Develop best practice recommendations for combining seabird study data collected from different platforms: study

This study developed best practice guidance to combine seabird survey data collected from different platforms based on a literature review, expert knowledge and a bespoke model development including sensitivity analysis. This can be used in environmental assessments for planning and licensing.


8 References

Aarts, G., J. Fieberg, and J. Matthiopoulos. 2012. Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods in Ecology and Evolution 3:177–187.

Aarts, G., M. MacKenzie, B. McConnell, M. Fedak, and J. Matthiopoulos. 2008. Estimating space-use and habitat preference from wildlife telemetry data. Ecography 31:140–160.

Amano, T., J. D. Lamming, and W. J. Sutherland. 2014. Spatial Gaps in Global Biodiversity Information and the Role of Citizen Science. BioScience 66:393–400.

Araújo, M. B., and M. New. 2007. Ensemble forecasting of species distributions. Trends in Ecology and Evolution 22:42–47.

Arthur, S. M., B. F. J. Manly, L. L. Mcdonald, W. Gerald, S. Ecology, N. Jan, S. M. Arthur, B. F. J. Manly, and G. W. Garner. 1996. Assessing Habitat Selection when Availability Changes. Ecology 77:215–227.

Asseburg, C., J. Harwood, J. Matthiopoulos, and S. Smout. 2009. The functional response of generalist predators and its implications for the monitoring of marine ecosystems. Top Predators in Marine Ecosystems:262–274.

Augustin, N. H., M. A. Mugglestone, and S. T. Buckland. 1996. An autologistic model for the spatial distribution of wildlife. Journal of Applied Ecology 33:339–347.

Bachl, F. E., F. Lindgren, D. L. Borchers, and J. B. Illian. 2019. inlabru: an R package for Bayesian spatial modelling from ecological survey data. Methods in Ecology and Evolution 10:760–766.

Bahn, V., and B. J. Mcgill. 2007. Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography 16:733–742.

Barry, S., and J. Elith. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology 43:413–423.

Beale, C. M., M. J. Brewer, and J. J. Lennon. 2014. A new statistical framework for the quantification of covariate associations with species distributions. Methods in Ecology and Evolution 5:421–432.

Beale, C. M., J. J. Lennon, J. M. Yearsley, M. J. Brewer, and D. A. Elston. 2010. Regression analysis of spatial data. Ecology Letters 13:246–264.

Beaumont, M. A. 2010. Approximate Bayesian Computation in Evolution and Ecology. Annual Review of Ecology, Evolution, and Systematics 41:379–406.

Bell, D. M., and D. R. Schlaepfer. 2016. On the dangers of model complexity without ecological justification in species distribution modeling. Ecological Modelling 330:50–59.

Bird, T. J., A. E. Bates, J. S. Lefcheck, N. A. Hill, R. J. Thomson, G. J. Edgar, R. D. Stuart-Smith, S. Wotherspoon, M. Krkosek, J. F. Stuart-Smith, G. T. Pecl, N. Barrett, and S. Frusher. 2014. Statistical solutions for error and bias in global citizen science datasets. Biological Conservation 173:144–154.

Bolker, B. M. 2008. Ecological models and data in R. Princeton University Press.

Bonney, R., C. B. Cooper, J. Dickinson, S. Kelling, T. Phillips, K. V. Rosenberg, and J. Shirk. 2009. Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy. BioScience 59:977–984.

Boyce, M. S., and L. L. McDonald. 1999. Relating populations to habitats using resource selection functions. Trends in Ecology & Evolution 14:268–272.

Boyce, M. S., L. L. McDonald, and B. F. J. Manly. 1999. Relating populations to habitats-Reply. Trends In Ecology & Evolution 14:490.

Bradbury, G., M. L. Burt, and R. Hexter. 2011. Digital aerial surveillance of inshore waterbirds in Liverpool Bay Special Protection Area.

Bradbury, G., M. Trinder, B. Furness, A. N. Banks, R. W. G. Caldow, and D. Hume. 2014. Mapping Seabird Sensitivity to offshore wind farms. PLoS ONE 9.

Buckland, S. T., D. R. Anderson, K. . Burnham, J. L. Laake, D. Borchers, and L. Thomas. 2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. Oxford University Press.

Buckland, S. T., D. R. Anderson, K. P. Burnham, and J. L. Laake. 2008. Advanced distance sampling.

Buckland, S. T., M. L. Burt, E. A. Rexstad, M. Mellor, A. E. Williams, and R. Woodward. 2012. Aerial surveys of seabirds: The advent of digital methods. Journal of Applied Ecology 49:960–967.

Buckland, S. T., J. L. Laake, and D. L. Borchers. 2010. Double-observer line transect methods: Levels of independence. Biometrics 66:169–177.

Buckland, S. T., K. B. Newman, C. Fernández, L. Thomas, and J. Harwood. 2007. Embedding Population Dynamics Models in Inference. Statistical Science 22:44–58.

