Bibliography

Here is a list of extra reading that may be interesting and provide further insight into particular topics. This is by no means a complete bibliography and we welcome any additions!

Also potentially useful is the distance sampling bibliography maintained by Tiago Marques, Eric Rexstad and David L Miller.

The below is organised by lecture/practical session. There is some duplication so that one can dip in and out without missing things.

Overall

Cannonical books and papers:

  • Distance sampling:
    • Buckland, S. T., Anderson, D. R., Burnham, K. P., Borchers, D. L., & Thomas, L. (2001). Introduction to Distance Sampling. Oxford University Press, Oxford, UK.
    • Buckland, S. T., Rexstad, E. A., Marques, T. A., & Oedekoven, C. S. (2015). Distance Sampling: Methods and Applications. Springer International Publishing.
      • Available for around £20 if your library has “Springer Link” access, look for “MyCopy”.
  • Density surface models
    • Hedley, S. L., & Buckland, S. T. (2004). Spatial models for line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics, 9(2), 181–199. http://doi.org/10.1198/1085711043578
    • Miller, D. L., Burt, M. L., Rexstad, E. A., & Thomas, L. (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4(11), 1001–1010. http://doi.org/10.1111/2041-210X.12105
  • Generalized additive models
    • Wood, S. (2006, 1st edition; 2017, 2nd edition). Generalized Additive Models. CRC Press.
    • Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric Regression. Cambridge University Press.

R and RStudio

Introductory

Packages

Using GIS/spatial data in R

Landscape

Other methods for abundance estimation

The sperm whale data

Estimation of sperm whale abundance in the North Atlantic by NOAA:

Introduction to distance sampling

Field methods, survey design etc

Detection function formulations

  • “Classic” paper on adjustment terms:
    • Buckland, S. T. (1992). Fitting Density Functions with Polynomials. Applied Statistics, 41(1), 63. http://doi.org/10.2307/2347618
  • Dealing with monotonicity by constructing the right model:
    • Miller, D. L., & Thomas, L. (2015). Mixture models for distance sampling detection functions. PLoS ONE. http://doi.org/10.6084/m9.figshare.1293041
  • Two papers on detection functions for when the detection function’s apex is away from zero:
    • Becker, E. F., & Quang, P. X. (2009). A gamma-shaped detection function for line-transect surveys with mark-recapture and covariate data. Journal of Agricultural, Biological, and Environmental Statistics, 14(2), 207–223. http://doi.org/10.1198/jabes.2009.0013
    • Becker, E. F., & Christ, A. M. (2015). A Unimodal Model for Double Observer Distance Sampling Surveys. PLoS ONE, 10(8), e0136403–18. http://doi.org/10.1371/journal.pone.0136403

Other stuff

  • Dealing with measurement error:
    • Marques, T. A. (2004). Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models. Biometrics, 60(3), 757–763. http://doi.org/10.1111/j.0006-341X.2004.00226.x
  • Movement in distance sampling:
    • Glennie, R., Buckland, S. T., & Thomas, L. (2015). The Effect of Animal Movement on Line Transect Estimates of Abundance. PLoS ONE, 10(3), e0121333–15. http://doi.org/10.1371/journal.pone.0121333
  • Overlapping transects
    • Buckland, ST 2006, ‘Point transect surveys for songbirds: robust methodologies’ The Auk, vol. 123, no. 2, pp. 345-357. https://doi.org/10.1642/0004-8038(2006)123[345:PSFSRM]2.0.CO;2
  • Camera traps
    • Howe, E. J., Buckland, S. T., Després‐Einspenner, M. and Kühl, H. S. (2017), Distance sampling with camera traps. Methods Ecol Evol, 8: 1558-1565. doi:10.1111/2041-210X.12790

