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Готові списки джерел за темами / Joint species distribution models / Статті в журналах

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Статті в журналах з теми "Joint species distribution models"

Автор: Grafiati

Опубліковано: 30 листопада 2024

Оновлено: 26 липня 2025

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1

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 (2019): 101–13. http://dx.doi.org/10.1111/jbi.13608.

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2

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 (2018): 198–211. http://dx.doi.org/10.1111/2041-210x.13106.

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3

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 (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 an

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4

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 (2015): 428–36. http://dx.doi.org/10.1111/2041-210x.12502.

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5

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 (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

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6

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 (2021): 1458–74. http://dx.doi.org/10.1111/2041-210x.13614.

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7

König, Christian, Rafael O. Wüest, Catherine H. Graham, et al. "Scale dependency of joint species distribution models challenges interpretation of biotic interactions." Journal of Biogeography 48, no. 7 (2021): 1541–51. http://dx.doi.org/10.1111/jbi.14106.

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8

Gavin, Daniel G., Matthew C. Fitzpatrick, Paul F. Gugger, et al. "Climate refugia: joint inference from fossil records, species distribution models and phylogeography." New Phytologist 204, no. 1 (2014): 37–54. http://dx.doi.org/10.1111/nph.12929.

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9

Wagner, Tyler, Gretchen J. A. Hansen, Erin M. Schliep, et al. "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 (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 abou

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10

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 (2017): 443–52. http://dx.doi.org/10.1111/2041-210x.12723.

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11

Briscoe Runquist, Ryan D., Thomas A. Lake, and David A. Moeller. "Improving predictions of range expansion for invasive species using joint species distribution models and surrogate co‐occurring species." Journal of Biogeography 48, no. 7 (2021): 1693–705. http://dx.doi.org/10.1111/jbi.14105.

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12

Thorson, James T., James N. Ianelli, Elise A. Larsen, et al. "Joint dynamic species distribution models: a tool for community ordination and spatio-temporal monitoring." Global Ecology and Biogeography 25, no. 9 (2016): 1144–58. http://dx.doi.org/10.1111/geb.12464.

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13

Deneu, Benjamin, Maximilien Servajean, Pierre Bonnet, Christophe Botella, François Munoz, and Alexis Joly. "Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment." PLOS Computational Biology 17, no. 4 (2021): e1008856. http://dx.doi.org/10.1371/journal.pcbi.1008856.

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Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (

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14

CARDADOR, LAURA, JOSÉ A. DÍAZ-LUQUE, FERNANDO HIRALDO, JAMES D. GILARDI, and JOSÉ L. TELLA. "The effects of spatial survey bias and habitat suitability on predicting the distribution of threatened species living in remote areas." Bird Conservation International 28, no. 4 (2017): 581–92. http://dx.doi.org/10.1017/s0959270917000144.

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SummaryKnowledge of a species’ potential distribution and the suitability of available habitat are fundamental for effective conservation planning and management. However, the quality of information on the distribution of species and their required habitats is highly variable in terms of accuracy and availability across taxa and regions, particularly in tropical landscapes where accessibility is especially challenging. Species distribution models (SDMs) provide predictive tools for addressing gaps for poorly surveyed species, but they rarely consider biases in geographical distribution of reco

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15

Rahman, Anis Ur, Gleb Tikhonov, Jari Oksanen, Tuomas Rossi, and Otso Ovaskainen. "Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting." PLOS Computational Biology 20, no. 9 (2024): e1011914. http://dx.doi.org/10.1371/journal.pcbi.1011914.

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Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this

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16

Zhang, Chongliang, Yong Chen, Binduo Xu, Ying Xue, and Yiping Ren. "Evaluating the influence of spatially varying catchability on multispecies distribution modelling." ICES Journal of Marine Science 77, no. 5 (2020): 1841–53. http://dx.doi.org/10.1093/icesjms/fsaa068.

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Abstract Varying catchability is a common feature in fisheries and has great impacts on fisheries assessments and species distribution models. However, spatial variations in catchability have been rarely evaluated, especially in the multispecies context. We advocate that the need for multispecies models stands for both challenges and opportunities to handle spatial catchability. This study evaluated the influence of spatially varying catchability on the performance of a novel joint species distribution model, namely Hierarchical Modelling of Species Communities (HMSC). We implemented the model

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17

Caradima, Bogdan, Nele Schuwirth, and Peter Reichert. "From individual to joint species distribution models: A comparison of model complexity and predictive performance." Journal of Biogeography 46, no. 10 (2019): 2260–74. http://dx.doi.org/10.1111/jbi.13668.

