Updated list of scientific articles using or citing hSDM is available on Google Scholar.

Articles using hSDM: Mertes, Jarzyna & Jetz (2020), Lambert et al. (2020), Lopes et al. (2019), Humphreys et al. (2019), Garriga et al. (2019), Domisch et al. (2019), Domisch et al. (2019), DellApa et al. (2018), Specht (2018), Humphreys (2018), Yesuf (2018), Pennino et al. (2017b), Costa, Pennino & Mendes (2017), Pennino et al. (2017a), Plumptre et al. (2016), Vilela et al. (2016), Wilson & Jetz (2016), Domisch, Wilson & Jetz (2016).

Articles/books citing hSDM: Croft & Smith (2019), Clark & Altwegg (2019), Botella (2019), Fletcher & Fortin (2018), MacKenzie et al. (2018), Martínez-Minaya et al. (2018), Rao, Pyne & Rao (2017), Lawson & Lee (2017), Herrando et al. (2017), Farley (2017), Guillera-Arroita (2016), Bird et al. (2014), Mazerolle (2015), Thuiller (2014), Lee (2013).


Bird, T.J., Bates, A.E., Lefcheck, J.S., Hill, N.A., Thomson, R.J., Edgar, G.J., Stuart-Smith, R.D., Wotherspoon, S., Krkosek, M., Stuart-Smith, J.F., Pecl, G.T., Barrett, N. & Frusher, S. (2014) Statistical solutions for error and bias in global citizen science datasets. Biological Conservation, 173, 144–154.
Botella, C. (2019) Statistical Methods for Spatial Plant Species Distribution Modeling Based on Large Masses of Uncertain Observations from Citizen-Science Programs. Theses, Université de Montpellier.
Clark, A.E. & Altwegg, R. (2019) Efficient bayesian analysis of occupancy models with logit link functions. Ecology and Evolution, 9, 756–768.
Costa, T.L.A., Pennino, M.G. & Mendes, L.F. (2017) Identifying ecological barriers in marine environment: The case study of dasyatis marianae. Marine Environmental Research, 125, 1–9.
Croft, S. & Smith, G.C. (2019) Structuring the unstructured: Estimating species-specific absence from multi-species presence data to inform pseudo-absence selection in species distribution models. bioRxiv.
DellApa, A., Pennino, M.G., Bangley, C.W. & Bonzek, C. (2018) A hierarchical bayesian modeling approach for the habitat distribution of smooth dogfish by sex and season in inshore coastal waters of the u.s. Northwest atlantic. Marine and Coastal Fisheries, 10, 590–605.
Domisch, S., Friedrichs, M., Hein, T., Borgwardt, F., Wetzig, A., Jähnig, S.C. & Langhans, S.D. (2019) Spatially explicit species distribution models: A missed opportunity in conservation planning? ed G. Iacona. Diversity and Distributions, 25, 758–769.
Domisch, S., Wilson, A.M. & Jetz, W. (2016) Model-based integration of observed and expert-based information for assessing the geographic and environmental distribution of freshwater species. Ecography, 39, 1078–1088.
Farley, S.S. (2017) A General Framework for Predicting the Optimal Computing Configurations for Climate-Driven Ecological Forecasting Models. PhD thesis, University of Wisconsin-Madison.
Fletcher, R. & Fortin, M.-J. (2018) Spatial Ecology and Conservation Modeling. Springer International Publishing.
Garriga, R.M., Marco, I., Casas-Díaz, E., Acevedo, P., Amarasekaran, B., Cuadrado, L. & Humle, T. (2019) Factors influencing wild chimpanzee (pan troglodytes verus) relative abundance in an agriculture-swamp matrix outside protected areas ed B.-S. Yue. PLOS ONE, 14, e0215545.
Guillera-Arroita, G. (2016) Modelling of species distributions, range dynamics and communities under imperfect detection: Advances, challenges and opportunities. Ecography, 40, 281–295.
Herrando, S., Keller, V., Voříšek, P., Kipson, M., Franch, M., Anton, M., Pla, M., Villero, D., Sierdsema, H., Kampichler, C. & others. (2017) High resolution maps for the second european breeding bird atlas: A first provision of standardised data and pilot modelled maps. Vogelwelt, 137, 33–41.