Advanced distance sampling

  • Covariates in the detection function
    • Marques, T. A., Thomas, L., Fancy, S. G., & Buckland, S. T. (2007). Improving estimates of bird density using multiple-covariate distance sampling. The Auk, 124(4), 1229. http://doi.org/http://dx.doi.org/10.1642/0004-8038(2007)124[1229:IEOBDU]2.0.CO;2
  • Covariates with indirect surveys (ants):
    • Borkin, K. M., Summers, R. W., & Thomas, L. (2012). Surveying abundance and stand type associations of Formica aquilonia and F. lugubris(Hymenoptera: Formicidae) nest mounds over an extensive area: trialing a novel method. European Journal of …, 109(1), 47–53. http://doi.org/10.14411/eje.2012.007
  • Goodness of fit testing for detection functions
    • Chapter 11, section 11 of Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2004). Advanced Distance Sampling. Oxford University Press, Oxford, UK.

Abundance estimation

  • Classic text on sampling theory, Horvitz-Thompson estimators
    • Thompson, S. K. (2002). Sampling (2nd ed.). Wiley.
  • Other example analyses

Uncertainty estimation

  • Definitive reference on calculating encounter rate variance
    • Fewster, R. M., Buckland, S. T., Burnham, K. P., Borchers, D. L., Jupp, P. E., Laake, J. L., & Thomas, L. (2009). Estimating the Encounter Rate Variance in Distance Sampling. Biometrics, 65(1), 225–236. http://doi.org/10.1111/j.1541-0420.2008.01018.x
  • How can we just add the squared CVs?
    • Goodman, L. A. (1960). On the Exact Variance of Products. Journal of the American Statistical Association, 55(292), 708. http://doi.org/10.2307/2281592
    • Seber, G. A. F. (1982). The Estimation of Animal Abundance and Related Parameters. Macmillan.
  • Obtaining uncertainty estimates from functions of MLEs
    • Borchers, D. L., Buckland, S. T., & Zucchini, W. (2002). Estimating Animal Abundance: Closed populations. Springer. (Appendix C)

What is a DSM?

  • Paper that proposes DSM methodology
    • Hedley, S. L., & Buckland, S. T. (2004). Spatial models for line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics, 9(2), 181–199. http://doi.org/10.1198/1085711043578
  • Update to that paper ~10 years on (open access)
    • Miller, D. L., Burt, M. L., Rexstad, E. A., & Thomas, L. (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4(11), 1001–1010. http://doi.org/10.1111/2041-210X.12105
  • Perception bias modelling (mark-recapture distance sampling)
    • Burt, M. L., Borchers, D. L., Jenkins, K. J., & Marques, T. A. (2014). Using mark-recapture distance sampling methods on line transect surveys. Methods in Ecology and Evolution, 5(11), 1180–1191. http://doi.org/10.1111/2041-210X.12294
  • Availability by simple correction
    • Winiarski, K. J., Burt, M. L., Rexstad, E., Miller, D. L., Trocki, C. L., Paton, P. W. C., & McWilliams, S. R. (2014). Integrating aerial and ship surveys of marine birds into a combined density surface model: A case study of wintering Common Loons. The Condor, 116(2), 149–161. http://doi.org/10.1650/CONDOR-13-085.1