Buckland, S. T., K. B. Newman, L. Thomas, and N. B. Koesters. 2004. State-space models for the dynamics of wild animal populations. Ecological Modelling 171:157–175.

Burnham, K. P., and D. R. Anderson. 2004. Multimodel inference understanding {AIC and BIC} in model selection. Sociological methods & research 33:261–304.

Burnham, K. P., D. R. Anderson, and K. P. Huyvaert. 2011. {AIC} model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65:23–35.

Burt, M. L., E. A. Rexstad, and S. T. Buckland. 2009. Comparison of visual and digital aerial survey results of avian abundance for Round 3, Norfolk Region.

Calabrese, J. M., G. Certain, C. Kraan, and C. F. Dormann. 2014. Stacking species distribution models and adjusting bias by linking them to macroecological models. Global Ecology and Biogeography 23:99–112.

Camphuysen, C. J., B. Calvo, J. Durinck, K. Ensor, A. Follestad, R. W. Furness, S. Garthe, G. Leaper, H. Skov, M. L. Tasker, and C. J. N. Winter. 1995. Consumption of discards by seabirds in the North Sea.

Camphuysen, K. C. J., T. A. D. Fox, M. M. F. Leopold, and I. K. Petersen. 2004. Towards standardised seabirds at sea census techniques in connection with environmental impact assessments for offshore wind farms in the U.K. Cowrie - Bam - 02-2002:1–38.

Carroll, M. J., E. D. Wakefield, E. S. Scragg, E. Owen, S. Pinder, M. Bolton, J. J. Waggitt, and P. G. H. Evans. 2019. Matches and mismatches between seabird distributions estimated from at-sea surveys and concurrent individual-level tracking. Frontiers in Ecology and Evolution 7.

Certain, G., and V. Bretagnolle. 2008. Monitoring seabirds population in marine ecosystem: The use of strip-transect aerial surveys. Remote Sensing of Environment 112:3314–3322.

Chakraborty, A., A. E. Gelfand, A. M. Wilson, A. M. Latimer, J. A. Silander, S. Journal, R. Statistical, S. Series, C. A. Statistics, and A. Chakraborty. 2011. Point pattern modelling for degraded presence-only data over large regions. Journal of the Royal Statistical Society. Series C: Applied Statistics 60:757–776.

Chambert, T., E. H. C. Grant, D. A. W. Miller, J. D. Nichols, K. P. Mulder, and A. B. Brand. 2018. Two-species occupancy modelling accounting for species misidentification and non-detection. Methods in Ecology and Evolution 9:1468–1477.

Chase, S. K., and A. Levine. 2016. A framework for evaluating and designing citizen science programs for natural resources monitoring. Conservation Biology 30:456–466.

Clark, T. J., J. Matthiopoulos, A. S. Bonnet-Lebrun, L. Campioni, P. Catry, I. Marengo, S. Poncet, and E. Wakefield. 2019. Integrating habitat and partial survey data to estimate the regional population of a globally declining seabird species, the sooty shearwater. Global Ecology and Conservation 17:1–15.

Clarke, E. D., L. B. Spear, M. L. McCracken, F. F. C. Marques, D. L. Borchers, S. T. Buckland, and D. G. Ainley. 2003. Validating the use of generalized additive models and at-sea surveys to estimate size and temporal trends of seabird populations. Journal of Applied Ecology 40:278–292.

Csilléry, K., M. G. B. Blum, O. E. Gaggiotti, and O. François. 2010. Approximate Bayesian Computation (ABC) in practice. Trends in Ecology and Evolution 25:410–418.

D'Amen, M., A. Dubuis, R. F. Fernandes, J. Pottier, L. Pellissier, and A. Guisan. 2015. Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models. Journal of Biogeography 42:1255–1266.

Distler, T., J. G. Schuetz, J. Velásquez-Tibatá, and G. M. Langham. 2015. Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change. Journal of Biogeography 42:976–988.

Dormann, C. F., J. M. Calabrese, G. Guillera-Arroita, E. Matechou, V. Bahn, K. Bartoń, C. M. Beale, S. Ciuti, J. Elith, K. Gerstner, J. Guelat, P. Keil, J. J. Lahoz-Monfort, L. J. Pollock, B. Reineking, D. R. Roberts, B. Schröder, W. Thuiller, D. I. Warton, B. A. Wintle, S. N. Wood, R. O. Wüest, and F. Hartig. 2018. Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs 88:485–504.

Dormann, C. F., J. M. McPherson, B. Araújo, Miguel, R. Bivand, J. Bolliger, G. Carl, R. G. Davies, A. Hirzel, W. Jetz, W. Daniel Kissling, I. Kühn, R. Ohlemüller, P. P. Peres-Neto, B. Reineking, B. Schröder, F. M. Schurr, and R. Wilson. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628.

Ehrlén, J., and W. F. Morris. 2015a. Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters 18:303–314.

Ehrlén, J., and W. F. Morris. 2015b. Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters 18:303–314.

Elith, J., S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17:43–57.

