References

Aarts, G., Fieberg, J., & Matthiopoulos, J. (2012). Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods in Ecology and Evolution, 3(1), 177–187. doi:10.1111/j.2041-210X.2011.00141.x

Aarts, G., MacKenzie, M., McConnell, B., Fedak, M., & Matthiopoulos, J. (2008). Estimating space-use and habitat preference from wildlife telemetry data. Ecography, 31(1), 140–160. doi:10.1111/j.2007.0906-7590.05236.x

Allee, W. (1951). Cooperation among Animals, with Human Implications. (p. 233). New York: Henry Schuman.

Allen, T. F. H., & Hoekstra, T. W. (1992). Toward a unified ecology (p. 384). New York: Columbia University Press.

Augustin, N. H., Trenkel, V. M., Wood, S. N., & Lorance, P. (2013). Space-time modelling of blue ling for fisheries stock management. Environmetrics, 24(2), 109–119. doi:10.1002/env.2196

Austin, M. P. (1999). A silent clash of paradigms: Some inconsistencies in community ecology. Oikos, 86(1), 170–178.

Baddeley, A., Berman, M., Fisher, N., Hardegen, A., Milne, R., Schuhmacher, D., et al.others. (2010). Spatial logistic regression and change-of-support in Poisson point processes. Electronic Journal of Statistics, 4, 1151–1201. doi:doi:10.1214/10-EJS581

Baddeley, A., & Turner, R. (2005). spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software, 12(6), 1–42. doi:10.18637/jss.v012.i06

Bahn, V., & Mcgill, B. J. (2007). Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16(6), 733–742. doi:10.1111/j.1466-8238.2007.00331.x

Bailleul, F., Charrassin, J.-B., Monestiez, P., Roquet, F., Biuw, M., & Guinet, C. (2007). Successful foraging zones of southern elephant seals from the Kerguelen Islands in relation to oceanographic conditions. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1487), 2169–2181. doi:10.1098/rstb.2007.2109

Barela, I., Burger, L. M., Taylor, J., Evans, K. O., Ogawa, R., McClintic, L., & Wang, G. (2020). Relationships between survival and habitat suitability of semi-aquatic mammals. Ecology and Evolution, 10(11), 4867–4875. doi:10.1002/ece3.6239

Barker, R. J., Schofield, M. R., Link, W. A., & Sauer, J. R. (2018). On the reliability of n-mixture models for count data. Biometrics, 74(1), 369–377. doi:10.1111/biom.12734

Barnett, L. A. K., Ward, E. J., & Anderson, S. C. (2021). Improving estimates of species distribution change by incorporating local trends. Ecography, 44(3), 427–439. doi:10.1111/ecog.05176

Bassar, R. D., Marshall, M. C., López-Sepulcre, A., Zandonà, E., Auer, S. K., Travis, J., … Reznick, D. N. (2010). Local adaptation in Trinidadian guppies alters ecosystem processes. Proceedings of the National Academy of Sciences of the United States of America, 107(8), 3616–3621. doi:10.1073/pnas.0908023107

Begon, M., Harper, J. L., & Townsend, C. R. (1996). Ecology: Individuals, Populations and Communities (3rd Edition) (p. 1068). Cambridge, Massachusetts, USA: Blackwell Science.

Bell, D. M., & Schlaepfer, D. R. (2016). On the dangers of model complexity without ecological justification in species distribution modeling. Ecological Modelling, 330, 50–59. doi:10.1016/j.ecolmodel.2016.03.012

Beyer, H. L., Gurarie, E., Börger, L., Panzacchi, M., Basille, M., Herfindal, I., … Matthiopoulos, J. (2016). ’You shall not pass!’: Quantifying barrier permeability and proximity avoidance by animals. Journal of Animal Ecology, 85(1), 43–53. doi:10.1111/1365-2656.12275

Beyer, H. L., Haydon, D. T., Morales, J. M., Frair, J. L., Hebblewhite, M., Mitchell, M., & Matthiopoulos, J. (2010). The interpretation of habitat preference metrics under use–availability designs. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365(1550), 2245–2254. doi:10.1098/rstb.2010.0083

Biernaskie, J. M., Walker, S. C., & Gegear, R. J. (2009). Bumblebees learn to forage like bayesians. The American Naturalist, 174(3), 413–423. doi:10.1086/603629

Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., … Frusher, S. (2014). Statistical solutions for error and bias in global citizen science datasets. Biological Conservation, 173, 144–154. doi:10.1016/j.biocon.2013.07.037

Blonder, B. (2018). Hypervolume concepts in niche- and trait-based ecology. Ecography, 41(9), 1441–1455. doi:10.1111/ecog.03187

Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009). Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy. BioScience, 59(11), 977–984. doi:10.1525/bio.2009.59.11.9

Borchers, D. L., & Marques, T. A. (2017). From distance sampling to spatial capture–recapture. AStA Advances in Statistical Analysis, 101(4), 475–494.

Börger, L., Matthiopoulos, J., Holdo, R. M., Morales, J. M., Couzin, I., & McCauley, E. (2013). Migration quantified: constructing models and linking them with data. Animal Migration, 110–128. doi:10.1093/acprof:oso/9780199568994.003.0008

Bowler, D. E., Nilsen, E. B., Bischof, R., O’Hara, R. B., Yu, T. T., Oo, T., … Linnell, J. D. (2019). Integrating data from different survey types for population monitoring of an endangered species: The case of the eld’s deer. Scientific Reports, 9(1), 7766.

Boyce, M. S., & McDonald, L. L. (1999). Relating populations to habitats using resource selection functions. Trends in Ecology & Evolution, 14(7), 268–272.

Bracis, C., & Mueller, T. (2017). Memory, not just perception, plays an important role in terrestrial mammalian migration. Proceedings of the Royal Society B: Biological Sciences, 284(1855), 20170449. doi:10.1098/rspb.2017.0449

Braczkowski, A. R., Balme, G. A., Dickman, A., Fattebert, J., Johnson, P., Dickerson, T., … Hunter, L. (2016). Scent lure effect on camera-trap based leopard density estimates. PLoS One, 11(4), e0151033.

Brasseur, S. M. J. M., van Polanen Petel, T. D., Gerrodette, T., Meesters, E. H. W. G., Reijnders, P. J. H., & Aarts, G. (2015). Rapid recovery of Dutch gray seal colonies fueled by immigration. Marine Mammal Science, 31(2), 405–426. doi:10.1111/mms.12160

Broennimann, O., Fitzpatrick, M. C., Pearman, P. B., Petitpierre, B., Pellissier, L., Yoccoz, N. G., … Guisan, A. (2012). Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography, 21(4), 481–497. doi:10.1111/j.1466-8238.2011.00698.x

Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., … Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400.

