Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation

[1]  S. Ciuti,et al.  Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance , 2022, bioRxiv.

[2]  M. Kéry,et al.  Spatiotemporal modelling of abundance from multiple data sources in an integrated spatial distribution model , 2022, Journal of Biogeography.

[3]  R. O’Hara,et al.  Integrated modeling of waterfowl distribution in western Canada using aerial survey and citizen science (eBird) data , 2021, Ecosphere.

[4]  A. Guisan,et al.  Data integration methods to account for spatial niche truncation effects in regional projections of species distribution. , 2021, Ecological applications : a publication of the Ecological Society of America.

[5]  John D. J. Clare,et al.  Integrating harvest and camera trap data in species distribution models , 2021 .

[6]  K. Holekamp,et al.  Integrating distance sampling and presence-only data to estimate species abundance. , 2020, Ecology.

[7]  T. Heskes,et al.  Disentangling drivers of spatial autocorrelation in species distribution models , 2020 .

[8]  Peter A. Henrys,et al.  Is more data always better? A simulation study of benefits and limitations of integrated distribution models , 2020, Ecography.

[9]  David L. Miller,et al.  Understanding the Stochastic Partial Differential Equation Approach to Smoothing , 2019, Journal of Agricultural, Biological and Environmental Statistics.

[10]  Philipp H. Boersch-Supan,et al.  Data Integration for Large-Scale Models of Species Distributions. , 2020, Trends in ecology & evolution.

[11]  Benjamin Zuckerberg,et al.  A practical guide for combining data to model species distributions. , 2019, Ecology.

[12]  Elise F Zipkin,et al.  Innovations in data integration for modeling populations. , 2019, Ecology.

[13]  Erlend B. Nilsen,et al.  Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld’s deer , 2019, Scientific Reports.

[14]  Hugh P. Possingham,et al.  Do Big Unstructured Biodiversity Data Mean More Knowledge? , 2019, Front. Ecol. Evol..

[15]  Janine B. Illian,et al.  Careful prior specification avoids incautious inference for log‐Gaussian Cox point processes , 2017, Journal of the Royal Statistical Society: Series C (Applied Statistics).

[16]  Carsten F. Dormann,et al.  Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure , 2017 .

[17]  David F. R. P. Burslem,et al.  Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology , 2017 .

[18]  Krishna Pacifici,et al.  Integrating multiple data sources in species distribution modeling: a framework for data fusion. , 2017, Ecology.

[19]  G. Guillera‐Arroita.  Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities , 2017 .

[20]  Bo Markussen,et al.  What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements , 2016 .

[21]  Alejandro Ruete,et al.  Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa , 2016, PloS one.

[22]  Timos Papadopoulos,et al.  Emerging technologies for biological recording , 2015 .

[23]  M. Cameletti,et al.  Spatial and Spatio-temporal Bayesian Models with R - INLA , 2015 .

[24]  Finn Lindgren,et al.  Bayesian Spatial Modelling with R-INLA , 2015 .

[25]  Kate E. Barlow,et al.  Citizen science reveals trends in bat populations: The National Bat Monitoring Programme in Great Britain , 2015 .

[26]  Robert M. Dorazio,et al.  Accounting for imperfect detection and survey bias in statistical analysis of presence‐only data , 2014 .

[27]  N. Burnside,et al.  A Spatial Analysis of Serotine Bat (Eptesicus serotinus) Roost Location and Landscape Structure: A Case Study in Sussex, UK , 2014 .

[28]  A. Budden,et al.  Big data and the future of ecology , 2013 .

[29]  Justin M. J. Travis,et al.  Fitting complex ecological point process models with integrated nested Laplace approximation , 2013 .

[30]  Haavard Rue,et al.  Going off grid: computationally efficient inference for log-Gaussian Cox processes , 2016 .

[31]  H. Rue,et al.  An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach , 2011 .

[32]  P. Dolman,et al.  Effects of landscape-scale broadleaved woodland configuration and extent on roost location for six bat species across the UK , 2011 .

[33]  J. Hodges,et al.  Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love , 2010 .

[34]  H. Rue,et al.  Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .

[35]  J. Møller,et al.  Log Gaussian Cox Processes , 1998 .

[36]  M. Robinson,et al.  Home range and habitat use by the serotine bat, Eptesicus serotinus, in England , 1997 .

[37]  P. Stephenson,et al.  Foraging behaviour and habitat use of the serotine bat (Eptesicus serotinus) in southern England , 1996 .

[38]  B. Erasmus,et al.  Geographic sampling bias in the South African Frog Atlas Project: implications for conservation planning , 2010, Biodiversity and Conservation.