Fieberg, J., J. Matthiopoulos, M. Hebblewhite, M. S. M. S. Boyce, and J. L. J. L. Frair. 2010. Correlation and studies of habitat selection: problem, red herring or opportunity? Philosophical Transactions of the Royal Society of London B: Biological Sciences 365:2233–2244.

Fieberg, J. R. J. R., J. D. J. D. Forester, G. M. G. M. Street, D. H. D. H. Johnson, A. A. A. ArchMiller, and J. Matthiopoulos. 2018. Used-habitat calibration plots: a new procedure for validating species distribution, resource selection, and step-selection models. Ecography 41:737–752.

Fithian, W., and T. Hastie. 2013. Finite-sample equivalence in statistical models for presence-only data. Annals of Applied Statistics 7:1917–1939.

Fletcher, R. J., T. J. Hefley, E. P. Robertson, B. Zuckerberg, R. A. McCleery, and R. M. Dorazio. 2019. A practical guide for combining data to model species distributions. Ecology 0:e02710.

Fletcher, R. J., R. A. McCleery, D. U. Greene, and C. A. Tye. 2016. Integrated models that unite local and regional data reveal larger-scale environmental relationships and improve predictions of species distributions. Landscape Ecology 31:1369–1382.

Frair, J. L., J. Fieberg, M. Hebblewhite, F. Cagnacci, N. J. DeCesare, and L. Pedrotti. 2010. Resolving issues of imprecise and habitat-biased locations in ecological analyses using {GPS} telemetry data. Philosophical Transactions of the Royal Society of London B: Biological Sciences 365:2187–2200.

Furnas, B. J., D. S. Newton, G. D. Capehart, and C. W. Barrows. 2019. Hierarchical distance sampling to estimate population sizes of common lizards across a desert ecoregion. Ecology and Evolution 9:3046–3058.

Furness, R. W. 2016. Third party review of Fehmarnbelt fixed link plausibility check report and suitability of baseline plus check data on marine birds and mammals.

Giraud, C., C. Calenge, C. Coron, and R. Julliard. 2016. Capitalizing on opportunistic data for monitoring relative abundances of species. Biometrics 72:649–658.

Grecian, W. J. J., M. J. J. Witt, M. J. J. Attrill, S. Bearhop, P. H. H. Becker, C. Egevang, R. W. W. Furness, B. J. J. Godley, J. González-Solís, D. Grémillet, M. Kopp, A. Lescroël, J. Matthiopoulos, S. C. C. Patrick, H.-U. Peter, R. A. A. Phillips, I. J. J. Stenhouse, and S. C. C. Votier. 2016. Seabird diversity hotspot linked to ocean productivity in the Canary Current Large Marine Ecosystem. Biology Letters 12.

Grecian, W. J., M. J. Witt, M. J. Attrill, S. Bearhop, B. J. Godley, D. Grémillet, K. C. Hamer, and S. C. Votier. 2012. A novel projection technique to identify important at-sea areas for seabird conservation: An example using Northern gannets breeding in the North East Atlantic. Biological Conservation 156:43–52.

Guillera-Arroita, G., and J. J. Lahoz-Monfort. 2012. Designing studies to detect differences in species occupancy: Power analysis under imperfect detection. Methods in Ecology and Evolution 3:860–869.

Guisan, A., and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8:993–1009.

Guisan, A., W. Thuiller, and N. E. Zimmermann. 2017. Habitat suitability and distribution models: with applications in R. Cambridge University Press.

Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135:147–186.

Hedley, S. L., and S. T. Buckland. 2004. Spatial models for line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics 9:181–199.

Hefley, T. J., and M. B. Hooten. 2016. Hierarchical Species Distribution Models. Current Landscape Ecology Reports 1:87–97.

Hochachka, W. M., D. Fink, R. A. Hutchinson, D. Sheldon, W. K. Wong, and S. Kelling. 2012. Data-intensive science applied to broad-scale citizen science. Trends in Ecology and Evolution 27:130–137.

Hodges, J. S., and B. J. Reich. 2010. Adding spatially-correlated errors can mess up the fixed effect you love. American Statistician 64:325–334.

Holbrook, J. D., L. E. Olson, N. J. DeCesare, M. Hebblewhite, J. R. Squires, and R. Steenweg. 2019. Functional responses in habitat selection: clarifying hypotheses and interpretations. Ecological Applications 29:e01852.

Holling, C. S. 1959. Some characteristics of simple types of predation and parasitism. The Canadian Entomologist 91:385–398.

Hothorn, T., J. Müller, B. Schröder, T. Kneib, and R. Brandl. 2011. Decomposing environmental, spatial, and spatiotemporal components of species distributions. Ecological Monographs 81:329–347.

Isojunno, S., J. Matthiopoulos, and P. G. H. H. P. G. H. Evans. 2012. Harbour porpoise habitat preferences: Robust spatio-temporal inferences from opportunistic data. Marine Ecology Progress Series 448:155–170.

