turbulent diffusion combustion characteristics. A flamelet library which indicates relationships for scalar dissipation rate, mixture fraction, and mass fraction of chemical species were given by the calculated results of counterflow diffusion flame using 16 chemical species and 25 elementary reactions. Clipped Gaussian and a log-normal distribution were employed as the PDFs of the mixture fraction and the scalar dissipation rate, respectively. In this model, the k-ε two equations turbulence model is used to describe the turbulent flow field and the radiative heat transfer is calculated by the six-flux model. The governing equations were solved iteratively by the SIMPLE algorithm. By the comparisons of the simulated and measured results, it was clarified that the simulated profiles conformed fairly well to the measured ones in the turbulent combustor, and that this combustion simulation model could predict the low heating value fuel turbulent diffusion combustion characteristics.
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Igi, Satoshi, Takahiro Kubo, and Masayoshi Kurihara. "Ductile Fracture Evaluation of Welded Joints With a Surface Flaw Under Large Deformation." In ASME 2005 Pressure Vessels and Piping Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pvp2005-71521.

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Tensile tests using welded joint specimens with a surface flaw were conducted in order to study the large deformation behavior and ductile fracture of welded structural component with a flaw. Two types of 490MPa class steel plates with different yield strength are prepared for this study. The surface flaw was introduced at the center of welded metal of welded joint specimen. Strain distribution during loading and the maximum strain at ductile fracture of specimen were measured in order to investigate the influence of stress-strain properties on ductile fracture behavior of the welded joint. Three dimensional elastoplastic finite element analyses were also carried out by using the welded joint specimen models in order to calculate the detailed stress and strain distributions around notch tip. In addition, a simplified analytical model to estimate fracture strain and critical flaw size of the welded joint was proposed using the stress-strain relationship combination between base material and welded metal by Swift’s equation. The results from the analytical model show fairly good agreement with experimental results.
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Hayashi, Kosuke, and Akio Tomiyama. "Interface Tracking Simulation of Mass Transfer From a Dissolving Bubble." In ASME-JSME-KSME 2011 Joint Fluids Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajk2011-04007.

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An interface tracking method for predicting bubble dissolution process is proposed. A non-diffusive scheme for advecting species concentrations is adopted to accurately compute the volume change due to mass transfer. The applicability of the proposed method is examined through several benchmark tests, i.e. mass transfer from a static bubble and that from free rising bubbles. Predicted species concentration distributions and mass transfer coefficients agree well with theoretical and empirical models. Dissolution of single carbon dioxide bubbles in a vertical pipe filled with water is also simulated. The bubbles consist only of carbon dioxide, and nitrogen and oxygen are initially dissolved in water. The volume change due to dissolution of carbon dioxide from the bubbles and evaporation of nitrogen and oxygen from water are well predicted.
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Fede, P., O. Simonin, and I. Ghouila. "3D Numerical Simulation of Polydisperse Pressurized Gas-Solid Fluidized Bed." In ASME-JSME-KSME 2011 Joint Fluids Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajk2011-12016.

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Three dimensional unsteady numerical simulations of dense pressurized polydisperse fluidized bed have been carried out. The geometry is a medium-scale industrial pilot for ethylene polymerization. The numerical simulation have been performed with a polydisperse collision model. The consistency of the polydisperse model predictions with the monodisperse ones is shown. The results show that the pressure distribution and the mean vertical gas velocity are not modified by polydispersion of the solid phase. In contrast, the solid particle species are not identically distributed in the fluidized bed indicating the presence of particle segregation.
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Leung, Wallace Woon-Fong, and Yong Ren. "A Numerical Model on Secondary Flow and Mixing in Rotating Microfluidics." In ASME 2010 8th International Conference on Nanochannels, Microchannels, and Minichannels collocated with 3rd Joint US-European Fluids Engineering Summer Meeting. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31060.

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The fluid mechanics of mixing different species in a closed rotating microfluidics chamber was investigated. Complicated secondary flow, in form of vortices, in a rotating chamber was generated for mixing different species by continuous changing the rate of rotation over time until uniform mixing of the different species in the chamber has been attained. Three vortices are observed when reference to the rotating chamber — one main vortex in the R-θ planes, generated from the d’Alembert force, was responsible for momentum and mass transfer while two pairs of toroidal vortices, generated from the Coriolis force, is responsible for momentum and mass transfer in the direction parallel to the rotational axis. These secondary flow and vortices help to reduce the mixing length between species of a given concentration. With a much smaller mixing length, diffusion can further effect the remaining mixing in a more reasonable time for the mixture to attain a more uniform or homogenous condition of the species in the microfluidic chamber. Numerical simulation using ANSYS-CFX was used to solve the transient Navier-Stokes equation in the rotating frame simulating the Newtonian laminar flow pattern for linear acceleration and deceleration (in rotation) schemes. Mass transfer was also modeled using the convective-diffusion equation governing movement of the different species in the chamber after the NS and continuity equations have been solved for the velocity field. Using the numerical model, parametric study of the rotating truncated pie-shaped chamber (radius and angular span) have been carried out to investigate the effect of momentum and mass transfer. An index, the mixing quality based on concentration distribution for the whole domain, was used respectively to quantitatively evaluate the mixing performance.
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Reports on the topic "Joint species distribution models"

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Aiello-Lammens, Matthew E., Robert Anderson, Mary E. Blair, Bethany A. Johnson, Jamie Kass, Sarah I. Meenan, Andrea Paz, Richard Pearson, and Gonzalo E. Pinilla-Buitrago. Species Distribution Modeling for Conservation Educators and Practitioners. American Museum of Natural History, 2023. http://dx.doi.org/10.5531/cbc.ncep.0184.

