master test

Hint

See also

Species-accumulation curves

Spatial capture-recapture (SCR) / Spatially explicit capture recapture (SECR): The SECR (or SCR) method is used to estimate the density of marked populations; an extension of traditional capture-recapture (CR; Karanth, 1995; Karanth & Nichols, 1998) models (Karanth, 1995; Karanth & Nichols, 1998) that explicitly accounts for camera location and animal movement (Burgar et al., 2018). SECR models use spatially referenced individual capture histories to infer where animals’ home range centres are, assuming that detection probability decreases with increasing distance between cameras and home range centres (Clarke et al., 2023). SECR models can be implemented using different statistical frameworks, including Bayesian estimation (Royle and Young, 2008; Morin et al., 2022).

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Ahumada et al., 2011

Assumptions, Pros, Cons

Assumptions

• Demographic closure (i.e., no births or deaths) (Wearn & Glover-Kapfer, 2017)

• Detection probability. of different individuals is equal (Wearn & Glover-Kapfer, 2017)

  • or, for SECR., individuals have equal Detection probability. at a given distance from the centre of their home range. (Wearn & Glover-Kapfer, 2017)

• Detections of different individuals are independent (Wearn & Glover-Kapfer, 2017)

• Behaviour is unaffected by cameras and marking. (Wearn & Glover-Kapfer, 2017)

• Individuals do not lose marks (Wearn & Glover-Kapfer, 2017)

• Individuals are not misidentified (Wearn & Glover-Kapfer, 2017)

• Surveys. are independent (Wearn & Glover-Kapfer, 2017)

• For conventional models, geographic closure (i.e., no immigration or emigration) (Wearn & Glover-Kapfer, 2017)

• Spatially explicit models have further assumptions. about animal movement (Wearn & Glover-Kapfer, 2017; Rowcliffe et al., 2008; Royle et al., 2009; O’Brien et al., 2011); these include:

  • Home ranges. are stable (Wearn & Glover-Kapfer, 2017)

  • Movement is unaffected by the cameras (Wearn & Glover-Kapfer, 2017)

  • Camera locations. are randomly placed. with respect to the distribution and orientation of home ranges. (Wearn & Glover-Kapfer, 2017)

  • Distribution of home range. centres follows a defined distribution (Poisson, or other, e.g., negative binomial) (Wearn & Glover-Kapfer, 2017)

Pros

• Produces direct estimates of density. or population size for explicit spatial regions (Chandler & Royle, 2013)

• Allows researchers to mark a subset of the population / to take advantage of natural markings (Wearn & Glover-Kapfer, 2017)

• Estimates are fully comparable across space, time, species and studies (Wearn & Glover-Kapfer, 2017)

• density. estimates obtained in a single model, fully incorporate spatial information of locations and individuals (Wearn & Glover-Kapfer, 2017)

• Both likelihood-based and Bayesian versions of the model have been implemented in relatively easy-to-use software DENSITY and SPACECAP, respectively, as well as associated R packages) (Wearn & Glover-Kapfer, 2017)

• Flexibility in study design (e.g., ‘holes’ in the trapping grid) (Wearn & Glover-Kapfer, 2017)

• Open SECR. (Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle et al., 2009) models exist that allow for estimation of recruitment and survival rates (Wearn & Glover-Kapfer, 2017)

• Avoid ad-hoc definitions of study area and edge effects (Doran-Myers, 2018)

• SECR. (Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle et al., 2009) accounts for variation in individual Detection probability.; can produce spatial variation in density.; SECR. (Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle et al., 2009) more sensitive to detect moderate-to-major populations changes (+/-20-80%) (Royle & Young, 2008; Royle et al., 2009)

Cons

• Requires that individuals are identifiable (Wearn & Glover-Kapfer, 2017)

• Requires that a minimum number of individuals are trapped (each recaptured multiple times ideally) (Wearn & Glover-Kapfer, 2017)