Brown, Joel S. (1988). Patch use as an indicator of habitat preference, predation risk, and competition. Behavioral Ecology and Sociobiology, 22(1), 37–47. doi:10.1007/BF00395696

Brown, Joel S., & Kotler, B. P. (2004). Hazardous duty pay and the foraging cost of predation. Ecology Letters, 7(10), 999–1014. doi:10.1111/j.1461-0248.2004.00661.x

Brown, J. S., Laundre, J. W., & Gurung, M. (1999). The Ecology of Fear: Optimal Foraging, Game Theory, and Trophic Interactions. Journal of Mammalogy, 80(2), 385–399. doi:10.2307/1383287

Buckland, S. T., Anderson, D. R., Burnham, K. P., & Laake, J. L. (2005). Distance sampling. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (Vol. 2). Chichester, UK: Wiley Online Library. doi:10.1002/0470011815.b2a16019

Calabrese, J. M., Certain, G., Kraan, C., & Dormann, C. F. (2014). Stacking species distribution models and adjusting bias by linking them to macroecological models. Global Ecology and Biogeography, 23(1), 99–112. doi:10.1111/geb.12102

Camphuysen, K. C. J., Shamoun-Baranes, J., Bouten, W., & Garthe, S. (2012). Identifying ecologically important marine areas for seabirds using behavioural information in combination with distribution patterns. Biological Conservation, 156, 22–29. doi:10.1016/j.biocon.2011.12.024

Casella, G., & Berger, R. L. (2002). Statistical inference (Vol. 2). Duxbury Pacific Grove, CA.

Caughley, G., & Sinclair, A. R. E. (1994). Wildlife Ecology and Management (p. 334). Wiley.

Chakraborty, A., Gelfand, A. E., Wilson, A. M., Latimer, A. M., Silander, J. A., Journal, S., … Chakraborty, A. (2011). Point pattern modelling for degraded presence-only data over large regions. Journal of the Royal Statistical Society. Series C: Applied Statistics, 60(5), 757–776. Retrieved from https://www.jstor.org/stable/41262305

Charnov, E. L. (1976). Optimal foraging, the marginal value theorem. Theoretical Population Biology, 9(2), 129–136. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1273796

Chase, J. M., & Leibold, M. A. (2003). Ecological Niches: Linking Classical and Contemporary Approaches (Vol. 13, p. 212). Springer Science+ Business Media BV, Formerly Kluwer Academic Publishers BV.

Ciuti, S., Northrup, J. M., Muhly, T. B., Simi, S., Musiani, M., Pitt, J. A., & Boyce, M. S. (2012). Effects of humans on behaviour of wildlife exceed those of natural predators in a landscape of fear. PLoS ONE, 7(11), e50611. doi:10.1371/journal.pone.0050611

Coron, C., Calenge, C., Giraud, C., & Julliard, R. (2018). Bayesian estimation of species relative abundances and habitat preferences using opportunistic data. Environmental and Ecological Statistics, 25(1), 71–93.

Craiu, R. V., Duchesne, T., & Fortin, D. (2008). Inference methods for the conditional logistic regression model with longitudinal data. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 50(1), 97–109.

Damos, P. (2016). Using multivariate cross correlations, Granger causality and graphical models to quantify spatiotemporal synchronization and causality between pest populations. BMC Ecology, 16(1), 1–17. doi:10.1186/s12898-016-0087-7

De Goeij, P., & Honkoop, P. J. C. (2002). The effect of immersion time on burying depth of the bivalve Macoma balthica (Tellinidae). Journal of Sea Research, 47(2), 109–119. doi:10.1016/S1385-1101(02)00095-3

DeCesare, N. J., Hebblewhite, M., Schmiegelow, F., Hervieux, D., McDermid, G. J., Neufeld, L., et al.others. (2012). Transcending scale dependence in identifying habitat with resource selection functions. Ecological Applications, 22(4), 1068–1083.

Dennis, R. L. H. (2012). What is a Habitat? An Awkward Question. In A resource-based habitat view for conservation (pp. 1–8). Chichester, West Sussex, UK: John Wiley & Sons, Ltd. doi:10.1002/9781444315257.ch1

Distler, T., Schuetz, J. G., Velásquez-Tibatá, J., & Langham, G. M. (2015). Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change. Journal of Biogeography, 42(5), 976–988. doi:10.1111/jbi.12479

Dorazio, R. M. (2012). Predicting the geographic distribution of a species from presence-only data subject to detection errors. Biometrics, 68(4), 1303–1312. doi:10.1111/j.1541-0420.2012.01779.x

Dorazio, R. M. (2014). Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Global Ecology and Biogeography, 23(12), 1472–1484. doi:10.1111/geb.12216

Dormann, Carsten. F., McPherson, Jana. M., Araújo, Bivand, R., Bolliger, J., Carl, G., … Wilson, R. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30(5), 609–628. doi:10.1111/j.2007.0906-7590.05171.x

Efford, M. G., & Dawson, D. K. (2012). Occupancy in continuous habitat. Ecosphere, 3(4), 1–15.

Elith, J., & Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677. doi:10.1111/j.1600-0587.2008.05505.x

Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57. doi:10.1111/j.1472-4642.2010.00725.x

Fagan, W. F., Lewis, M. A., Auger-Méthé, M., Avgar, T., Benhamou, S., Breed, G., … Mueller, T. (2013, October). Spatial memory and animal movement. John Wiley & Sons, Ltd. doi:10.1111/ele.12165

Fieberg, J. (2007). Kernel density estimators of home range: Smoothing and the autocorrelation red herring. Ecology, 88(4), 1059–1066.

Fieberg, J. (2012). Estimating population abundance using sightability models: R SightabilityModel package. Journal of Statistical Software, 51(9), 1–20.

Fieberg, J., Alexander, M., Tse, S., & Clair, K. S. (2013). Abundance estimation with sightability data: A Bayesian data augmentation approach. Methods in Ecology and Evolution, 4(9), 854–864.

Fieberg, J., & Börger, L. (2012). Could you please phrase ‘home range’ as a question? Journal of Mammalogy, 93(4), 890–902.

Fieberg, J., Matthiopoulos, J., Hebblewhite, M., Boyce, M. S., & Frair, J. L. (2010). Correlation and studies of habitat selection: Problem, red herring or opportunity? Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365(1550), 2233–2244. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2894958\&tool=pmcentrez\&rendertype=abstract

Fieberg, J., Shertzer, K. W., Conn, P. B., Noyce, K. V., & Garshelis, D. L. (2010). Integrated population modeling of black bears in Minnesota: implications for monitoring and management. Plos One, 5(8), e12114.

Fieberg, J., Vitense, K., & Johnson, D. H. (2020). Resampling-based methods for biologists. PeerJ, 8, e9089. doi:10.7717/peerj.9089

Fithian, W., Elith, J., Hastie, T., & Keith, D. A. (2015). Bias correction in species distribution models: Pooling survey and collection data for multiple species. Methods in Ecology and Evolution, 6(4), 424–438. doi:10.1111/2041-210X.12242

Fithian, W., & Hastie, T. (2013). Finite-sample equivalence in statistical models for presence-only data. The Annals of Applied Statistics, 7(4), 1917. doi:10.1214/13-AOAS667

Fleming, C. H., Calabrese, J. M., Mueller, T., Olson, K. A., Leimgruber, P., & Fagan, W. F. (2014). From fine-scale foraging to home ranges: A semivariance approach to identifying movement modes across spatiotemporal scales. The American Naturalist, 183(5), E154–E167.

Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., & Calabrese, J. M. (2015). Rigorous home range estimation with movement data: A new autocorrelated kernel density estimator. Ecology, 96(5), 1182–1188.

Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., & Calabrese, J. M. (2016). Estimating where and how animals travel: An optimal framework for path reconstruction from autocorrelated tracking data. Ecology, 97(3), 576–582.