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Models that predict distributions of species by combining known occurrence records with digital layers of environmental variables have much potential for application in conservation. Through using this module, teachers will enable students to develop species distribution models, to apply the models across a series of analyses, and to interpret predictions accurately. In addition to its original components, this module features an updated and condensed synthesis document ("A Brief Introduction to Species Distribution Modeling for Conservation Educators and Practitioners," which provides theoretical and practical guidance for the expanding field of species distribution modeling. The synthesis is supplemented by a new exercise where learners create and optimize species distribution models using Wallace, an R-based GUI (Graphical User Interface) application for ecological modeling that currently focuses on building, evaluating, and visualizing models of species niches and distributions. Additionally, there are four new PowerPoint presentations on species distribution models (the history and theory, data and algorithms, and evaluating SDMs), as well as a presentation on how to use Wallace. The original Synthesis, "Species' Distribution Modeling for Conservation Educators and Practitioners," introduces learners to the modeling approach, outlines key concepts and terminology, and describes questions that may be addressed using the approach. A theoretical framework that is fundamental to ensuring that students understand the uses and limitations of the models is then described. Additionally, it details the main steps in building and testing a distribution model, and describes three case studies that illustrate applications of the models. This module is targeted at a level suitable for teaching graduate students and conservation professionals.
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Kenchington, E., L. Beazley, C. Lirette, F. J. Murillo, and J. Guijarro. Delineation of significant benthic areas in eastern Canada using kernel density analysis and species distribution models. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305872.

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Riordan, Erin C., Arlee M. Montalvo, and Jan L. Beyers. Using species distribution models with climate change scenarios to aid ecological restoration decisionmaking for southern California shrublands. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 2018. http://dx.doi.org/10.2737/psw-rp-270.

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Riordan, Erin C., Arlee M. Montalvo, and Jan L. Beyers. Using species distribution models with climate change scenarios to aid ecological restoration decisionmaking for southern California shrublands. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 2018. http://dx.doi.org/10.2737/psw-rp-270.

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Peters, Matthew P., Louis R. Iverson, Anantha M. Prasad, and Steve N. Matthews. Integrating fine-scale soil data into species distribution models: preparing Soil Survey Geographic (SSURGO) data from multiple counties. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2013. http://dx.doi.org/10.2737/nrs-gtr-122.

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Weldon, James, and Carlotta Meriggi. Modelling the risks of invasive aquatic species spread in Swedish lakes. Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.r68r25qcb1.

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Species distribution modelling is a valuable tool for identifying areas most at risk of the spread of invasive species. Here we model the environmental factors governing the distributions of two invasive species of concern that are currently found in Sweden at only a limited number of locations: the aquatic macrophyte Elodea nuttallii (Nuttall’s waterweed / smal vattenpest) and the bivalve Dreissena polymorpha (Zebra mussel / vandrarmussla). For E.nuttallii, the greatest risk factors are connectivity with other water bodies (facilitating dispersion), human population density and length of growing season. This implies that it is principally well-connected lakes in populated areas of southern Sweden that are most at risk of further spread (although other areas of concern are identified). For D.polymorpha, water alkalinity and the proportion of agricultural land (a source of nutrient pollution) are the most important factors, and the models identify lakes Vänern and Vättern, waters in parts of Östergötland, Jämtland and Gotland as key areas of concern for further spread.
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Guilfoyle, Michael, Ruth Beck, Bill Williams, Shannon Reinheimer, Lyle Burgoon, Samuel Jackson, Sherwin Beck, Burton Suedel, and Richard Fischer. Birds of the Craney Island Dredged Material Management Area, Portsmouth, Virginia, 2008-2020. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45604.

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This report presents the results of a long-term trend analyses of seasonal bird community data from a monitoring effort conducted on the Craney Island Dredged Material Management Area (CIDMMA) from 2008 to 2020, Portsmouth, VA. The USACE Richmond District collaborated with the College of William and Mary and the Coastal Virginia Wildlife Observatory, Waterbird Team, to conduct year-round semimonthly area counts of the CIDMMA to examine species presence and population changes overtime. This effort provides information on the importance of the area to numerous bird species and bird species’ groups and provides an index to those species and group showing significant changes in populations during the monitoring period. We identified those species regionally identified as Highest, High, and Moderate Priority Species based on their status as rare, sensitive, or in need of conservation attention as identified by the Atlantic Coast Joint Venture (ACJV), Bird Conservation Region (BCR), New England/Mid-Atlantic Bird Conservation Area (BCR 30). Of 134 ranked priority species in the region, the CIDMMA supported 102 of 134 (76%) recognized in the BCR, including 16 of 19 (84%) of Highest priority ranked species, 47 of 60 (78.3%) of High priority species, and 39 of 55 (71%) of Moderate priority species for BCR 30. All bird count and species richness data collected were fitted to a negative binomial (mean abundance) or Poisson distribution (mean species richness) and a total of 271 species and over 1.5 million birds were detected during the monitoring period. Most all bird species and species groups showed stable or increasing trends during the monitoring period. These results indicate that the CIDMMA is an important site that supports numerous avian species of local and regional conservation concern throughout the year.
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Méndez-Vizcaíno, Juan C., Alexander Guarín, César Anzola-Bravo, and Anderson Grajales-Olarte. Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia. Banco de la República, October 2021. http://dx.doi.org/10.32468/be.1178.