Jones, E. L. L., B. J. J. McConnell, S. Smout, P. S. S. Hammond, C. D. D. Duck, C. D. D. Morris, D. Thompson, D. J. F. J. F. Russel, C. Vincent, M. Cronin, R. J. J. Sharples, and J. Matthiopoulos. 2015. Patterns of space use in sympatric marine colonial predators reveal scales of spatial partitioning. Marine Ecology Progress Series 534.

Kearney, M., and W. Porter. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. Ecology letters 12:334–350.

Keil, P., J. Belmaker, A. M. Wilson, P. Unitt, and W. Jetz. 2013. Downscaling of species distribution models: A hierarchical approach. Methods in Ecology and Evolution 4:82–94.

Keil, P., A. M. Wilson, and W. Jetz. 2014. Uncertainty, priors, autocorrelation and disparate data in downscaling of species distributions. Diversity and Distributions 20:797–812.

Kent, M., R. A. Moyeed, C. L. Reid, R. Pakeman, and R. Weaver. 2006. Geostatistics, spatial rate of change analysis and boundary detection in plant ecology and biogeography. Progress in Physical Geography 30:201–231.

Kindsvater, H. K. H. K., N. K. N. K. Dulvy, C. Horswill, M.-J. M. J. M.-J. Juan-Jordá, M. Mangel, and J. Matthiopoulos. 2018. Overcoming the Data Crisis in Biodiversity Conservation. Trends in Ecology and Evolution 33:676–688.

de Knegt, H. J., F. van Langevelde, M. B. Coughenour, A. K. Skidmore, W. F. de Boer, I. . M. A. Heitkönig, N. M. Knox, R. Slotow, C. van der Waal, and H. H. T. Prins. 2010. Spatial autocorrelation and the scaling of species — environment relationships. Ecology 91:2455–2465.

Koshkina, V., Y. Wang, A. Gordon, R. M. Dorazio, M. White, and L. Stone. 2017. Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection. Methods in Ecology and Evolution 8:420–430.

Kosmala, M., A. Wiggins, A. Swanson, and B. Simmons. 2016. Assessing data quality in citizen science. Frontiers in Ecology and the Environment 14:551–560.

Lahoz-Monfort, J. J., G. Guillera-Arroita, and B. A. Wintle. 2014. Imperfect detection impacts the performance of species distribution models. Global Ecology and Biogeography 23:504–515.

Lele, S. R., M. Moreno, and E. Bayne. 2012. Dealing with detection error in site occupancy surveys: What can we do with a single survey? Journal of Plant Ecology 5:22–31.

Levin, S. A. 1992. The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture. Ecology 73:1943–1967.

Lewis, S., T. N. Sherratt, K. C. Hamer, and S. Wanless. 2001. Evidence of intra-specific ccompetition for food in a pelagic seabird. Nature 412:816–819.

Lieske, D. J., D. A. Fifield, and C. Gjerdrum. 2014. Maps, models, and marine vulnerability: Assessing the community distribution of seabirds at-sea. Biological Conservation 172:15–28.

Lindgren, F. 2015. Journal of Statistical Software Bayesian Spatial Modelling with R-INLA. Journal Of Statistical Software 63:1–25.

Louzao, M., J. Bécares, B. Rodríguez, K. D. Hyrenbach, A. Ruiz, and J. M. Arcos. 2009. Combining vessel-based surveys and tracking data to identify key marine areas for seabirds. Marine Ecology Progress Series 391:183–197.

Manly, B. F. J. 2003. Estimating a resource selection function with line transect sampling. Pages 213–228 Journal of Applied Mathematics and Decision Sciences.

Marques, F. F. C., and S. T. Buckland. 2003. Incorporating Covariates into Standard Line Transect Analyses. Integration The Vlsi Journal:924–935.

Martino, S., and N. Chopin. 2007. Approximate Bayesian Inference for Latent Gaussian Models Using Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations. Journal of the Royal Statistical Society Series B:1–28.

Matthiopoulos, J. 2003a. The use of space by animals as a function of accessibility and preference. Ecological Modelling 159:239–268.

Matthiopoulos, J. 2003b. Model-supervised kernel smoothing for the estimation of spatial usage. Oikos 102:367–377.

Matthiopoulos, J., and G. Aarts. 2007. The Spatial analysis of marine mammal abundance. Pages 27–33 in I. L. Boyd, W. D. Bowen, and S. J. Iverson, editors. Marine Mammal Ecology And Conservation: A Handbook of Techniques (Oxford Biology) (Techniques in Ecology & Conservation). Oxford University Press.

Matthiopoulos, J., L. Cordes, B. Mackey, D. Thompson, C. Duck, S. Smout, M. Caillat, and P. Thompson. 2014. State-space modelling reveals proximate causes of harbour seal population declines. Oecologia 174:151–162.

Matthiopoulos, J., J. Fieberg, G. Aarts, H. L. H. L. Beyer, J. M. J. M. J. M. Morales, and D. T. D. T. D. T. Haydon. 2015. Establishing the link between habitat selection and animal population dynamics. Ecological Monographs 85:413–436.