Fletcher, R. J., Hefley, T. J., Robertson, E. P., Zuckerberg, B., McCleery, R. A., & Dorazio, R. M. (2019). A practical guide for combining data to model species distributions. Ecology, 100(6), e02710. doi:10.1002/ecy.2710

Fourcade, Y., Besnard, A. G., & Secondi, J. (2018). Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography, 27(2), 245–256. doi:10.1111/geb.12684

Fretwell, S. D., & Lucas, H. L. (1969). On territorial behavior and other factors influencing habitat distribution in birds - I. Theoretical development. Acta Biotheoretica, 19(1), 16–36. doi:10.1007/BF01601953

Gallien, L., Douzet, R., Pratte, S., Zimmermann, N. E., & Thuiller, W. (2012). Invasive species distribution models - how violating the equilibrium assumption can create new insights. Global Ecology and Biogeography, 21(11), 1126–1136. doi:10.1111/j.1466-8238.2012.00768.x

Garrote, G., Gil-Sánchez, J. M., McCain, E. B., Lillo, S. de, Tellería, J. L., & Simón, M. Á. (2012). The effect of attractant lures in camera trapping: A case study of population estimates for the iberian lynx (lynx pardinus). European Journal of Wildlife Research, 58(5), 881–884.

Gelfand, A. E. (2020). Statistical challenges in spatial analysis of plant ecology data. Spatial Statistics, 37, 100418. doi:10.1016/j.spasta.2020.100418

Gelfand, A. E., & Shirota, S. (2019). Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data. Ecological Monographs, 89(3), 1–17. doi:10.1002/ecm.1372

Giannini, T. C., Chapman, D. S., Saraiva, A. M., Alves-dos-Santos, I., & Biesmeijer, J. C. (2013). Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants. Ecography, 36(6), 649–656. doi:10.1111/j.1600-0587.2012.07191.x

Giraud, C., Calenge, C., Coron, C., & Julliard, R. (2016). Capitalizing on opportunistic data for monitoring relative abundances of species. Biometrics, 72(2), 649–658.

Grady, J. M., Maitner, B. S., Winter, A. S., Kaschner, K., Tittensor, D. P., Record, S., … Brown, J. H. (2019). Biodiversity patterns: Metabolic asymmetry and the global diversity of marine predators. Science, 363(6425). doi:10.1126/science.aat4220

Graham, C. H., Ferrier, S., Huettman, F., Moritz, C., & Peterson, A. T. (2004). New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution, 19(9), 497–503. doi:10.1016/j.tree.2004.07.006

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424–438.

Grecian, W. J., Witt, M. J., Attrill, M. J., Bearhop, S., Becker, P. H., Egevang, C., … Votier, S. C. (2016). Seabird diversity hotspot linked to ocean productivity in the canary current large marine ecosystem. Biology Letters, 12(8). doi:10.1098/rsbl.2016.0024

Grohmann, C., Hartmann, J. N., Kovalev, A., & Gorb, S. N. (2019). Dandelion diaspore dispersal: frictional anisotropy of cypselae of Taraxacum officinale enhances their interlocking with the soil. Plant and Soil, 440(1-2), 399–408. doi:10.1007/s11104-019-04086-x

Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8(9), 993–1009. doi:10.1111/j.1461-0248.2005.00792.x

Guisan, A., Thuiller, W., & Zimmermann, N. E. (2017). Habitat suitability and distribution models: With applications in r. Cambridge University Press.

Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2-3), 147–186. doi:10.1016/S0304-3800(00)00354-9

Hall, L. S., Krausman, P. R., & Morrison, M. L. (1997). The Habitat Concept and a Plea for Standard Terminology. Wildlife Society Bulletin, 25(1), 173–182.

Hanks, E. M., Hooten, M. B., Alldredge, M. W., et al. (2015). Continuous-time discrete-space models for animal movement. The Annals of Applied Statistics, 9(1), 145–165.

Hastie, T., & Tibshirani, R. (1993). Varying-Coefficient Models. Journal of the Royal Statistical Society, 55(4), 757–796.

Hedley, S. L., & Buckland, S. T. (2004). Spatial models for line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics, 9(2), 181–199. doi:10.1198/1085711043578

Hefley, T. J., & Hooten, M. B. (2016). Hierarchical Species Distribution Models. Current Landscape Ecology Reports, 1(2), 87–97. doi:10.1007/s40823-016-0008-7

Hijmans, R. J., Phillips, S., Leathwick, J., & Elith, J. (2017). Dismo: Species distribution modeling. Retrieved from https://CRAN.R-project.org/package=dismo

Hirzel, A. H., Hausser, J., Chessel, D., Perrin, N., & Jul, N. (2002). Ecological-niche factor analysis : How to compute habitat-suitability maps without absence data ? ECOLOGICAL-NICHE FACTOR ANALYSIS : HOW TO COMPUTE HABITAT-SUITABILITY MAPS WITHOUT ABSENCE DATA ? Society, 83(7), 2027–2036.

Hirzel, Alexandre H., & Le Lay, G. (2008). Habitat suitability modelling and niche theory. Journal of Applied Ecology, 45(5), 1372–1381. doi:10.1111/j.1365-2664.2008.01524.x

Hochachka, W. M., Fink, D., Hutchinson, R. A., Sheldon, D., Wong, W. K., & Kelling, S. (2012). Data-intensive science applied to broad-scale citizen science. Trends in Ecology and Evolution, 27(2), 130–137. doi:10.1016/j.tree.2011.11.006

Hodges, J. S., & Reich, B. J. (2010). Adding spatially-correlated errors can mess up the fixed effect you love. American Statistician, 64(4), 325–334. doi:10.1198/tast.2010.10052

Holbrook, J. D., Olson, L. E., DeCesare, N. J., Hebblewhite, M., Squires, J. R., & Steenweg, R. (2019). Functional responses in habitat selection: Clarifying hypotheses and interpretations. Ecological Applications, e01852. doi:10.1002/eap.1852

Holling, C. S. (1959). Some characteristics of simple types of predation and parasitism. The Canadian Entomologist, 91(7), 385–398.

Holt, R. D. (2009). Bringing the Hutchinsonian niche into the 21st century: Ecological and evolutionary perspectives. Proceedings of the National Academy of Sciences of the United States of America, 106(Supplement 2), 19659–19665. doi:10.1073/pnas.0905137106

Hooten, M. B., Hanks, E. M., Johnson, D. S., & Alldredge, M. W. (2013). Reconciling resource utilization and resource selection functions. Journal of Animal Ecology, 82(6), 1146–1154.

Hooten, M. B., Hanks, E. M., Johnson, D. S., & Alldredge, M. W. (2014). Temporal variation and scale in movement-based resource selection functions. Statistical Methodology, 17(C), 82–98. doi:10.1016/j.stamet.2012.12.001

Hooten, M. B., Johnson, D. S., Mcclintock, B. T., & Morales, J. M. (2017). Animal Movement: Statistical Models for Telemetry Data (p. 320). CRC Press.

Horne, J. S., Fieberg, J., Börger, L., Rachlow, J. L., Calabrese, J. M., & Fleming, C. H. (2020). Animal home ranges: Concepts, uses, and estimation. In Population Ecology in Practice (pp. 315–332). New York, NY: Wiley.

Horne, J. S., Garton, E. O., & Rachlow, J. L. (2008). A synoptic model of animal space use: Simultaneous estimation of home range, habitat selection, and inter/intra-specific relationships. Ecological Modelling, 214(2-4), 338–348.