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18

Clark, James S., Alan E. Gelfand, Christopher W. Woodall, and Kai Zhu. "More than the sum of the parts: forest climate response from joint species distribution models." Ecological Applications 24, no. 5 (2014): 990–99. http://dx.doi.org/10.1890/13-1015.1.

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19

Zhang, Chongliang, Yong Chen, Binduo Xu, Ying Xue, and Yiping Ren. "Comparing the prediction of joint species distribution models with respect to characteristics of sampling data." Ecography 41, no. 11 (2018): 1876–87. http://dx.doi.org/10.1111/ecog.03571.

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20

Banville, Francis, Dominique Gravel, and Timothée Poisot. "What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases." PLOS Computational Biology 19, no. 9 (2023): e1011458. http://dx.doi.org/10.1371/journal.pcbi.1011458.

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Food webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the use of the principle of maximum entropy (MaxEnt) in network ecology is still rare. This is surprising considering that MaxEnt is a statistical tool precisely designed for understanding and predicting many types of constrained systems. This principle asserts that the least-biased probability distribution of a system’s property, constrained by prior k

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21

Doser, Jeffrey W., Andrew O. Finley, Marc Kéry, and Elise F. Zipkin. "spOccupancy: An R package for single-species, multi-species, and integrated spatial occupancy models." Methods in Ecology and Evolution 13, no. 8 (2022): 1670–78. https://doi.org/10.5281/zenodo.15190428.

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<strong>Abstract</strong> Occupancy modelling is a common approach to assess species distribution patterns, while explicitly accounting for false absences in detection–nondetection data. Numerous extensions of the basic single-species occupancy model exist to model multiple species, spatial autocorrelation and to integrate multiple data types. However, development of specialized and computationally efficient software to incorporate such extensions, especially for large datasets, is scarce or absent. We introduce the spOccupancy R package designed to fit single-species and multi-species s

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22

Viljanen, Markus, Lisa Tostrams, Niels Schoffelen, et al. "A joint model for the estimation of species distributions and environmental characteristics from point-referenced data." PLOS ONE 19, no. 6 (2024): e0304942. http://dx.doi.org/10.1371/journal.pone.0304942.

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Background Predicting and explaining species occurrence using environmental characteristics is essential for nature conservation and management. Species distribution models consider species occurrence as the dependent variable and environmental conditions as the independent variables. Suitable conditions are estimated based on a sample of species observations, where one assumes that the underlying environmental conditions are known. This is not always the case, as environmental variables at broad spatial scales are regularly extrapolated from point-referenced data. However, treating the predic

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23

Neves, Tomé, Luís Borda-de-Água, Maria da Luz Mathias, and Joaquim T. Tapisso. "The Influence of the Interaction between Climate and Competition on the Distributional Limits of European Shrews." Animals 12, no. 1 (2021): 57. http://dx.doi.org/10.3390/ani12010057.

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It is known that species’ distributions are influenced by several ecological factors. Nonetheless, the geographical scale upon which the influence of these factors is perceived is largely undefined. We assessed the importance of competition in regulating the distributional limits of species at large geographical scales. We focus on species with similar diets, the European Soricidae shrews, and how interspecific competition changes along climatic gradients. We used presence data for the seven most widespread terrestrial species of Soricidae in Europe, gathered from GBIF, European museums, and c

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24

Khan, Taimur, Ahmed El-Gabbas, Marina Golivets, et al. "Prototype Biodiversity Digital Twin: Invasive Alien Species." Research Ideas and Outcomes 10 (June 17, 2024): e124579. https://doi.org/10.3897/rio.10.e124579.

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Invasive alien species (IAS) threaten biodiversity and human well-being. These threats may increase in the future, necessitating accurate projections of potential locations and the extent of invasions. The main aim of the IAS prototype Digital Twin (IAS pDT) is to dynamically project the level of plant invasion at habitat level across Europe under current and future climates using joint species distribution models. The pDT detects updates in data sources and versions of the datasets and model outputs, implementing the FAIR principles. The pDT's outputs will be available via an interactive dash

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25

Zurell, Damaris, Laura J. Pollock, and Wilfried Thuiller. "Do joint species distribution models reliably detect interspecific interactions from co-occurrence data in homogenous environments?" Ecography 41, no. 11 (2018): 1812–19. http://dx.doi.org/10.1111/ecog.03315.