• Requires that each individual is captured at a number of camera locations. (Wearn & Glover-Kapfer, 2017)

• Multiple cameras per station may be required to identify individuals; difficult to implement at large spatial scales as it requires a high density. of cameras (Morin et al., 2022)

• May not be precise. enough for long-term monitoring (Green et al., 2020)

• Cameras must be close enough that animals are detected at multiple camera locations. (Wearn & Glover-Kapfer, 2017) (may be challenging to implement at large scales as many cameras are needed)’ (Chandler & Royle, 2013)

• ½ MMDM (Mean Maximum Distance Moved) will usually lead to an underestimation of home range. size and thus overestimation of density. (Parmenter et al., 2003; Noss et al., 2012; Wearn & Glover-Kapfer, 2017)

Overview

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In-depth

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Visual resources

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Shiny apps/Widgets

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Analytical tools & Resources

Type	Name	Note	URL	Reference
rJAGS/R code	mfidino/multi-state-occupancy-models		mfidino/multi-state-occupancy-models	Fidino, M. (2021a) multi-state-occupancy-models. https://github.com/mfidino/integrated-occupancy-model
JAGS/R code	A gentle introduction to an integrated occupancy model that combines presence-only and detection/non-detection data, and how to fit it in JAGS; integrated-occupancy-model”		https://masonfidino.com/bayesian_integrated_model/; mfidino/integrated-occupancy-model	Fidino, M. (2021b) A gentle introduction to an integrated occupancy model that combines presence-only and detection/non-detection data, and how to fit it in JAGS https://masonfidino.com/bayesian_integrated_model ; Fidino, M. (2021c) integrated-occupancy-models https://github.com/mfidino/integrated-occupancy-model
JAGS code/Tutorial	So, you don’t have enough data to fit a dynamic occupancy model? An introduction to auto-logistic occupancy models; auto-logistic-occupancy
https://masonfidino.com/autologistic_occupancy_model/; mfidino/auto-logistic-occupancy	Fidino, M. (2021d) So, you don’t have enough data to fit a dynamic occupancy model? An introduction to auto-logistic occupancy models. https://masonfidino.com/autologistic_occupancy_model ; Fidino, M. (2021e) auto-logistic-occupancy. https://github.com/mfidino/auto-logistic-occupancy
R package	Package “autoOcc”	An R package for fitting autologistic occupancy models	mfidino/autoOcc	Fidino, M. (2023) autoOcc: An R package for fitting autologistic occupancy models. R package version 0.1.1, https://github.com/mfidino/autoOcc
R code	mfidino/periodicity	Using Fourier series to predict periodic patterns in dynamic occupancy models	mfidino/periodicity	Fidino, M., & Magle, S. B. (2017). Using Fourier series to predict periodic patterns in dynamic occupancy models. Ecosphere,8(9) , e01944. https://doi.org/10.1002/ecs2.1944
Spreadsheet	OccPower.xlsx	Spreadsheet to compute power to detect difference in 2 independent occupancy estimates using asymptotic approximations described in Guillera-Arroita et. al. (2012).	Download the XLS	Guillera-Arroita, G., & Lahoz-Monfort, J. J. (2012). Designing studies to detect differences in species occupancy: Power analysis under imperfect detection. Methods in Ecology and Evolution, 3(5), 860-869. https://doi.org/10.1111/j.2041-210X.2012.00225.x
R code/Tutorial	“An Introduction to Camera Trap Data Management and Analysis in R > Chapter 11 Occupancy”		https://bookdown.org/c_w_beirne/wildCo-Data-Analysis/occupancy.html	WildCo Lab (2021c). Chapter 11 Occupancy. https://bookdown.org/c_w_beirne/wildCo-Data-Analysis/occupancy.html
Program	Program “PRESENCE”	“Relatively simple, but comprehensive, software dedicated to occupancy estimation. Linux version available. Can also be used for occupancy-based species richness estimation.” (Wearn & Glover-Kapfer, 2017)	Software: <www.mbr-pwrc.usgs.gov/software/presence.html>; Help forum: <www.phidot.org>	Hines, J. E. (2006). PRESENCE - Software to estimate patch occupancy and related parameters. https://www.mbr-pwrc.usgs.gov/software/presence.html.
R package	Package “RPresence”	“The R counterpart to Presence. Cross-platform (Windows, Mac and Linux).” (Wearn & Glover-Kapfer, 2017)	https://www.mbr-pwrc.usgs.gov/software/presence.shtml	Hines, J. E. (2006). PRESENCE - Software to estimate patch occupancy and related parameters. https://www.mbr-pwrc.usgs.gov/software/presence.html.