Hubbell, S. P. (2001). The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Vol. 32). Princeton University Press.

Hutchinson, G. E. (1957). Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22(0), 75–96. doi:10.1201/9781315366746

Iannarilli, F., Erb, J., Arnold, T., & Fieberg, J. (2017). Evaluation of design and analysis of a camera-based multi-species occupancy survey of carnivores in Minnesota. In Summaries of wildlife research findings 2017 (pp. 176–195). Minnesota Department of Natural Resources (MNDNR).

Illian, J. B., Sørbye, S. H., & Rue, H. (2012). A toolbox for fitting complex spatial point process models using integrated nested laplace approximation (INLA). The Annals of Applied Statistics, 1499–1530.

Ims, R. A. (1990). On the Adaptive Value of Reproductive Synchrony as a Predator-Swamping Strategy. The American Naturalist, 136(4), 485–498. doi:10.2307/2462190

Isaac, N. J. B., Jarzyna, M. A., Keil, P., Dambly, L. I., Boersch-Supan, P. H., Browning, E., … O’Hara, R. B. (2020). Data Integration for Large-Scale Models of Species Distributions. Trends in Ecology and Evolution, 35(1), 56–67. doi:10.1016/j.tree.2019.08.006

Jansen, J., Woolley, S. N. C., Dunstar, P. K., Foster, S. D., Hill, N. A., Haward, M., & Johnson, C. R. (2022). Stop ignoring map uncertainty in biodiversity science and conservation policy. Nature Ecology & Evolution. doi:10.1038/s41559-022-01778-z

Jennrich, R., & Turner, F. (1969). Measurement of non-circular home range. Journal of Theoretical Biology, 22(2), 227–237.

Jesmer, B. R., Merkle, J. A., Goheen, J. R., Aikens, E. O., Beck, J. L., Courtemanch, A. B., … Kauffman, M. J. (2018). Is ungulate migration culturally transmitted? Evidence of social learning from translocated animals. Science, 361(6406), 1023–1025. doi:10.1126/science.aat0985

Johnson, C. J., Nielsen, S. E., Merrill, E. H., McDonald, T. L., & Boyce, M. S. (2006). Resource selection functions based on use-availability data: Theoretical motivation and evaluation methods. Journal of Wildlife Management, 70(2), 347–357.

Johnson, D. H. (1980). The comparison of usage and availability measurements for evaluating resource preference. Ecology, 61(1), 65–71.

Jonsen, I. D., Myers, R. A., & James, M. C. (2007). Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model. Marine Ecology Progress Series, 337(2004), 255–264.

Kamil, A. C., Misthal, R. L., & Stephens, D. W. (1993). Failure of simple optimal foraging models to predict residence time when patch quality is uncertain. Behavioral Ecology, 4(4), 350–363. doi:10.1093/beheco/4.4.350

Kearney, M., & Porter, W. (2009). Mechanistic niche modelling: Combining physiological and spatial data to predict species’ ranges. Ecology Letters, 12(4), 334–50. doi:10.1111/j.1461-0248.2008.01277.x

Keating, K. A., & Cherry, S. (2004). Use and interpretation of logistic regression in habitat-selection studies. Journal of Wildlife Management, 68(4), 774–789.

Kelling, S., Hochachka, W. M., Fink, D., Riedewald, M., Caruana, R., Ballard, G., & Hooker, G. (2009). Data-intensive Science: A New Paradigm for Biodiversity Studies. BioScience, 59(7), 613–620. doi:10.1525/bio.2009.59.7.12

Kery, M., & Royle, J. A. (2020). Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS: Volume 2: Dynamic and Advanced Models (p. 890). Academic Press.

Kéry, M., & Royle, J. A. (2015). Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in r and BUGS: Volume 1: Prelude and static models. Academic Press.

Kie, J. G., Matthiopoulos, J., Fieberg, J., Powell, R. A., Cagnacci, F., Mitchell, M. S., … Moorcroft, P. R. (2010). The home-range concept: Are traditional estimators still relevant with modern telemetry technology? Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365(1550), 2221–2231. doi:10.1098/rstb.2010.0093

Kirk, D. A., Park, A. C., Smith, A. C., Howes, B. J., Prouse, B. K., Kyssa, N. G., … Prior, K. A. (2018). Our use, misuse, and abandonment of a concept: Whither habitat? Ecology and Evolution, 8(8), 4197–4208. doi:10.1002/ece3.3812

Koshkina, V., Wang, Y., Gordon, A., Dorazio, R. M., White, M., & Stone, L. (2017). Integrated species distribution models: Combining presence-background data and site-occupancy data with imperfect detection. Methods in Ecology and Evolution, 8(4), 420–430. doi:10.1111/2041-210X.12738

Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. (2015). TMB: Automatic differentiation and laplace approximation. arXiv Preprint arXiv:1509.00660.

Křivan, V., Cressman, R., & Schneider, C. (2008). The ideal free distribution: A review and synthesis of the game-theoretic perspective. Theoretical Population Biology, 73, 403–425. doi:10.1016/j.tpb.2007.12.009

Lancia, R. A., Kendall, W. L., Pollock, K. H., & Nichols, J. D. (2005). Estimating the number of animals in wildlife populations. In Techniques for wildlife investigations and management (pp. 106–153). Bethesda, MD: Wildlife Society.

Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., & Morales, J. M. (2012). Flexible and practical modeling of animal telemetry data: Hidden markov models and extensions. Ecology, 93(11), 2336–2342. doi:10.1890/11-2241.1

Laundré, J. W., Hernández, L., & Ripple, W. J. (2010). The Landscape of Fear: Ecological Implications of Being Afraid (Vol. 3, pp. 1–7).

Law, G. R., Feltbower, R. G., Taylor, J. C., Parslow, R. C., Gilthorpe, M. S., Boyle, P., & McKinney, P. A. (2008, August). What do epidemiologists mean by ’population mixing’? Pediatr Blood Cancer. doi:10.1002/pbc.21570

Lele, Subhash R. (2009). A new method for estimation of resource selection probability function. Journal of Wildlife Management, 73(1), 122–127. doi:10.2193/2007-535

Lele, Subhash R., & Keim, J. (2006). Weighted distributions and estimation of resource selection probability functions. Ecology, 87(12), 3021–3028.

Levin, S. A. (1992). The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture. Ecology, 73(6), 1943–1967. doi:10.2307/1941447

Linden, D. W., Sirén, A. P., & Pekins, P. J. (2018). Integrating telemetry data into spatial capture–recapture modifies inferences on multi-scale resource selection. Ecosphere, 9(4), e02203.

Lindgren, F., Rue, H., et al. (2015). Bayesian spatial modelling with R-INLA. Journal of Statistical Software, 63(19), 1–25. doi:10.18637/jss.v063.i19

Lindgren, F., & Rue, H. (2015). Bayesian spatial modelling with R-INLA. Journal of Statistical Software, 63(19), 1–25. Retrieved from http://www.jstatsoft.org/v63/i19/

Link, W. A., Schofield, M. R., Barker, R. J., & Sauer, J. R. (2018). On the robustness of N-mixture models. Ecology, 99(7), 1547–1551. doi:10.1002/ecy.2362

Loehlin, J. C., & Beaujean, A. A. (2016). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis (p. 390). Routledge.

MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Bailey, L., & Hines, J. E. (2017). Occupancy estimation and modeling: Inferring patterns and dynamics of species occurrence. Elsevier.

Manceur, A. M., & Kühn, I. (2014). Inferring model-based probability of occurrence from preferentially sampled data with uncertain absences using expert knowledge. Methods in Ecology and Evolution, 5(8), 739–750.

Manly, B., McDonald, L., Thomas, D., McDonald, T. L., & Erickson, W. P. (2002). Resource selection by animals: Statistical design and analysis for field studies (p. 222). Springer Science & Business Media. doi:10.1007/0-306-48151-0

Mark S.Boyce, Pierre R.Vernier, Scott E.Nielsen, & Fiona K.A.Schmiegelow. (2002). Evaluating resource selection functions. Ecological Modelling, 157(2-3), 281–300. doi:10.1016/S0304-3800(02)00200-4

Marzluff, J. M., Millspaugh, J. J., Hurvitz, P., & Handcock, M. S. (2004). Relating resources to a probabilistic measure of space use: Forest fragments and steller’s jays. Ecology, 85(5), 1411–1427.

Matthiopoulos, Jason. (2003a). Model-supervised kernel smoothing for the estimation of spatial usage. Oikos, 102(2), 367–377. doi:10.1034/j.1600-0706.2003.12528.x

Matthiopoulos, Jason. (2003b). The use of space by animals as a function of accessibility and preference. Ecological Modelling, 159(2-3), 239–268. doi:10.1016/S0304-3800(02)00293-4

Matthiopoulos, J. (2011). How to be a Quantitative Ecologist: The ’A to R’ of Green Mathematics and Statistics. doi:10.1002/9781119991595

Matthiopoulos, J., & Aarts, G. (2007). The Spatial analysis of marine mammal abundance. In I. L. Boyd, W. D. Bowen, & S. J. Iverson (Eds.), Marine mammal ecology and conservation: A handbook of techniques (oxford biology) (techniques in ecology & conservation) (pp. 27–33). Oxford University Press. doi:10.1007/978-94-009-5211-9\_4

Matthiopoulos, Jason, Cordes, L., Mackey, B., Thompson, D., Duck, C., Smout, S., … Thompson, P. (2014). State-space modelling reveals proximate causes of harbour seal population declines. Oecologia, 174(1), 151–162. doi:10.1007/s00442-013-2764-y

Matthiopoulos, Jason, Fieberg, J., Aarts, G., Barraquand, F., & Kendall, B. E. (2020). Within reach? Habitat availability as a function of individual mobility and spatial structuring. The American Naturalist, 195(6), 1009–1026. doi:10.1086/708519

Matthiopoulos, Jason, Fieberg, J., Aarts, G., Beyer, H. L., Morales, J. M., & Haydon, D. T. (2015). Establishing the link between habitat selection and animal population dynamics. Ecological Monographs, 85(3), 413–436. doi:10.1890/14-2244.1

Matthiopoulos, Jason, Field, C., & MacLeod, R. (2019). Predicting population change from models based on habitat availability and utilization. Proceedings of the Royal Society B: Biological Sciences, 286(1901). doi:10.1098/rspb.2018.2911

Matthiopoulos, Jason, Harwood, J., & Thomas, L. (2005). Metapopulation consequences of site fidelity for colonially breeding mammals and birds. Journal of Animal Ecology, 74(4), 716–727. doi:10.1111/j.1365-2656.2005.00970.x

Matthiopoulos, Jason, Hebblewhite, M., Aarts, G., & Fieberg, J. (2011). Generalized functional responses for species distributions. Ecology, 92(3), 583–589. doi:10.1890/10-0751.1

Maxwell, J. (2018). The allure of lure and its impact on perceived community composition when monitoring tropical mammalian biodiversity (PhD thesis). University of Delaware.

Mayor, S. J., Schneider, D. C., Schaefer, J. A., & Mahoney, S. P. (2009). Habitat selection at multiple scales. Ecoscience, 16(2), 238–247. doi:10.2980/16-2-3238

McClintock, B. T., King, R., Thomas, L., Matthiopoulous, J., McConnell, B. J., & Morales, J. M. (2012). A general modelling framework for animal movement and migration using multistate random walks. Ecological Monographs, 82(3), 1–52. doi:10.1890/11-0326.1

McCrea, R. S., & Morgan, B. J. (2014). Analysis of capture-recapture data. Chapman; Hall/CRC.

McInerny, G. J., & Etienne, R. S. (2013). ’Niche’ or ’distribution’ modelling? A response to warren. Trends in Ecology and Evolution, 28(4), 191–192. doi:10.1016/j.tree.2013.01.007

McNamara, J. M., & Houston, A. I. (1987). Starvation and predation as factors limiting population size. Ecology, 68(5), 1515–1519. doi:10.2307/1939235

Merkle, J. A., Sawyer, H., Monteith, K. L., Dwinnell, S. P., Fralick, G. L., & Kauffman, M. J. (2019). Spatial memory shapes migration and its benefits: Evidence from a large herbivore. Ecology Letters, 22(11), 1797–1805.

Merow, C., Smith, M. J., Jr, T. C. E., Guisan, A., Mcmahon, S. M., Normand, S., … Elith, J. (2014). What do we gain from simplicity versus complexity in species distribution models? Ecography, 37(12), 1267–1281. doi:10.1111/ecog.00845

Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058–1069.

Michelot, T., Blackwell, P. G., & Matthiopoulos, J. (2019). Linking resource selection and step selection models for habitat preferences in animals. Ecology, 100(1), 1–12. doi:10.1002/ecy.2452

Miller, D. A., Pacifici, K., Sanderlin, J. S., & Reich, B. J. (2019). The recent past and promising future for data integration methods to estimate species’ distributions. Methods in Ecology and Evolution, 10(1), 22–37. doi:10.1111/2041-210X.13110

Miller, J. A., & Holloway, P. (2017). Niche Theory and Models. International Encyclopedia of Geography: People, the Earth, Environment and Technology, 1–10. doi:10.1002/9781118786352.wbieg0637

Mills, D., Fattebert, J., Hunter, L., & Slotow, R. (2019). Maximising camera trap data: Using attractants to improve detection of elusive species in multi-species surveys. PloS One, 14(5), e0216447.

Millspaugh, J. J., Nielson, R. M., McDonald L., L., Marzluff, J. M., Gitzen, R. A., Rittenhouse, C. D., … Sheriff, S. L. (2006). Analysis of resource selection using utilization distributions. Journal of Wildlife Management, 70(2), 384–395. doi:10.2193/0022-541X(2006)70[384:AORSUU]2.0.CO;2

Moilanen, A., Wilson, & Possingham, H. P. (2008). Spatial Conservation Prioritization: Quantitative Methods and Computational Tools (p. 328). Oxford UP.

Møller, J., Syversveen, A. R., & Waagepetersen, R. P. (1998). Log gaussian cox processes. Scandinavian Journal of Statistics, 25(3), 451–482.