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26

Bokhutlo, Thethela, Eduardo R. Cunha, and Kirk O. Winemiller. "Inference of Fish Community Assembly in Intermittent Rivers Using Joint Species Distribution Models and Trophic Guilds." Open Journal of Ecology 13, no. 07 (2023): 497–515. http://dx.doi.org/10.4236/oje.2023.137030.

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27

Thorson, James T., and Lewis A. K. Barnett. "Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat." ICES Journal of Marine Science 74, no. 5 (2017): 1311–21. http://dx.doi.org/10.1093/icesjms/fsw193.

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Several approaches have been developed over the last decade to simultaneously estimate distribution or density for multiple species (e.g. “joint species distribution” or “multispecies occupancy” models). However, there has been little research comparing estimates of abundance trends or distribution shifts from these multispecies models with similar single-species estimates. We seek to determine whether a model including correlations among species (and particularly species that may affect habitat quality, termed “biogenic habitat”) improves predictive performance or decreases standard errors fo

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28

Guillaumet, Alban, and Roger Prodon. "Avian succession along ecological gradients: Insight from species-poor and species-rich communities of Sylvia warblers." Current Zoology 57, no. 3 (2011): 307–17. http://dx.doi.org/10.1093/czoolo/57.3.307.

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Abstract The mechanisms responsible for species replacement during ecological successions is a long-standing and open debate. In this study, we examined the distribution of the Sardinian warbler Sylvia melanocephala along two grassland-to-forest gradients, one in a high-diversity area (Albera-Aspres chain in Catalonia: eight Sylvia warbler species) and one in a low-diversity area (Mount Hymittos in Greece: four species). In Catalonia, distribution models suggested that the apparent exclusion of S. melanocephala from the open and forest ends of the gradient may be explained entirely by the pref

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29

Chen, Kai, Kevin S. Burgess, Fangliang He, Xiang-Yun Yang, Lian-Ming Gao, and De-Zhu Li. "Seed traits and phylogeny explain plants' geographic distribution." Biogeosciences 19, no. 19 (2022): 4801–10. http://dx.doi.org/10.5194/bg-19-4801-2022.

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Abstract. Understanding the mechanisms that shape the geographic distribution of plant species is a central theme of biogeography. Although seed mass, seed dispersal mode and phylogeny have long been suspected to affect species distribution, the link between the sources of variation in these attributes and their effects on the distribution of seed plants are poorly documented. This study aims to quantify the joint effects of key seed traits and phylogeny on species distribution. We collected the seed mass and seed dispersal mode from 1426 species of seed plants representing 501 genera of 122 f

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30

Strebel, Nicolas, Marc Kéry, Jérôme Guélat, and Thomas Sattler. "Spatiotemporal modelling of abundance from multiple data sources in an integrated spatial distribution model." Journal of Biogeography 49, no. 3 (2022): 563–75. https://doi.org/10.5281/zenodo.6510904.

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<strong>Abstract</strong> <strong>Aim: </strong>In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous datasets are often deemed to be incompatible. Consequently, many available datasets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends. <strong>Location:

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31

James Omaiye, Ojonubah. "Numerical Analysis of Ordinary Differential Equations of Ecological Competing Species Across Diverse Environments." African Journal of Mathematics and Statistics Studies 6, no. 1 (2023): 88–102. http://dx.doi.org/10.52589/ajmss_evssxtr7.

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In a geographical region, species have their range margins (i.e., the geographic boundaries where species can be found). Several species distribution models have shown that environmental factors (i.e., abiotic factors) and species interactions (i.e., biotic interactions) are responsible for shaping the distributions of species. Yet, most of the models often focus on one of these factors and ignore their joint effects. Consequently, predicting which species will exist and at what range margins is a challenge in ecology. Thus, in this paper, the combined influences of these ecological factors on

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32

Pandey, Bikram, Nirdesh Nepal, Salina Tripathi, et al. "Distribution Pattern of Gymnosperms’ Richness in Nepal: Effect of Environmental Constrains along Elevational Gradients." Plants 9, no. 5 (2020): 625. http://dx.doi.org/10.3390/plants9050625.

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Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental va

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33

Afsar, Bekir, Kyle Eyvindson, Tuomas Rossi, Martijn Versluijs, and Otso Ovaskainen. "Prototype Biodiversity Digital Twin: Forest Biodiversity Dynamics." Research Ideas and Outcomes 10 (June 17, 2024): e125086. https://doi.org/10.3897/rio.10.e125086.