R package	R package “unmarked”	“Implements a wide variety of occupancy and count-based abundance models (the latter are mostly not appropriate for camera-trapping). Actively being developed and supported by a community of users. Cross-platform (Windows, Mac and Linux).” (Wearn & Glover-Kapfer, 2017)	https://cran.r-project.org/web/packages/unmarked/index.html; https://groups.google.com/d/forum/unmarked,; https://hmecology.github.io/unmarked>	Fiske, I. & Chandler, R. (2011). unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. Journal of Statistical Software, 43(10), 1-23. https://www.jstatsoft.org/v43/i10 ; Kellner, K. F., Smith, A. D., Royle, J. A., Kery, M, Belant, J. L., & Chandler, R. B. (2023). The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology. Methods in Ecology and Evolution, 14 (6), 1408-1415. https://www.jstatsoft.org/v43/i10/
R code/Tutorial	Multi-season Occupancy Models		https://darinjmcneil.weebly.com/multi-season-occupancy.html	McNeil, D. (n.d.). Multi-season Occupancy Models. https://darinjmcneil.weebly.com/multi-season-occupancy.html
R package	Package “detect”	R package for analyzing wildlife data with detection error	psolymos/detect	Solymos, P. (2023). Package ‘detect’: Analyzing Wildlife Data with Detection Error. R package version 0.4-6. https://cran.r-project.org/web/packages/detect/detect.pdf
R code/Tutorial	Occupancy Modeling	Easy to follow explanation of occupancy models with accompanying tutorial and R code.	https://kevintshoemaker.github.io/NRES-746/Occupancy.html
Tutorial	occupancyTuts: Occupancy modelling tutorials with RPresence	Occupancy modelling tutorials with RPresence	https://doi.org/10.1111/2041-210X.14285	Donovan, T., Hines, J., & MacKenzie, D. (2024). OCCUPANCYTUTS: Occupancy modelling tutorials with RPRESENCE. Methods in Ecology and Evolution, 15(3), 477-483. https://doi.org/10.1111/2041-210X.14285
R code/Tutorial	Implicit dynamics occupancy models in R	Implicit dynamics occupancy models with the R package RPresence. These models estimate occupancy probability when it changes through time without estimating colonization and extinction parameters. The code and sample data from this tutorial are available on GitHub; jamesepaterson/occupancyworkshop.	https://jamesepaterson.github.io/jamespatersonblog/2024-06-02_implicitdynamicsoccupancy.html	Paterson, J. (2024). Implicit dynamics occupancy models in R. https://jamesepaterson.github.io/jamespatersonblog/2024-06-02_implicitdynamicsoccupancy.html
Tutorial	Using the mgcvmgcv package to create a generalized additive occupancy model in R		https://masonfidino.com/generalized_additive_occupancy_model	Fidino, M. (2021F) Using the mgcvmgcv package to create a generalized additive occupancy model in R. https://masonfidino.com/generalized_additive_occupancy_model
R Shiny app	Bias in single-season occupancy models	“Compute the relative bias (in %) in the maximum-likelihood estimator of the occupancy probability ψ in a single-season (aka static) occupancy model with constant parameters fitted with the package ‘unmarked’.”	Repo: oliviergimenez/bias_occupancy_flexdashboard App: https://ecologicalstatistics.shinyapps.io/bias_occupancy	Gimenez, O. (2020a). Bias in single-season occupancy models. https://ecologicalstatistics.shinyapps.io/bias_occupancy;
R code	Bias in occupancy estimate for a static model	“R code to calculate bias in occupancy estimate as a function of the detection probability given various levels of occupancy probability, various number of sites and surveys.”	oliviergimenez/bias_occupancy	Gimenez, O. (2020b). Bias in occupancy estimate for a static model. https://github.com/oliviergimenez/bias_occupancy
R code/ Presentation	Species Distribution Modelling	‘Vernon Visser provided a brief introduction to SDMs. Below you can replace the lecture slides and R script from this seminar. Provided in these materials is: - A step-by-step guide to running your own SDM - Suggestions for best practices - References that can help provide more detail on the methods -An R script that is annotated to make its understanding and adaptability easier’	https://science.uct.ac.za/seec/stats-toolbox-seminars-spatial-and-species-distribution-toolboxes/species-distribution-modelling