Monsarrat, S., Pennino, M. G., Smith, T. D., Reeves, R. R., Meynard, C. N., Kaplan, D. M., & Rodrigues, A. S. L. (2015). Historical summer distribution of the endangered North Atlantic right whale (Eubalaena glacialis): a hypothesis based on environmental preferences of a congeneric species. Diversity and Distributions, 21(8), 925–937. doi:10.1111/ddi.12314

Moorcroft, Paul R. (2012). Mechanistic approaches to understanding and predicting mammalian space use: recent advances, future directions. Journal of Mammalogy, 93(4), 903–916. doi:10.1644/1

Moorcroft, Paul R., & Barnett, A. (2008). Mechanistic home range models and resource selection analysis: A reconciliation and unification. Ecology, 89(4), 1112–1119. doi:10.1890/06-1985.1

Moorcroft, Paul R., Lewis, M. A., & Crabtree, R. L. (1999). Home Range Analysis Using a Mechanistic Home Range Model, 80(5), 1656–1665.

Moorcroft, Paul R., Lewis, M. A., & Crabtree, R. L. (2006). Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone. Proceedings of the Royal Society B: Biological Sciences, 273(1594), 1651–1659. doi:10.1098/rspb.2005.3439

Morris, L. R., Proffitt, K. M., & Blackburn, J. K. (2016). Mapping resource selection functions in wildlife studies: Concerns and recommendations. Applied Geography, 76, 173–183. doi:10.1016/j.apgeog.2016.09.025

Muff, S., Signer, J., & Fieberg, J. (2019). Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. Journal of Animal Ecology, (July), 1–13. doi:10.1111/1365-2656.13087

Muff, S., Signer, J., & Fieberg, J. (2020). Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using bayesian or frequentist computation. Journal of Animal Ecology, 89(1), 80–92.

Murdoch, W. W., Briggs, C. J., & Nisbet, R. M. (2003). Consumer-resource dynamics (p. 464). Princeton University Press.

Murray, J. D. (2013). Mathematical Biology (p. 770). Springer.

Murtaugh, P. A. (2014). In defense of P values. Ecology, 95(3), 611–617. doi:10.1093/JNCICS/PKAA012

Mysterud, A., & Ims, R. A. (1998). Functional responses in habitat use: Availability influences relative use in trade-off situations. Ecology, 79(4), 1435–1441.

Newman, K. B., Buckland, S. T., Morgan, B. J. T., King, R., Borchers, D. L., Cole, D. J., … Thomas, L. (2014). Modelling Population Dynamics: Model Formulation, Fitting and Assessment Using State-Space Methods (p. 228). New York: Springer.

Nichols, J. D., Hines, J. E., Sauer, J. R., Fallon, F. W., Fallon, J. E., & Heglund, P. J. (2000). A double-observer approach for estimating detection probability and abundance from point counts. The Auk, 117(2), 393–408.

Nisbet, R. M., & Gurney, W. S. C. (2004). Modelling fluctuating populations (p. 396). Blackburn.

Noonan, M. J., Fleming, C. H., Tucker, M. A., Kays, R., Harrison, A.-L., Crofoot, M. C., et al.others. (2020). Effects of body size on estimation of mammalian area requirements. Conservation Biology, 34(4), 1017–1028.

Noonan, M. J., Tucker, M. A., Fleming, C. H., Akre, T. S., Alberts, S. C., Ali, A. H., et al.others. (2019). A comprehensive analysis of autocorrelation and bias in home range estimation. Ecological Monographs, 89(2), e01344. doi:10.1002/ecm.1344

Nur, N. (1987). Population Growth Rate and the Measurement of Fitness: A Critical Reflection. Oikos, 48(3), 338. doi:10.2307/3565523

Nychka, D., Furrer, R., Paige, J., & Sain, S. (2021). Fields: Tools for spatial data. Boulder, CO, USA: University Corporation for Atmospheric Research. Retrieved from https://github.com/dnychka/fieldsRPackage

O’Connell, A. F., Nichols, J. D., & Karanth, K. U. (2010). Camera traps in animal ecology: Methods and analyses. Springer Science & Business Media.

Odling-Smee, F. J., Laland, K. N., & Feldman, M. W. (2013). Niche construction: the neglected process in evolution. Princeton: Princeton University Press.

Okubo, A. (1980). Diffusion and ecological problems: mathematical models. Basel, Switzerland: Springer.

Oliveira-Santos, L. G. R., Forester, J. D., Piovezan, U., Tomas, W. M., & Fernandez, F. A. (2016). Incorporating animal spatial memory in step selection functions. Journal of Animal Ecology, 85(2), 516–524.

Ollason, J. G. (1980). Learning to Forage-Optimally? Theoretical Population Biology, 18, 44–56.

Oppel, S., Meirinho, A., Ramírez, I., Gardner, B., O’Connell, A. F., Miller, P. I., & Louzao, M. (2012). Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biological Conservation, 156, 94–104. doi:10.1016/j.biocon.2011.11.013

Otto, S. P., & Day, T. (2011). A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution (p. 744). Princeton UP.

Ovaskainen, O., & Abrego, N. (2020). Joint Species Distribution Modelling: With Applications in R (Ecology, Biodiversity and Conservation) (p. 388). Cambridge University Press.

Ovaskainen, O., Hottola, J., & Shtonen, J. (2010). Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. Ecology, 91(9), 2514–2521. doi:10.1890/10-0173.1

Pacifici, K., Reich, B. J., Miller, D. A., Gardner, B., Stauffer, G., Singh, S., … Collazo, J. A. (2017). Integrating multiple data sources in species distribution modeling: A framework for data fusion. Ecology, 98(3), 840–850. doi:10.1002/ecy.1710

Pacifici, K., Reich, B. J., Miller, D. A., & Pease, B. S. (2019). Resolving misaligned spatial data with integrated species distribution models. Ecology, e02709.

Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M., & Packer, C. (2017, November). A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Blackwell Publishing Ltd. doi:10.1111/ele.12832

Paton S Robert, & Matthiopoulos Jason. (2018). Defining the scale of habitat availability for models of habitat selection. Ecology, 97(July), 1113–1122. doi:10.1002/ecy.2446

Pearce, J. L., & Boyce, M. S. (2006). Modelling distribution and abundance with presence-only data. Journal of Applied Ecology, 43(3), 405–412. doi:10.1111/j.1365-2664.2005.01112.x

Pearl, R., & Reed, L. J. (1920). On the Rate of Growth of the Population of the United States Since 1790 and its Mathematical Representation. Proceedings of the National Academy of Sciencesatural Academy of Sciences, 6, 275–288. doi:10.1073/pnas.6.6.275

Pearman, P. B., Guisan, A., Broennimann, O., & Randin, C. F. (2008). Niche dynamics in space and time. Trends in Ecology and Evolution, 23(3), 149–158. doi:10.1016/j.tree.2007.11.005

Pearson, R. G., & Dawson, T. P. (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12(5), 361–371. Retrieved from http://dx.doi.org/10.1046/j.1466-822X.2003.00042.x

Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martinez-Meyer, E., Nakamura, M., & Araújo, M. B. (2011). Ecological niches and geographic distributions (Vol. 56, p. 314). Princeton University Press.

Phillips, S. J., & Dudik, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 31(2), 161–175.

Photopoulou, T., Fedak, M. A., Thomas, L., & Matthiopoulos, J. (2014). Spatial variation in maximum dive depth in gray seals in relation to foraging. Marine Mammal Science, 30(3), 923–938. doi:10.1111/mms.12092

Proffitt, K. M., Goldberg, J. F., Hebblewhite, M., Russell, R., Jimenez, B., Robinson, H. S., … Schwartz, M. K. (2015). Integrating resource selection into spatial capture-recapture models for large carnivores. Ecosphere, 6(11), 1–15.