DSM applications

  • Harihar, A., Pandav, B., & MacMillan, D. C. (2014). Identifying realistic recovery targets and conservation actions for tigers in a human-dominated landscape using spatially explicit densities of wild prey and their determinants. Diversity and Distributions, 20(5), 567–578. http://doi.org/10.1111/ddi.12174
  • Winiarski, K. J., Miller, D. L., Paton, P. W. C., & McWilliams, S. R. (2014). A spatial conservation prioritization approach for protecting marine birds given proposed offshore wind energy development. Biological Conservation, 169(C), 79–88. http://doi.org/10.1016/j.biocon.2013.11.004
  • Winiarski, K. J., Burt, M. L., Rexstad, E., Miller, D. L., Trocki, C. L., Paton, P. W. C., & McWilliams, S. R. (2014). Integrating aerial and ship surveys of marine birds into a combined density surface model: A case study of wintering Common Loons. The Condor, 116(2), 149–161. http://doi.org/10.1650/CONDOR-13-085.1
  • Winiarski, K. J., Miller, D. L., Paton, P., & McWilliams, S. R. (2013). Spatially explicit model of wintering common loons: conservation implications. Marine Ecology Progress Series, 492, 273–283. http://doi.org/10.3354/meps10492
  • Paxton, C. G., Burt, M. L., Hedley, S. L., Víkingsson, G. A., Gunnlaugsson, T., & Desportes, G. (2013). Density surface fitting to estimate the abundance of humpback whales based on the NASS-95 and NASS- 2001 aerial and shipboard surveys. NAMMCO Scientific Publications, 7(0), 143. http://doi.org/10.7557/3.2711
  • Hedley, S. L., & Bravington, M. V. (2010). Antarctic minke whale abundance from the SPLINTR model: some ‘reference’ dataset results and “preferred” estimates from the second and third circumpolar IDCR … (No. SC/61/IA14). International Whaling Commission.
  • Bravington, M. V., & Hedley, S. L. (2009). Antarctic minke whale abundance estimates from the second and third circumpolar IDCR/SOWER surveys using the SPLINTR model (No. SC/61/IA14) (pp. 1–25). International Whaling Commission.
  • Katsanevakis, S. (2007). Density surface modeling with line transect sampling as a tool for abundance estimation of marine benthic species: The Pinna nobilis example in a marine lake. Marine Biology 152:77–85.
  • Becker, E. A., Forney, K. A., Ferguson, M. C., Foley, D. G., Smith, R. C., Barlow, J., & Redfern, J. V. (2010). Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data. Marine Ecology Progress Series, 413, 163–183. http://doi.org/10.3354/meps08696
  • Williams, R., Hedley, S. L., Branch, T. A., Bravington, M. V., Zerbini, A. N., & Findlay, K. P. (2011). Chilean blue whales as a case study to illustrate methods to estimate abundance and evaluate conservation status of rare species. Conservation Biology, 25(3), 526–535. http://doi.org/10.1111/j.1523-1739.2011.01656.x
  • Redfern, J. V., Barlow, J., Ballance, L. T., Gerrodette, T., & Becker, E. A. (2008). Absence of scale dependence in dolphin–habitat models for the eastern tropical Pacific Ocean. Marine Ecology Progress Series, 363, 1–14. http://doi.org/10.3354/meps07495

Generalized additive models

Cannonical reference is Wood (2006)

Response distributions

  • Papers about using the Tweedie distribution
    • Shono, H. (2008). Application of the Tweedie distribution to zero-catch data in CPUE analysis. Fisheries Research, 93(1-2), 154–162. http://doi.org/10.1016/j.fishres.2008.03.006
    • Foster, S. D., & Bravington, M. V. (2012). A Poisson–Gamma model for analysis of ecological non-negative continuous data. Environmental and Ecological Statistics, 20(4), 533–552. http://doi.org/10.1007/s10651-012-0233-0
  • Comparison of negative binomial and quasi-Poisson
    • Ver Hoef, J. M., & Boveng, P. L. (2007). Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology, 88(11), 2766–2772. http://doi.org/10.2307/25590942

Smooths

  • Section 4.1 of Wood (2006)
  • Within mgcv the ?smooth.terms manual page lists all spline bases available in mgcv (and therefore dsm)
  • Figure of the thin plate spline basis functions adapted from Figure 4.6 of Wood (2006).

Selecting basis size

  • Practical advice in mgcv ?choose.k manual page

Checking and validation

  • Paper on randomised quantile residuals
    • Dunn, P. K., & Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5(3), 236–244. http://doi.org/10.1080/10618600.1996.10474708
  • Spatial model checking plots using deviance residuals (preprint)
    • Marra, G., Miller, D. L., & Zanin, L. (2011). Modelling the spatiotemporal distribution of the incidence of resident foreign population. Statistica Neerlandica, 66(2), 133–160. http://doi.org/10.1111/j.1467-9574.2011.00500.x
  • Validating spatial models