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Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities on the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium relationships and their macroeconomic consistency. We also illustrate the technical criteria used to represent the prospective factors of risk through the probability distributions of shocks.
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Brandt, Leslie A., Cait Rottler, Wendy S. Gordon, Stacey L. Clark, Lisa O'Donnell, April Rose, Annamarie Rutledge, and Emily King. Vulnerability of Austin’s urban forest and natural areas: A report from the Urban Forestry Climate Change Response Framework. U.S. Department of Agriculture, Northern Forests Climate Hub, October 2020. http://dx.doi.org/10.32747/2020.7204069.ch.

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The trees, developed green spaces, and natural areas within the City of Austin’s 400,882 acres will face direct and indirect impacts from a changing climate over the 21st century. This assessment evaluates the vulnerability of urban trees and natural and developed landscapes within the City Austin to a range of future climates. We synthesized and summarized information on the contemporary landscape, provided information on past climate trends, and illustrated a range of projected future climates. We used this information to inform models of habitat suitability for trees native to the area. Projected shifts in plant hardiness and heat zones were used to understand how less common native species, nonnative species, and cultivars may tolerate future conditions. We also assessed the adaptability of planted and naturally occurring trees to stressors that may not be accounted for in habitat suitability models such as drought, flooding, wind damage, and air pollution. The summary of the contemporary landscape identifies major stressors currently threatening trees and forests in Austin. Major current threats to the region’s urban forest include invasive species, pests and disease, and development. Austin has been warming at a rate of about 0.4°F per decade since measurements began in 1938 and temperature is expected to increase by 5 to 10°F by the end of this century compared to the most recent 30-year average. Both increases in heavy rain events and severe droughts are projected for the future, and the overall balance of precipitation and temperature may shift Austin’s climate to be more similar to the arid Southwest. Species distribution modeling of native trees suggests that suitable habitat may decrease for 14 primarily northern species, and increase for four more southern species. An analysis of tree species vulnerability that combines model projections, shifts in hardiness and heat zones, and adaptive capacity showed that only 3% of the trees estimated to be present in Austin based on the most recent Urban FIA estimate were considered to have low vulnerability in developed areas. Using a panel of local experts, we also assessed the vulnerability of developed and natural areas. All areas were rated as having moderate to moderate-high vulnerability, but the underlying factors driving that vulnerability differed by natural community and between East and West Austin. These projected changes in climate and their associated impacts and vulnerabilities will have important implications for urban forest management, including the planting and maintenance of street and park trees, management of natural areas, and long-term planning.
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10

bell, Matthew, Marcel P. Huijser, and David Kack. Exploring Apex Predator Effects on Wildlife-Vehicle Collisions: A Case Study on Wolf Reintroductions in Yellowstone. Western Transportation Institute, September 2024. http://dx.doi.org/10.15788/1727735675.

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This study investigates the impact of wolf reintroduction on wildlife-vehicle collisions (WVCs) along a segment of US-191 bordering Yellowstone National Park. Wolves were reintroduced in 1995–1996, and subsequent wolf pack establishment may have influenced the behavior and population dynamics of prey species, potentially altering WVC patterns. Using carcass data collected from 1989 to 2021, the analysis was divided into two primary phases: before wolves (1989–1996) and after wolves (1997–2021). A series of linear mixed-effects models were developed to assess changes in WVCs across these time periods. Predictor variables included average annual daily traffic (AADT), elk population estimates, and wolf counts. Results showed that WVCs significantly declined in the post-wolf period, suggesting that the presence of wolves may reduce WVCs directly by modifying prey behavior and movement patterns, or indirectly by reducing prey population densities. Further analysis revealed that while elk populations were a significant predictor of WVCs before wolves were reintroduced, this relationship weakened post-reintroduction. Traffic volume did not significantly influence WVC patterns in either period, nor did it interact significantly with wolf presence. The inclusion of wolf counts as a continuous variable showed a negative relationship with WVCs, indicating that higher wolf densities may contribute to a further reduction in collisions over time. These findings suggest that apex predators can play a role in mitigating human-wildlife conflicts, such as WVCs, by influencing prey species’ behavior and distribution. The study provides valuable insights for wildlife managers and transportation planners, highlighting the potential benefits of predator conservation for road safety and ecosystem health.
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