Pulliam, H. R. (2000). On the relationship between niche and distribution. Ecology Letters, 3(4), 349–361. doi:10.1046/j.1461-0248.2000.00143.x

Qiao, H., Feng, X., Escobar, L. E., Peterson, A. T., Soberón, J., Zhu, G., & Papeş, M. (2019). An evaluation of transferability of ecological niche models. Ecography, 42(3), 521–534. doi:10.1111/ecog.03986

Randin, C. F., Dirnböck, T., Dullinger, S., Zimmermann, N. E., Zappa, M., & Guisan, A. (2006). Are niche-based species distribution models transferable in space? Journal of Biogeography, 33(10), 1689–1703. doi:10.1111/j.1365-2699.2006.01466.x

Renner, I. W., Elith, J., Baddeley, A., Fithian, W., Hastie, T., Phillips, S. J., … Warton, D. I. (2015). Point process models for presence-only analysis. Methods in Ecology and Evolution, 6(4), 366–379.

Renner, I. W., Louvrier, J., & Gimenez, O. (2019). Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalised likelihood maximisation. bioRxiv, 615583.

Renner, I. W., & Warton, D. I. (2013). Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69(1), 274–281.

Riotte-Lambert, L., & Matthiopoulos, J. (2019). Communal and efficient movement routines can develop spontaneously through public information use. Behavioral Ecology, 30(2), 408–416. doi:10.1093/beheco/ary180

Robertson;, M. P., Caithness, N., & Villet, M. H. (2012). A PCA-based modelling for predicting technique environmental suitability for organisms from presence records. Biodiversity Research, 7(1), 15–27.

Robinson, A. L. M., Elith, J., Hobday, A. J., Pearson, R. G., Kendall, B. E., & Richardson, A. J. (2017). Pushing the limits in marine species distribution modelling : Lessons from the land present challenges and opportunities linked references are available on JSTOR for this article : Pushing the limits in marine species distribution modelling : Lessons from. Global Ecology and Biogeography Biogeography, 20(6), 789–802.

Royle, J. A. (2004). N-mixture models for estimating population size from spatially replicated counts. Biometrics, 60(1), 108–115.

Royle, J. A., Chandler, R. B., Sollmann, R., & Gardner, B. (2013). Spatial capture-recapture. Academic Press.

Royle, J. A., Chandler, R. B., Sun, C. C., & Fuller, A. K. (2013). Integrating resource selection information with spatial capture–recapture. Methods in Ecology and Evolution, 4(6), 520–530.

Russell, D. J. F., Hastie, G. D., Thompson, D., Janik, V. M., Hammond, P. S., Scott-Hayward, L. A. S., … McConnell, B. J. (2016). Avoidance of wind farms by harbour seals is limited to pile driving activities. Journal of Applied Ecology, 53(6). doi:10.1111/1365-2664.12678

Sarker, S. K., Reeve, R., & Matthiopoulos, J. (2021). Solving the fourth-corner problem: forecasting ecosystem primary production from spatial multispecies trait-based models. Ecological Monographs, 0(0), 0–3. doi:10.1002/ecm.1454

Sarker, S. K., Reeve, R., Thompson, J., Paul, N. K., & Matthiopoulos, J. (2016). Are we failing to protect threatened mangroves in the Sundarbans world heritage ecosystem? Scientific Reports, 6. doi:10.1038/srep21234

Schank, C. J., Cove, M. V., Kelly, M. J., Mendoza, E., O’Farrill, G., Reyna-Hurtado, R., et al.others. (2017). Using a novel model approach to assess the distribution and conservation status of the endangered baird’s tapir. Diversity and Distributions, 23(12), 1459–1471. doi:10.1111/ddi.12631

Scheele, B. C., Foster, C. N., Banks, S. C., & Lindenmayer, D. B. (2017). Niche contractions in declining species: Mechanisms and consequences. Trends in Ecology and Evolution, 32(5), 346–355. doi:10.1016/j.tree.2017.02.013

Schurr, F. M., Pagel, J., Cabral, J. S., Groeneveld, J., Bykova, O., O’Hara, R. B., … Zimmermann, N. E. (2012). How to understand species’ niches and range dynamics: A demographic research agenda for biogeography. Journal of Biogeography, 39(12), 2146–2162. doi:10.1111/j.1365-2699.2012.02737.x

Scotson, L., Fredriksson, G., Ngoprasert, D., Wong, W.-M., & Fieberg, J. (2017). Projecting range-wide sun bear population trends using tree cover and camera-trap bycatch data. PloS One, 12(9), e0185336.

Seale, M., Zhdanov, O., Cummins, C., Kroll, E., Blatt, M., Busse, A., … Nakayama, N. (2019). Moisture-Dependent Morphing Tunes the Dispersal of Dandelion Diaspores. SSRN Electronic Journal. doi:10.2139/ssrn.3334428

Seidler, T. G., & Plotkin, J. B. (2006). Seed dispersal and spatial pattern in tropical trees. PLoS Biol, 4(11), e344.

Sequeira, A. M. M., Bouchet, P. J., Yates, K. L., Mengersen, K., & Caley, M. J. (2018). Transferring biodiversity models for conservation: Opportunities and challenges. Methods in Ecology and Evolution, 9(5), 1250–1264. doi:10.1111/2041-210X.12998

Sicacha-Parada, J., Steinsland, I., Cretois, B., & Borgelt, J. (2020). Accounting for spatial varying sampling effort due to accessibility in Citizen Science data: A case study of moose in Norway. Spatial Statistics, 100446. doi:10.1016/j.spasta.2020.100446

Signer, J., Fieberg, J., & Avgar, T. (2017). Estimating utilization distributions from fitted step-selection functions. Ecosphere, 8(4). doi:10.1002/ecs2.1771

Signer, J., Fieberg, J., & Avgar, T. (2019). Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecology and Evolution, (July 2018), 880–890. doi:10.1002/ece3.4823

Silverman, B. W. (1986). DENSITY ESTIMATION FOR STATISTICS AND DATA ANALYSIS (pp. 1–45). Chapman; Hall. doi:10.1201/9781315140919

Silvertown, J. (2009). A new dawn for citizen science. Trends in Ecology and Evolution, 24, 467–470. doi:10.1016/j.chemosphere.2018.03.203

Simmonds, E. G., Jarvis, S. G., Henrys, P. A., Isaac, N. J., & O’Hara, R. B. (2020). Is more data always better? A simulation study of benefits and limitations of integrated distribution models. Ecography, 43, 1413–1422.

Šmejkal, M., Souza, A. T., Blabolil, P., Bartoň, D., Sajdlová, Z., Vejřík, L., & Kubečka, J. (2018). Nocturnal spawning as a way to avoid egg exposure to diurnal predators. Scientific Reports, 8(1), 1–7. doi:10.1038/s41598-018-33615-4

Soberón, J. (2007). Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10(12), 1115–1123. doi:10.1111/j.1461-0248.2007.01107.x

Sollmann, R. (2018). A gentle introduction to camera-trap data analysis. African Journal of Ecology, 56(4), 740–749.