Analytical tools & Resources 2

Type	Name	Note	URL	Reference
rJAGS/R code	mfidino/multi-state-occupancy-models		mfidino/multi-state-occupancy-models	Fidino, M. (2021a) multi-state-occupancy-models. https://github.com/mfidino/integrated-occupancy-model
JAGS/R code	A gentle introduction to an integrated occupancy model that combines presence-only and detection/non-detection data, and how to fit it in JAGS; integrated-occupancy-model”		https://masonfidino.com/bayesian_integrated_model/; mfidino/integrated-occupancy-model	Fidino, M. (2021b) A gentle introduction to an integrated occupancy model that combines presence-only and detection/non-detection data, and how to fit it in JAGS https://masonfidino.com/bayesian_integrated_model ; Fidino, M. (2021c) integrated-occupancy-models https://github.com/mfidino/integrated-occupancy-model
R package	Package “autoOcc”	An R package for fitting autologistic occupancy models	mfidino/autoOcc	Fidino, M. (2023) autoOcc: An R package for fitting autologistic occupancy models. R package version 0.1.1, https://github.com/mfidino/autoOcc
R code	mfidino/periodicity	Using Fourier series to predict periodic patterns in dynamic occupancy models	mfidino/periodicity	Fidino, M., & Magle, S. B. (2017). Using Fourier series to predict periodic patterns in dynamic occupancy models. Ecosphere,8(9) , e01944. https://doi.org/10.1002/ecs2.1944
Spreadsheet	OccPower.xlsx	Spreadsheet to compute power to detect difference in 2 independent occupancy estimates using asymptotic approximations described in Guillera-Arroita et. al. (2012).	Download the XLS	Guillera-Arroita, G., & Lahoz-Monfort, J. J. (2012). Designing studies to detect differences in species occupancy: Power analysis under imperfect detection. Methods in Ecology and Evolution, 3(5), 860-869. https://doi.org/10.1111/j.2041-210X.2012.00225.x
R code/Tutorial	“An Introduction to Camera Trap Data Management and Analysis in R > Chapter 11 Occupancy”		https://bookdown.org/c_w_beirne/wildCo-Data-Analysis/occupancy.html	WildCo Lab (2021c). Chapter 11 Occupancy. https://bookdown.org/c_w_beirne/wildCo-Data-Analysis/occupancy.html
Program	Program “PRESENCE”	“Relatively simple, but comprehensive, software dedicated to occupancy estimation. Linux version available. Can also be used for occupancy-based species richness estimation.” (Wearn & Glover-Kapfer, 2017)	Software: <www.mbr-pwrc.usgs.gov/software/presence.html>; Help forum: <www.phidot.org>	Hines, J. E. (2006). PRESENCE - Software to estimate patch occupancy and related parameters. https://www.mbr-pwrc.usgs.gov/software/presence.html.
R package	Package “RPresence”	“The R counterpart to Presence. Cross-platform (Windows, Mac and Linux).” (Wearn & Glover-Kapfer, 2017)	https://www.mbr-pwrc.usgs.gov/software/presence.shtml	Hines, J. E. (2006). PRESENCE - Software to estimate patch occupancy and related parameters. https://www.mbr-pwrc.usgs.gov/software/presence.html.
R package	R package “unmarked”	“Implements a wide variety of occupancy and count-based abundance models (the latter are mostly not appropriate for camera-trapping). Actively being developed and supported by a community of users. Cross-platform (Windows, Mac and Linux).” (Wearn & Glover-Kapfer, 2017)	https://cran.r-project.org/web/packages/unmarked/index.html; https://groups.google.com/d/forum/unmarked,; https://hmecology.github.io/unmarked>	Fiske, I. & Chandler, R. (2011). unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. Journal of Statistical Software, 43(10), 1-23. https://www.jstatsoft.org/v43/i10 ; Kellner, K. F., Smith, A. D., Royle, J. A., Kery, M, Belant, J. L., & Chandler, R. B. (2023). The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology. Methods in Ecology and Evolution, 14 (6), 1408-1415. https://www.jstatsoft.org/v43/i10/