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Forests are crucial in supporting biodiversity and providing ecosystem services. Understanding forest biodiversity dynamics under different management strategies and climate change scenarios is essential for effective conservation and management. This paper introduces the Forest Biodiversity Dynamics Prototype Digital Twin (pDT), integrating forest and biodiversity models to predict the effects of management options on forest ecosystems. The primary objective is to identify optimal management strategies that promote biodiversity, focusing on conservation and adaptation to different climate con

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34

Shitikov, V. K., T. D. Zinchenko, and L. V. Golovatyuk. "Models of Joint Distribution of Species on the Example of Benthic Communities from Small Rivers of the Volga Basin." Biology Bulletin Reviews 12, no. 1 (2022): 84–93. http://dx.doi.org/10.1134/s2079086422010078.

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35

Adebiyi, Adeyemi A., Jasper F. Kok, Yang Wang, et al. "Dust Constraints from joint Observational-Modelling-experiMental analysis (DustCOMM): comparison with measurements and model simulations." Atmospheric Chemistry and Physics 20, no. 2 (2020): 829–63. http://dx.doi.org/10.5194/acp-20-829-2020.

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Abstract. Mineral dust is the most abundant aerosol species by mass in the atmosphere, and it impacts global climate, biogeochemistry, and human health. Understanding these varied impacts on the Earth system requires accurate knowledge of dust abundance, size, and optical properties, and how they vary in space and time. However, current global models show substantial biases against measurements of these dust properties. For instance, recent studies suggest that atmospheric dust is substantially coarser and more aspherical than accounted for in models, leading to persistent biases in modelled i

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36

Jensen, Alexander J., Ryan P. Kelly, William H. Satterthwaite, Eric J. Ward, Paul Moran, and Andrew Olaf Shelton. "Modeling ocean distributions and abundances of natural- and hatchery-origin Chinook salmon stocks with integrated genetic and tagging data." PeerJ 11 (November 28, 2023): e16487. http://dx.doi.org/10.7717/peerj.16487.

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Background Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific

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37

Smith, James A., and Daniel D. Johnson. "Evaluating drivers and predictability of catch composition in a highly mixed trawl fishery using stacked and joint species distribution models." Fisheries Research 279 (November 2024): 107151. http://dx.doi.org/10.1016/j.fishres.2024.107151.

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38

Hur, Chan, and Hyeyoung Park. "Zero-Shot Image Classification with Rectified Embedding Vectors Using a Caption Generator." Applied Sciences 13, no. 12 (2023): 7071. http://dx.doi.org/10.3390/app13127071.

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Although image recognition technologies are developing rapidly with deep learning, conventional recognition models trained by supervised learning with class labels do not work well when test inputs from untrained classes are given. For example, a recognizer trained to classify Asian bird species cannot recognize the species of kiwi, because the class label “kiwi” and its image samples have not been seen during training. To overcome this limitation, zero-shot classification has been studied recently, and the joint-embedding-based approach has been suggested as one of the promised solutions. In

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39

Stolf, F., and D. B. Dunson. "Infinite joint species distribution models." Biometrika, July 22, 2025. https://doi.org/10.1093/biomet/asaf055.

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Summary Joint species distribution models are popular in ecology for modelling covariate effects on species occurrence, while characterizing cross-species dependence. Data consist of multivariate binary indicators of the occurrences of different species in each sample, along with sample-specific covariates. A key problem is that current models implicitly assume that the list of species under consideration is predefined and finite, while for highly diverse groups of organisms, it is impossible to anticipate which species will be observed in a study, and discovery of unknown species is common. T

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40

Van Ee, Justin J., Jacob S. Ivan, and Mevin B. Hooten. "Community confounding in joint species distribution models." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-15694-6.

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AbstractJoint species distribution models have become ubiquitous for studying species-environment relationships and dependence among species. Accounting for community structure often improves predictive power, but can also affect inference on species-environment relationships. Specifically, some parameterizations of joint species distribution models allow interspecies dependence and environmental effects to explain the same sources of variability in species distributions, a phenomenon we call community confounding. We present a method for measuring community confounding and show how to orthogo

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41

Hui, Francis K. C., Quan Vu, and Mevin B. Hooten. "Spatial confounding in joint species distribution models." Methods in Ecology and Evolution, September 6, 2024. http://dx.doi.org/10.1111/2041-210x.14420.