Matthiopoulos, J., C. Field, and R. MacLeod. 2019. Predicting population change from models based on habitat availability and utilization. Proceedings of the Royal Society B: Biological Sciences 286.

Matthiopoulos, J., M. Hebblewhite, G. Aarts, and J. Fieberg. 2011. Generalized functional responses for species distributions. Ecology 92:583–589.

Matthiopoulos, J., B. Mcconnell, C. Duck, and M. Fedak. 2004. Using satellite telemetry and aerial counts to estimate space use by grey seals around the British Isles. Journal of Applied Ecology 41:476–491.

McLoughlin, P. D., D. W. Morris, D. Fortin, E. Vander Wal, and A. L. Contasti. 2010. Considering ecological dynamics in resource selection functions. Journal of Animal Ecology 79:4–12.

Mcloughlin, P. D., D. W. Morris, D. Fortin, E. Vander Wal, A. L. Contasti, P. D. Mcloughlin, D. W. Morris, D. Fortin, E. Vander Wal, and A. L. Contasti. 2018. Considering ecological dynamics in resource selection functions. Journal of Animal Ecology 79:4–12.

Mendel, B., P. Schwemmer, V. Peschko, S. Müller, H. Schwemmer, M. Mercker, and S. Garthe. 2019. Operational offshore wind farms and associated ship traffic cause profound changes in distribution patterns of Loons (Gavia spp.). Journal of Environmental Management 231:429–438.

Merow, C., M. J. Smith, and J. A. Silander. 2013. A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069.

Michelot, T., P. G. Blackwell, S. Chamaillé-Jammes, and J. Matthiopoulos. 2019a. Inference in MCMC step selection models. Biometrics 1:1–10.

Michelot, T., P. G. Blackwell, and J. Matthiopoulos. 2019b. Linking resource selection and step selection models for habitat preferences in animals. Ecology 100:1–12.

Michelot, T., P. Gloaguen, P. G. Blackwell, and M. P. Étienne. 2019c. The Langevin diffusion as a continuous-time model of animal movement and habitat selection. Methods in Ecology and Evolution 10:1894–1907.

Miller, D. A., J. D. Nicholas, M. B. T., E. H. Campbell Grant, and L. L. Bailey. 2011. Improving occupancy estimation when two types of observational error occur: non-detection and species misidentificatio. Ecology 92:1422–1428.

Miller, D. A. W., K. Pacifici, J. S. Sanderlin, and B. J. Reich. 2019. The recent past and promising future for data integration methods to estimate species' distributions. Methods in Ecology and Evolution 10:22–37.

Monestiez, P., L. Dubroca, E. Bonnin, J. Durbec, and C. Guinet. 2006. Geostatistical modelling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observation efforts. Ecological Modelling 193:615–628.

Moorcroft, P. R. 2012. Mechanistic approaches to understanding and predicting mammalian space use: recent advances, future directions. Journal of Mammalogy 93:903–916.

Moorcroft, P. R., A. Barnett, P. R. Moorcroft, and A. Barnett. 2008. Mechanistic Home Range Models and Resource Selection Analysis : A Reconciliation and Unification 89:1112–1119.

Moorcroft, P. R., M. A. Lewis, and R. L. Crabtree. 1999. Home Range Analysis Using a Mechanistic Home Range Model 80:1656–1665.

Moorcroft, P. R., M. A. Lewis, and R. L. Crabtree. 2006. Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone. Proceedings of the Royal Society B: Biological Sciences 273:1651–1659.

Van Moorter, B., D. Visscher, S. Benhamou, L. Börger, M. S. Boyce, and J. M. Gaillard. 2009. Memory keeps you at home: A mechanistic model for home range emergence. Oikos 118:641–652.

Morales, J. M. M., P. R. R. Moorcroft, J. Matthiopoulos, J. L. L. Frair, J. G. G. Kie, R. A. A. Powell, E. H. H. Merrill, and D. T. T. Haydon. 2010. Building the bridge between animal movement and population dynamics. Philosophical Transactions of the Royal Society B Biological Sciences 365:2289–2301.

Morris, L. R., K. M. Proffitt, and J. K. Blackburn. 2016. Mapping resource selection functions in wildlife studies: Concerns and recommendations. Applied Geography 76:173–183.

Munson, M. A., R. Caruana, D. Fink, W. M. Hochachka, M. Iliff, K. V. Rosenberg, D. Sheldon, B. L. Sullivan, C. Wood, and S. Kelling. 2010. A method for measuring the relative information content of data from different monitoring protocols. Methods in Ecology and Evolution:no-no.

Mysterud, A., and R. A. Ims. 1998. Functional responses in habitat use: availability influences relative use in trade-off situations. Ecology 79:1435–1441.

National Archives. 2022. Open Government Licence https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ Accessed 30/03/2022

NaturalEngland. 2019. Digital video aerial surveys of red-throated diver in the Outer Thames Estuary Special Protection Area 2018.

Nelli, L., H. M. Ferguson, and J. Matthiopoulos. 2019. Achieving explanatory depth and spatial breadth in infectious disease modelling: Integrating active and passive case surveillance. Statistical Methods in Medical Research:096228021985638.