R code/Tutorial	Multi-season Occupancy Models		https://darinjmcneil.weebly.com/multi-season-occupancy.html	McNeil, D. (n.d.). Multi-season Occupancy Models. https://darinjmcneil.weebly.com/multi-season-occupancy.html
R package	Package “detect”	R package for analyzing wildlife data with detection error	psolymos/detect	Solymos, P. (2023). Package ‘detect’: Analyzing Wildlife Data with Detection Error. R package version 0.4-6. https://cran.r-project.org/web/packages/detect/detect.pdf
R code/Tutorial	Occupancy Modeling	Easy to follow explanation of occupancy models with accompanying tutorial and R code.	https://kevintshoemaker.github.io/NRES-746/Occupancy.html
Tutorial	occupancyTuts: Occupancy modelling tutorials with RPresence	Occupancy modelling tutorials with RPresence	https://doi.org/10.1111/2041-210X.14285	Donovan, T., Hines, J., & MacKenzie, D. (2024). OCCUPANCYTUTS: Occupancy modelling tutorials with RPRESENCE. Methods in Ecology and Evolution, 15(3), 477-483. https://doi.org/10.1111/2041-210X.14285
R code/Tutorial	Implicit dynamics occupancy models in R	Implicit dynamics occupancy models with the R package RPresence. These models estimate occupancy probability when it changes through time without estimating colonization and extinction parameters. The code and sample data from this tutorial are available on GitHub; jamesepaterson/occupancyworkshop.	https://jamesepaterson.github.io/jamespatersonblog/2024-06-02_implicitdynamicsoccupancy.html	Paterson, J. (2024). Implicit dynamics occupancy models in R. https://jamesepaterson.github.io/jamespatersonblog/2024-06-02_implicitdynamicsoccupancy.html
Tutorial	Using the mgcvmgcv package to create a generalized additive occupancy model in R		https://masonfidino.com/generalized_additive_occupancy_model	Fidino, M. (2021F) Using the mgcvmgcv package to create a generalized additive occupancy model in R. https://masonfidino.com/generalized_additive_occupancy_model
R Shiny app	Bias in single-season occupancy models	“Compute the relative bias (in %) in the maximum-likelihood estimator of the occupancy probability ψ in a single-season (aka static) occupancy model with constant parameters fitted with the package ‘unmarked’.”	Repo: oliviergimenez/bias_occupancy_flexdashboard App: https://ecologicalstatistics.shinyapps.io/bias_occupancy	Gimenez, O. (2020a). Bias in single-season occupancy models. https://ecologicalstatistics.shinyapps.io/bias_occupancy;
R code	Bias in occupancy estimate for a static model	“R code to calculate bias in occupancy estimate as a function of the detection probability given various levels of occupancy probability, various number of sites and surveys.”	oliviergimenez/bias_occupancy	Gimenez, O. (2020b). Bias in occupancy estimate for a static model. https://github.com/oliviergimenez/bias_occupancy
R code/ Presentation	Species Distribution Modelling	‘Vernon Visser provided a brief introduction to SDMs. Below you can replace the lecture slides and R script from this seminar. Provided in these materials is: - A step-by-step guide to running your own SDM - Suggestions for best practices - References that can help provide more detail on the methods -An R script that is annotated to make its understanding and adaptability easier’	https://science.uct.ac.za/seec/stats-toolbox-seminars-spatial-and-species-distribution-toolboxes/species-distribution-modelling