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Анотація:

Abstract Joint species distribution models (JSDMs) are a popular method for analysing multivariate abundance data, with important applications such as uncovering how species communities are driven by environmental processes, model‐based ordination to visualise community composition patterns across sites and variance partitioning to quantify the relative contributions of different processes in shaping a species community. One issue that has received relatively little attention in the study of joint species distributions is that of spatial confounding: when one or more of the environmental predi

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Tobler, Mathias W., Marc Kéry, Francis K. C. Hui, Gurutzeta Guillera‐Arroita, Peter Knaus, and Thomas Sattler. "Joint species distribution models with species correlations and imperfect detection." Ecology 100, no. 8 (2019). http://dx.doi.org/10.1002/ecy.2754.

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Bystrova, Daria, Giovanni Poggiato, Billur Bektaş, et al. "Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models." Frontiers in Ecology and Evolution 9 (March 9, 2021). http://dx.doi.org/10.3389/fevo.2021.601384.

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Анотація:

Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. (2017) proposed to use a Dirichlet process, a Bayesian nonparame

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Kettunen, Juho, Lauri Mehtätalo, Eeva‐Stiina Tuittila, Aino Korrensalo, and Jarno Vanhatalo. "Joint species distribution modeling with competition for space." Environmetrics, October 18, 2023. http://dx.doi.org/10.1002/env.2830.

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AbstractJoint species distribution models (JSDM) are among the most important statistical tools in community ecology. However, existing JSDMs cannot model mutual exclusion between species. We tackle this deficiency in the context of modeling plant percentage cover data, where mutual exclusion arises from limited growing space and competition for light. We propose a hierarchical JSDM where latent Gaussian variable models describe species' niche preferences and Dirichlet‐Multinomial distribution models the observation process and competition between species. We also propose a decision theoretic

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Roberts, Sarah M., Patrick N. Halpin, and James S. Clark. "Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-021-04110-0.

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AbstractSingle species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model

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Wilkinson, David P., Nick Golding, Gurutzeta Guillera‐Arroita, Reid Tingley, and Michael A. McCarthy. "Defining and evaluating predictions of joint species distribution models." Methods in Ecology and Evolution, November 8, 2020. http://dx.doi.org/10.1111/2041-210x.13518.

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North, Joshua S., Erin M. Schliep, Gretchen J. A. Hansen, et al. "Accounting for spatiotemporal sampling variation in joint species distribution models." Journal of Applied Ecology, November 28, 2023. http://dx.doi.org/10.1111/1365-2664.14547.

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Abstract Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accoun

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Vallé, Clément, Giovanni Poggiato, Wilfried Thuiller, Frédéric Jiguet, Karine Princé, and Isabelle Le Viol. "Species associations in joint species distribution models: from missing variables to conditional predictions." Journal of Biogeography, November 4, 2023. http://dx.doi.org/10.1111/jbi.14752.

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AbstractAimThe abundance and distribution of multiple species are interconnected through various mechanisms (e.g. biotic interactions or common responses to the environment) shaping communities. Joint species distribution models (jSDM) have been introduced as a potential tool to integrate these mechanisms when modelling multiple species distributions, by inferring a residual matrix of species associations that could inform on biotic interactions. However, the direct link between these residual associations and biotic interactions has been challenged. Here, we test how the data type, resolution

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Wang, Zihui, Sarah Piché‐Choquette, Jocelyn Lauzon, Sarah Ishak, and Steven W. Kembel. "Modelling the distribution of plant‐associated microbes with species distribution models." Journal of Ecology, April 8, 2025. https://doi.org/10.1111/1365-2745.70035.

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Abstract Plants interact with diverse microorganisms that play a crucial role in plant growth and development. The diversity and distribution of plant microbiota are altered by anthropogenic environmental change, leading to subsequent impacts on ecosystems. Modeling the distribution of plant‐associated microbes is critical for predicting and managing future changes in microbial function, but challenges and open questions when developing these models still remain. We present a conceptual framework for process‐oriented predictive modeling of the distribution of plant‐associated microbiota. We fi

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Mowry, Stacy, Sean Moore, Nicole L. Achee, Benedicte Fustec, and T. Alex Perkins. "Improving distribution models of sparsely documented disease vectors by incorporating information on related species via joint modeling." Ecography, May 3, 2024. http://dx.doi.org/10.1111/ecog.07253.

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Анотація:

A necessary component of understanding vector‐borne disease risk is accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data‐rich species but may produce inaccurate results for sparsely documented vectors. In light of global change, vectors that are currently not well‐documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely documented vector based on the assumption that the

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