Newman, K. B., S. T. Buckland, B. J. T. Morgan, R. King, D. L. Borchers, D. J. Cole, P. Besbeas, O. Gimenez, and L. Thomas. 2014. Modelling Population Dynamics: Model Formulation, Fitting and Assessment Using State-Space Methods. Springer.

Ngoprasert, D., G. A. Gale, and A. J. Tyre. 2019. Abundance estimation from multiple data types for group-living animals: An example using dhole (Cuon alpinus). Global Ecology and Conservation 20:e00792.

Nur, N., J. Jahncke, M. P. Herzog, J. Howar, K. D. Hyrenbach, J. E. Zamon, D. G. Ainley, J. A. Wiens, K. Morgan, L. T. Ballance, N. Nur, J. Jahncke, M. P. Herzog, J. Howar, K. D. Hyrenbach, J. E. Zamon, D. G. Ainley, J. A. Wiens, K. Morgan, L. T. Ballance, and D. Stralberg. 2011. Where the wild things are : predicting hotspots of seabird aggregations in the California Current System 21:2241–2257.

Oedekoven, C. S., M. L. Mackenzie, L. A. S. Scott-Hayward, and E. Rexstad. 2012a. Marine Scotland Science Report 05/14 Statistical Modelling of Seabird and Cetacean Data: Literature Review.

Oedekoven, C. S., M. L. Mackenzie, L. A. S. Scott-Hayward, and E. Rexstad. 2012b. Marine Scotland Science Report 04 / 14 Statistical Modelling of Seabird and Cetacean Data : Guidance Document.

Oppel, S., A. Meirinho, I. Ramírez, B. Gardner, A. F. O'Connell, P. I. Miller, and M. Louzao. 2012. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biological Conservation 156:94–104.

Ovaskainen, O., N. Abrego, P. Halme, and D. Dunson. 2016. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods in Ecology and Evolution 7:549–555.

Ovaskainen, O., J. Hottola, and J. Siitonen. 2010. Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. Ecology 91:2514–21.

Pacifici, K., B. J. Reich, D. A. W. Miller, B. Gardner, G. Stauffer, S. Singh, A. McKerrow, and J. A. Collazo. 2017. Integrating multiple data sources in species distribution modeling: A framework for data fusion. Ecology 98:840–850.

Pacifici, K., B. J. Reich, D. A. W. Miller, and B. S. Pease. 2019. Resolving misaligned spatial data with integrated species distribution models. Ecology 100:1–15.

Paton, R. S., and J. Matthiopoulos. 2018. Defining the scale of habitat availability for models of habitat selection. Ecology 97:1113–1122.

Patterson, T. A. T. A., L. Thomas, C. Wilcox, O. Ovaskainen, and J. Matthiopoulos. 2008. State-space models of individual animal movement. Trends in Ecology & Evolution 23:87–94.

Pearce, J. L., and M. S. Boyce. 2006. Modelling distribution and abundance with presence-only data. Journal of Applied Ecology 43:405–412.

Pearman, P. B., A. Guisan, O. Broennimann, and C. F. Randin. 2008. Niche dynamics in space and time. Trends in Ecology and Evolution 23:149–158.

Peel, S. L., N. A. Hill, S. D. Foster, S. J. Wotherspoon, C. Ghiglione, and S. Schiaparelli. 2019. Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types. Methods in Ecology and Evolution 10:1002–1014.

Perrow, M. R., A. J. P. Harwood, E. R. Skeate, E. Praca, and S. M. Eglington. 2015. Use of multiple data sources and analytical approaches to derive a marine protected area for a breeding seabird. Biological Conservation 191:729–738.

Petersen, I., and R. Nielsen. 2011. Abundance and distribution of selected waterbird species in Danish marine areas.

Peterson, A. T., J. Soberón, R. G. Pearson, R. P. Anderson, E. Martinez-Meyer, M. Nakamura, and M. B. Araújo. 2011. Ecological niches and geographic distributions. Princeton University Press.

R Core Team. 2019. {R}: A language and environment for statistical computing. {Vienna, Austria; 2014}. URL http://www. R-project. org.

Reich, B. J., K. Pacifici, and J. W. Stallings. 2018. Integrating auxiliary data in optimal spatial design for species distribution modelling. Methods in Ecology and Evolution 9:1626–1637.

Renner, I. W., J. Elith, A. Baddeley, W. Fithian, T. Hastie, S. J. Phillips, G. Popovic, and D. I. Warton. 2015. Point process models for presence-only analysis. Methods in Ecology and Evolution 6:366–379.

Renner, I. W., J. Louvrier, and O. Gimenez. 2019. Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalized likelihood maximization. Methods in Ecology and Evolution 2019:2118–2128.

Renner, I. W., and D. I. Warton. 2013. Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics 69:274–281.

Rexstad, E. A., and S. T. Buckland. 2012. SOSS-01. Displacement analysis boat surveys Kentish Flats.