Sollmann, R., Gardner, B., Belant, J. L., Wilton, C. M., & Beringer, J. (2016). Habitat associations in a recolonizing, low-density black bear population. Ecosphere, 7(8).

Steenweg, R., Hebblewhite, M., Whittington, J., Lukacs, P., & McKelvey, K. (2018). Sampling scales define occupancy and underlying occupancy–abundance relationships in animals. Ecology, 99(1), 172–183.

Steinhorst, R. K., & Samuel, M. D. (1989). Sightability adjustment methods for aerial surveys of wildlife populations. Biometrics, 415–425.

Stephens, P. A., & Sutherland, W. J. (1999). What is the Allee effect? Oikos, 87(1), 185–190. doi:10.2307/3547011

Street, G. M., Vander Vennen, L. M., Avgar, T., Mosser, A., Anderson, M. L., Rodgers, A. R., & Fryxell, J. M. (2015). Habitat selection following recent disturbance: Model transferability with implications for management and conservation of moose (Alces alces). Canadian Journal of Zoology, 93(999), 813–821.

Swihart, R. K., & Slade, N. A. (1985). Testing for independence of observations in animal movements. Ecology, 66(4), 1176–1184.

Thurfjell, H., Ciuti, S., & Boyce, M. S. (2014). Applications of step-selection functions in ecology and conservation. Movement Ecology, 2(4).

Tikhonov, G., Opedal, Ø. H., Abrego, N., Lehikoinen, A., Jonge, M. M. J. de, Oksanen, J., & Ovaskainen, O. (2020). Joint species distribution modelling with the r-package Hmsc. Methods in Ecology and Evolution, 11(3), 442–447. doi:10.1111/2041-210X.13345

Tilman, D. (1982). Resource competition and community structure. Monographs in Population Biology, 17, 1–296.

Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit. Economic Geography, 46, 234–240.

Tredennick, A. T., Hooten, M. B., & Adler, P. B. (2017). Do we need demographic data to forecast plant population dynamics? Methods in Ecology and Evolution, 8(5), 541–551. doi:10.1111/2041-210X.12686

Tuanmu, M.-N., Viña, A., Roloff, G. J., Liu, W., Ouyang, Z., Zhang, H., & Liu, J. (2011). Temporal transferability of wildlife habitat models: implications for habitat monitoring. Journal of Biogeography, 38(8), 1510–1523. doi:10.1111/j

Van Gils, J. A., Edelaar, P., Escudero, G., & Piersma, T. (2004, January). Carrying capacity models should not use fixed prey density thresholds: A plea for using more tools of behavioural ecology. doi:10.1111/j.0030-1299.2003.12214.x

Van Leeuwen, A., De Roos, A. M., & Persson, L. (2008). How cod shapes its world. Journal of Sea Research, 60(1-2), 89–104. doi:10.1016/j.seares.2008.02.008

Van Winkle, W. (1975). Comparison of several probabilistic home-range models. The Journal of Wildlife Management, 118–123.

Verhulst, P. (1845). Resherches mathematiques sur la loi d’accroissement de la population. Nouveaux Memoires de l’Academie Royale Des Sciences, 18, 1–41.

Wang, Y., & Stone, L. (2019). Understanding the connections between species distribution models for presence-background data. Theoretical Ecology, 12(1), 73–88. doi:10.1007/s12080-018-0389-9

Warren, D. L. (2012). In defense of ’niche modeling’. Trends in Ecology and Evolution, 27(9), 497–500. doi:10.1016/j.tree.2012.03.010

Warren, D. L. (2013). ’Niche modeling’: That uncomfortable sensation means it’s working. A reply to McInerny and etienne. Trends in Ecology and Evolution, 28(4), 193–194. doi:10.1016/j.tree.2013.02.003

Warton, D. I., Renner, I. W., & Ramp, D. (2013). Model-based control of observer bias for the analysis of presence-only data in ecology. PloS One, 8(11), e79168. doi:10.1371/journal.pone.0079168

Warton, D. I., & Shepherd, L. C. (2010). Poisson point process models solve the ‘pseudo-absence problem’ for presence-only data in ecology. The Annals of Applied Statistics, 4(3), 1383–1402.

Watts, K., Whytock, R. C., Park, K. J., Fuentes-Montemayor, E., Macgregor, N. A., Duffield, S., & McGowan, P. J. K. (2020). Ecological time lags and the journey towards conservation success. Nature Ecology and Evolution, 4(3), 304–311. doi:10.1038/s41559-019-1087-8

Wearn, O. R., & Glover-Kapfer, P. (2019). Snap happy: Camera traps are an effective sampling tool when compared with alternative methods. Royal Society Open Science, 6(3), 181748.

Weddell, B. J. (2002). Conserving Living Natural Resources. Cambridge University Press. doi:10.1017/cbo9780511804298

Welsh, A. H., Lindenmayer, D. B., & Donnelly, C. F. (2013). Fitting and interpreting occupancy models. PloS One, 8(1), e52015.

Wenger, S. J., & Olden, J. D. (2012). Assessing transferability of ecological models: An underappreciated aspect of statistical validation. Methods in Ecology and Evolution, 3(2), 260–267. doi:10.1111/j.2041-210x.2011.00170.x

Wilson, R. P., Börger, L., Holton, M. D., Scantlebury, D. M., Gómez-Laich, A., Quintana, F., … Shepard, E. L. C. (2020). Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. Journal of Animal Ecology, 89(1), 161–172. doi:10.1111/1365-2656.13040

Wisz, M. S., Pottier, J., Kissling, W. D., Pellissier, L., Damgaard, C. F., Dormann, C. F., … Svenning, J. (2013). The role of biotic interactions in shaping distributions and realised assemblages of species : Implications for species distribution modelling, 88, 15–30. doi:10.1111/j.1469-185X.2012.00235.x

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

Wood, S. N., Pya, N., & Säfken, B. (2016). Smoothing Parameter and Model Selection for General Smooth Models. Journal of the American Statistical Association, 111(516), 1548–1563. doi:10.1080/01621459.2016.1180986

Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home-range studies. Ecology, 70(1), 164–168.

Yackulic, C. B., Chandler, R., Zipkin, E. F., Royle, J. A., Nichols, J. D., Campbell Grant, E. H., & Veran, S. (2013). Presence-only modelling using MAXENT: When can we trust the inferences? Methods in Ecology and Evolution, 4(3), 236–243. doi:10.1111/2041-210x.12004

Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., … Sequeira, A. M. M. (2018). Outstanding challenges in the transferability of ecological models. Trends in Ecology and Evolution, 33(10), 790–802. doi:10.1016/j.tree.2018.08.001

Yen, J. D. L., Tonkin, Z., Lyon, J., Koster, W., Kitchingman, A., Stamation, K., & Vesk, P. A. (2019). Integrating multiple data types to connect ecological theory and data among levels. Frontiers in Ecology and Evolution, 7, 95. doi:10.3389/fevo.2019.00095

Yuan, Y., Bachl, F. E., Lindgren, F., Borchers, D. L., Illian, J. B., Buckland, S. T., et al.others. (2017). Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales. The Annals of Applied Statistics, 11(4), 2270–2297. doi:10.1214/17-AOAS1078

Zipkin, E. F., & Saunders, S. P. (2018). Synthesizing multiple data types for biological conservation using integrated population models. Biological Conservation, 217, 240–250. doi:10.1016/j.biocon.2017.10.017