Humphreys, J.M. (2018) Traits, Species, and Communities: Integrative Bayesian Approaches to Ecological Biogeography Across Geographic, Environmental, Phylogenetic, and Morphological Space. PhD thesis, The Florida State University.
Humphreys, J.M., Elsner, J.B., Jagger, T.H. & Steppan, S.J. (2019) Integrated modeling of phylogenies, species traits, and environmental gradients to better predict biogeographic distributions. Peerj preprint.
Lambert, C., Authier, M., Dorémus, G., Laran, S., Panigada, S., Spitz, J., Canneyt, O.V. & Ridoux, V. (2020) Setting the scene for mediterranean litterscape management: The first basin-scale quantification and mapping of floating marine debris. Environmental Pollution, 114430.
Lawson, A. & Lee, D. (2017) Bayesian disease mapping for public health. Handbook of Statistics 36, Disease Modelling and Public Health, Part A, pp. 443–481. Elsevier.
Lee, D. (2013) CARBayes: An R Package for bayesian spatial modeling with conditional autoregressive priors. Journal of Statistical Software, 55.
Lopes, P.F.M., Verba, J.T., Begossi, A. & Pennino, M.G. (2019) Predicting species distribution from fishers’ local ecological knowledge: A new alternative for data-poor management. Canadian Journal of Fisheries and Aquatic Sciences, 76, 1423–1431.
MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L. & Hines, J.E. (2018) Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition. Elsevier.
Martínez-Minaya, J., Cameletti, M., Conesa, D. & Pennino, M.G. (2018) Species distribution modeling: A statistical review with focus in spatio-temporal issues. Stochastic Environmental Research and Risk Assessment, 32, 3227–3244.
Mazerolle, M.J. (2015) Estimating detectability and biological parameters of interest with the use of the R environment. Journal of Herpetology, 49, 541–559.
Mertes, K., Jarzyna, M.A. & Jetz, W. (2020) Hierarchical multi-grain models improve descriptions of species’ environmental associations, distribution, and abundance. Ecological Applications, 30, e02117.
Pennino, M.G., Mérigot, B., Fonseca, V.P., Monni, V. & Rotta, A. (2017a) Habitat modeling for cetacean management: Spatial distribution in the southern pelagos sanctuary (mediterranean sea). Deep Sea Research Part II: Topical Studies in Oceanography, 141, 203–211.
Pennino, M.G., Vilela, R., Valeiras, J. & Bellido, J.M. (2017b) Discard management: A spatial multi-criteria approach. Marine Policy, 77, 144–151.
Plumptre, A.J., Nixon, S., Kujirakwinja, D.K., Vieilledent, G., Critchlow, R., Williamson, E.A., Nishuli, R., Kirkby, A.E. & Hall, J.S. (2016) Catastrophic decline of worlds largest primate: 80% loss of grauers gorilla (gorilla beringei graueri) population justifies critically endangered status ed A. Margalida. PLOS ONE, 11, e0162697.
Rao, A.S.R.S., Pyne, S. & Rao, C.R. (2017) Disease Modelling and Public Health, Part a. Elsevier Science.
Specht, H. (2018) Occupancy survey data and analysis code for shorebird and waterfowl habitat use in NW north dakota, 2014-2015.
Thuiller, W. (2014) Editorial commentary on BIOMOD - optimizing predictions of species distributions and projecting potential future shifts under global change’. Global Change Biology, 20, 3591–3592.
Vilela, R., Pena, U., Esteban, R. & Koemans, R. (2016) Bayesian spatial modeling of cetacean sightings during a seismic acquisition survey. Marine Pollution Bulletin, 109, 512–520.
Wilson, A.M. & Jetz, W. (2016) Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions ed M. Loreau. PLOS Biology, 14, e1002415.
Yesuf, G.U. (2018) Modelling Future Range Shift Gaps in a Biodiversity Hotspot: A Case Study of Critically Endangered Plants in Madagascar. PhD thesis, Kingston University.