References

Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J. T., Bayne, E., Boutin, S., & Stephens, P. (2015). Camera trap Trapping: A Review and Recommendations for Linking Surveys to Ecological Processes. Journal of Applied Ecology, 52(3), 675-685. https://doi.org/10.1111/1365-2664.12432

Byrne, M. & Golden, J. (2021). Occupancy Modeling. https://kevintshoemaker.github.io/NRES-746/Occupancy.html

Chatterjee, N., Schuttler, T. G., Nigam, P., & Habib, B. (2021). Deciphering the rarity-detectability continuum: optimizing Survey design for terrestrial mammalian community. Ecosphere 12(9), e03748. https://doi.org/10.1002/ecs2.3748

Clarke, J., Bohm, H., Burton, C., Constantinou, A. (2023). Using Camera Traps to Estimate Medium and Large Mammal Density: Comparison of Methods and Recommendations for Wildlife Managers. https://doi.org/10.13140/RG.2.2.18364.72320

Cove, M. (2020a, Sep 27). Occupancy Modeling Video 1 -- Sampling Techniques for Mammals. [Video]. YouTube. https://www.youtube.com/watch?v=n21Ugw0lYcY

Cove, M. (2020b, Sep 27). Occupancy Modeling Video 2 -- Introductory Statistical Review. [Video]. YouTube. https://www.youtube.com/watch?v=u--F8_oRpVU&t=1s

Cove, M. (2020c, Sep 27). Occupancy Modeling Video 3 -- What are Occupancy Models and What are the Applications? [Video]. YouTube. https://www.youtube.com/watch?v=-F-txltI_iA

Cove, M. (2020d, Sep 28). Occupancy Modeling Video 4 -- How to Run and Interpret the Models in PRESENCE [Video]. YouTube. https://www.youtube.com/watch?v=DVo4KVMPnWg

Donovan, T., Hines, J., & MacKenzie, D. (2024). OCCUPANCYTUTS: Occupancy modelling tutorials with RPRESENCE. Methods in Ecology and Evolution, 15(3), 477-483. https://doi.org/10.1111/2041-210X.14285

Efford, M. G., & Dawson, D. K. (2012). Occupancy in continuous habitat. Ecosphere, 3(4). Article 32. https://doi.org/10.1890/es11-00308.1

Fidino, M. (2021d) So, you don't have enough data to fit a dynamic occupancy model? An introduction to auto-logistic occupancy models. https://masonfidino.com/autologistic_occupancy_model

Fidino, M. (2021a) multi-state-occupancy-models. https://github.com/mfidino/integrated-occupancy-model

Fidino, M. (2021b) A gentle introduction to an integrated occupancy model that combines presence-only and detection/non-detection data, and how to fit it in JAGS https://masonfidino.com/bayesian_integrated_model

Fidino, M. (2021c) integrated-occupancy-models https://github.com/mfidino/integrated-occupancy-model