Riotte-Lambert, L., and J. Matthiopoulos. 2019. Communal and efficient movement routines can develop spontaneously through public information use. Behavioral Ecology 30:408–416.

Robertson, M. P., N. Caithness, M. H. Villet, and N. J. Mar. 2001. A PCA-Based Modelling Technique for Predicting Environmental Suitability for Organisms from Presence Records A PCA-based modelling technique for predicting environmental suitability for organisms from presence records. Diversity 7:15–27.

Robinson, N. M., W. A. Nelson, M. J. Costello, J. E. Sutherland, and C. J. Lundquist. 2017. A systematic review of marine-based Species Distribution Models (SDMs) with recommendations for best practice. Frontiers in Marine Science 4:1–11.

Rue, H., S. Martino, and N. Chopin. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71:319–392.

Sansom, A., L. J. Wilson, R. W. G. Caldow, and M. Bolton. 2018. Comparing marine distribution maps for seabirds during the breeding season derived from different survey and analysis methods. PLoS ONE 13:1–17.

Sardà-Palomera, F., L. Brotons, D. Villero, H. Sierdsema, S. E. Newson, and F. Jiguet. 2012. Mapping from heterogeneous biodiversity monitoring data sources. Biodiversity and Conservation 21:2927–2948.

Segurado, P., M. B. Araújo, and W. E. Kunin. 2006. Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology 43:433–444.

Signer, J., J. Fieberg, and T. Avgar. 2017. Estimating utilization distributions from fitted step-selection functions. Ecosphere 8.

Sinclair, S. J., M. D. White, and G. R. Newell. 2010. How useful are species distribution models for managing biodiversity under future climates? Ecology and Society 15.

Smout, S., C. Asseburg, J. Matthiopoulos, C. Fernández, S. Redpath, S. Thirgood, and J. Harwood. 2010. The Functional Response of a Generalist Predator. PLoS ONE 5:7.

Sokal, U. R. R., and U. F. J. Rohlf. 1995. Biometry. W. H. Freeman.

La Sorte, F. A., C. A. Lepczyk, J. L. Burnett, A. H. Hurlbert, M. W. Tingley, and B. Zuckerberg. 2018. Opportunities and challenges for big data ornithology. The Condor 120:414–426.

Strindberg, S., and S. T. Buckland. 2004. Zigzag survey designs in line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics 9:443–461.

Talluto, M. V., I. Boulangeat, A. Ameztegui, I. Aubin, D. Berteaux, A. Butler, F. Doyon, C. R. Drever, M. J. Fortin, T. Franceschini, J. Liénard, D. Mckenney, K. A. Solarik, N. Strigul, W. Thuiller, and D. Gravel. 2016. Cross-scale integration of knowledge for predicting species ranges: A metamodelling framework. Global Ecology and Biogeography 25:238–249.

Tasker, M. L., P. H. Jones, T. Dixon, and B. F. Blake. 1984. Counting Seabirds at Sea from Ships: A Review of Methods Employed and a Suggestion for a Standardized Approach. The Auk 101:567–577.

Thaxter, C. B., B. Lascelles, K. Sugar, A. S. C. P. Cook, S. Roos, M. Bolton, R. H. W. Langston, and N. H. K. Burton. 2012. Seabird foraging ranges as a preliminary tool for identifying candidate Marine Protected Areas. Biological Conservation 156:53–61.

Thiers, L., M. Louzao, V. Ridoux, M. Le Corre, S. Jaquemet, and H. Weimerskirch. 2014. Combining methods to describe important marine habitats for top predators: Application to identify biological hotspots in tropical waters. PLoS ONE 9:1–23.

Thomas, L., S. T. Buckland, E. A. Rexstad, J. L. Laake, S. Strindberg, S. L. Hedley, J. R. B. Bishop, T. A. Marques, and K. P. Burnham. 2010. Distance software: Design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology 47:5–14.

Thorson, J. T., J. N. Ianelli, E. A. Larsen, L. Ries, M. D. Scheuerell, C. Szuwalski, and E. F. Zipkin. 2016. Joint dynamic species distribution models: a tool for community ordination and spatio-temporal monitoring. Global Ecology and Biogeography 25:1144–1158.

Tuanmu, M.-N., A. Viña, G. J. Roloff, W. Liu, Z. Ouyang, H. Zhang, and J. Liu. 2011. Temporal transferability of wildlife habitat models: implications for habitat monitoring. Journal of Biogeography 38:1510–1523.

Tulloch, V. J., H. P. Possingham, S. D. Jupiter, C. Roelfsema, A. I. T. Tulloch, and C. J. Klein. 2013. Incorporating uncertainty associated with habitat data in marine reserve design. Biological Conservation 162:41–51.

Turlure, C., N. Schtickzelle, Q. Dubois, M. Baguette, R. L. H. Dennis, and H. Van Dyck. 2019. Suitability and transferability of the resource-based habitat concept: A test with an assemblage of butterflies. Frontiers in Ecology and Evolution 7.