Fidino, M. (2021e) auto-logistic-occupancy. https://github.com/mfidino/auto-logistic-occupancy

Fidino, M. (2021F) Using the mgcvmgcv package to create a generalized additive occupancy model in R. https://masonfidino.com/generalized_additive_occupancy_model

Fidino, M. (2023) autoOcc: An R package for fitting autologistic occupancy models. R package version 0.1.1, https://github.com/mfidino/autoOcc

Fidino, M., & Magle, S. B. (2017). Using Fourier series to predict periodic patterns in dynamic occupancy models. Ecosphere,8(9) , e01944. https://doi.org/10.1002/ecs2.1944

Kellner, K. F., Smith, A. D., Royle, J. A., Kery, M, Belant, J. L., & Chandler, R. B. (2023). The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology. Methods in Ecology and Evolution, 14 (6), 1408-1415. https://www.jstatsoft.org/v43/i10/

Gaston, K. J., Blackburn, T. M., Greenwood, J. J. D., Gregory, R. D., Quinn, R. M., & Lawton, J. H. (2000). Abundance-Occupancy Relationships. The Journal of Applied Ecology, 37(s1), 39-59. https://doi.org/10.1046/j.1365-2664.2000.00485.x

Gimenez, O. (2020a). Bias in single-season occupancy models. https://ecologicalstatistics.shinyapps.io/bias_occupancy;

Gimenez, O. (2020b). Bias in occupancy estimate for a static model. https://github.com/oliviergimenez/bias_occupancy

Gimenez, O. (2023, May 16). Workshop on estimating (wolf) occupancy with R [Video]. YouTube. https://www.youtube.com/watch?v=rpjVrFI_dr8

Guillera‐Arroita, G. (2017). Modelling of species distributions, range dynamics and communities under imperfect detection: Advances, challenges and opportunities. Ecography, 40(2), 281-295. https://doi.org/10.1111/ecog.02445

Hines, J. E. (2006). PRESENCE - Software to estimate patch occupancy and related parameters. https://www.mbr-pwrc.usgs.gov/software/presence.html.

Fiske, I. & Chandler, R. (2011). unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. Journal of Statistical Software, 43(10), 1-23. https://www.jstatsoft.org/v43/i10

MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Bailey, L. L., & Hines, J. E. (2017). Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. 2nd ed. Academic Press, San Diego. https://www.sciencedirect.com/book/9780124071971/occupancy-estimation-and-modeling.

McNeil, D. (n.d.). Multi-season Occupancy Models. https://darinjmcneil.weebly.com/multi-season-occupancy.html

Murray, M. H., Fidino, M., Lehrer, E. W., Simonis, J. L., & Magle, S. B. (2021). A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality. Journal of Animal Ecology, 90(8), 1973-1984. https://doi.org/10.1111/1365-2656.13515

Neilson, E. W., Avgar, T., Burton, A. C., Broadley, K., & Boutin, S. (2018). Animal movement affects interpretation of occupancy models from camera‐trap Surveys of unmarked animals. Ecosphere, 9(1). https://doi.org/10.1002/ecs2.2092

Noon, B. R., Bailey, L. L., Sisk, T. D., & McKelvey, K. S. (2012). Efficient Species-Level Monitoring at the Landscape Scale. Conservation Biology, 26(3), 432-41. https://doi.org/10.1111/j.1523-1739.2012.01855.x.

Paterson, J. (2024). Implicit dynamics occupancy models in R. https://jamesepaterson.github.io/jamespatersonblog/2024-06-02_implicitdynamicsoccupancy.html

Proteus (2018, Mar 19). Occupancy modelling - more than species presence/absence! [Video]. YouTube. https://www.youtube.com/watch?v=Sp4kb4_TiBA&t=2s

Proteus. (2019a, May 30). Occupancy modelling - the difference between probability and proportion of units occupied [Video]. YouTube. https://www.youtube.com/watch?v=zKQFY8W4ceU

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