Tyre, A. J., B. Tenhumberg, S. A. Field, D. Niejalke, K. Parris, and H. P. Possingham. 2003. Improving precision and reducing bias in biological surveys: Estimating false-negative error rates. Ecological Applications 13:1790–1801.

Waggitt, J. J., P. G. H. Evans, J. Andrade, A. . Banks, O. Boisseau, M. Bolton, G. Bradbury, T. Brereton, C. J. Camphuysen, J. Durinck, T. Felce, R. C. Fijn, I. Garcia‐Baron, S. Garthe, S. C. . Geelhoed, A. Gilles, M. Goodall, J. Haelters, S. Hamilton, L. Hartny‐Mills, N. Hodgins, K. James, M. Jessopp, A. S. Kavanagh, M. Leopold, K. Lohrengel, M. Louzao, N. Markones, J. Martinez‐Cediera, O. O'Cadhla, S. L. Perry, G. J. Pierce, V. Ridoux, K. P. Robinson, M. B. Santos, C. Saavedra, H. Skov, E. W. M. Stienen, S. Sveegaard, P. Thompson, N. Vanermen, D. Wall, A. Webb, J. Wilson, S. Wanless, and J. G. Hiddink. 2019. Distribution maps of cetacean and seabird populations in the North‐East Atlantic. Journal of Applied Ecology 205:1365-2664.13525.

Wakefield, E. D., T. W. Bodey, S. Bearhop, J. Blackburn, K. Colhoun, R. Davies, R. G. Dwyer, J. A. Green, D. Grémillet, A. L. Jackson, M. J. Jessopp, A. Kane, R. H. W. Langston, A. Lescroël, S. Murray, M. Le Nuz, S. C. Patrick, C. Péron, L. M. Soanes, S. Wanless, S. C. Votier, and K. C. Hamer. 2013. Space partitioning without territoriality in gannets. Science 341:68–70.

Wakefield, E. D. E. D., R. A. R. A. Phillips, P. N. P. N. Trathan, J. Arata, R. Gales, N. Huin, R. Graham, S. M. S. M. Waugh, H. Weimerskirch, and J. Matthiopoulos. 2011. Habitat preference, accessibility, and competition limit the global distribution of breeding Black-browed Albatrosses. Ecological Monographs 81:141–167.

Wald, D. M., J. Longo, and A. R. Dobell. 2016. Design principles for engaging and retaining virtual citizen scientists. Conservation Biology 30:562–570.

Warton, D. I., L. C. Shepherd, and others. 2010. Poisson point process models solve the "pseudo-absence problem''' for presence-only data in ecology." The Annals of Applied Statistics 4:1383–1402.

Webb, A., and G. Nehls. 2019. Surveying Seabirds. Page 330 in M. R. Perrow, editor. Wildlife and windfarms, Conflicts and solutions.

Wood, S. N. 2006. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC.

Yamamoto, T., Y. Watanuki, E. L. Hazen, B. B. Nishizawa, H. Sasaki, and A. Takahashi. 2015. Statistical integration of tracking and vessel survey data to incorporate life history differences in habitat models. Ecological Applications 25:2394–2406.

Yates, K. L., P. J. Bouchet, M. J. Caley, K. Mengersen, C. F. Randin, S. Parnell, A. H. Fielding, A. J. Bamford, S. Ban, A. M. Barbosa, C. F. Dormann, J. Elith, C. B. Embling, G. N. Ervin, R. Fisher, S. Gould, R. F. Graf, E. J. Gregr, P. N. Halpin, R. K. Heikkinen, S. Heinänen, A. R. Jones, P. K. Krishnakumar, V. Lauria, H. Lozano-Montes, L. Mannocci, C. Mellin, M. B. Mesgaran, E. Moreno-Amat, S. Mormede, E. Novaczek, S. Oppel, G. Ortuño Crespo, A. T. Peterson, G. Rapacciuolo, J. J. Roberts, R. E. Ross, K. L. Scales, D. Schoeman, P. Snelgrove, G. Sundblad, W. Thuiller, L. G. Torres, H. Verbruggen, L. Wang, S. Wenger, M. J. Whittingham, Y. Zharikov, D. Zurell, and A. M. M. Sequeira. 2018. Outstanding Challenges in the Transferability of Ecological Models. Trends in Ecology and Evolution 33:790–802.

Yen, J. D. L., Z. Tonkin, J. Lyon, W. Koster, A. Kitchingman, K. Stamation, and P. A. Vesk. 2019. Integrating multiple data types to connect ecological theory and data among levels. Frontiers in Ecology and Evolution 7:1–7.

Zeller, K. A., K. McGarigal, and A. R. Whiteley. 2012. Estimating landscape resistance to movement: A review. Landscape Ecology 27:777–797.

Zeller, K. A., T. W. Vickers, H. B. Ernest, and W. M. Boyce. 2017. Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study. PLoS ONE 12:1–20.

Zipkin, E. F., and S. P. Saunders. 2018. Synthesizing multiple data types for biological conservation using integrated population models. Biological Conservation 217:240–250.

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