Covariates

  • Using in situ vs. remotely sensed covariates
    • Becker, E. A., Forney, K. A., Ferguson, M. C., Foley, D. G., Smith, R. C., Barlow, J., & Redfern, J. V. (2010). Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data. Marine Ecology Progress Series, 413, 163–183. http://doi.org/10.3354/meps08696
  • Can we trust covariates from GIS?
    • Foster, S. D., Shimadzu, H., & Darnell, R. (2012). Uncertainty in spatially predicted covariates: is it ignorable? Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(4), 637–652. http://doi.org/10.1111/j.1467-9876.2011.01030.x
  • Information on the weird Pathfinder “false islands” problem (scroll down to false islands image)

Multiple smooths and model selection

  • That great quote from Tobler is from:
    • Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234-240.
  • Paper about shrinkage selection in GAMs
    • Marra, G., & Wood, S. N. (2011). Practical variable selection for generalized additive models. Computational Statistics and Data Analysis, 55(7), 2372–2387. http://doi.org/10.1016/j.csda.2011.02.004
  • Approximate $p$-values:
    • Marra, G., & Wood, S. N. (2012). Coverage properties of confidence intervals for generalized additive model components. Scandinavian Journal of Statistics, 39(1), 53–74. http://doi.org/10.1111/j.1467-9469.2011.00760.x
  • Explanation of deviance for GLMs
    • Wood (2006) p. 70
  • REML for smoothness selection
    • Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), 3–36. http://doi.org/10.1111/j.1467-9868.2010.00749.x
    • Reiss, P. T., & Ogden, R. T. (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), 505–523.
  • Example sensitivity analysis for DSMs, appendix of this paper (preprint available on DLM’s website
    • Winiarski, K. J., Burt, M. L., Rexstad, E., Miller, D. L., Trocki, C. L., Paton, P. W. C., & McWilliams, S. R. (2014). Integrating aerial and ship surveys of marine birds into a combined density surface model: A case study of wintering Common Loons. The Condor, 116(2), 149–161. http://doi.org/10.1650/CONDOR-13-085.1

Variance

  • How do we calculate $\mathbf{V}_\boldsymbol{\beta}$ (variance of the GAM parameters)?
    • Section 4.8 (“Distributional results”) of Wood (2006)
  • Propagating variance from the detection function
    • Williams, R., Hedley, S. L., Branch, T. A., Bravington, M. V., Zerbini, A. N., & Findlay, K. P. (2011). Chilean blue whales as a case study to illustrate methods to estimate abundance and evaluate conservation status of rare species. Conservation Biology, 25(3), 526–535. http://doi.org/10.1111/j.1523-1739.2011.01656.x
    • Appendix B of Miller et al (2013) available on DLM’s website (more technical)
    • Bravington, M. V., Miller, D. L. and Hedley, S. L. Reliable variance propagation for spatial density surface models available on arXiv
  • (Re-iterating from above) How can we just add the squared CVs?
    • Goodman, L. A. (1960). On the exact variance of products. Journal of the American Statistical Association, 55(292), 708. http://doi.org/10.2307/2281592
    • Seber, G. A. F. (1982). The Estimation of Animal Abundance and Related Parameters. Macmillan.

Extrapolation

  • Laura Mannocci’s thesis work on extrapolation
    • Mannocci, L., Monestiez, P., Spitz, J., & Ridoux, V. (2015). Extrapolating cetacean densities beyond surveyed regions: habitat-based predictions in the circumtropical belt. Journal of Biogeography, n/a–n/a. http://doi.org/10.1111/jbi.12530
  • Paul Conn and co’s work on using a generalised Cook’s Distance to find places where you shouldn’t extrapolate
    • Conn, P. B., Johnson, D. S., & Boveng, P. L. (2015). On extrapolating past the range of observed data when making statistical predictions in ecology. PLoS ONE, 10(10), e0141416–16. http://doi.org/10.1371/journal.pone.0141416
  • Katherine Yates and Phil Bouchet’s papers on transferrability
    • Outstanding Challenges in the Transferability of Ecological Models. Yates, Katherine L. et al. Trends in Ecology & Evolution https://doi.org/10.1016/j.tree.2018.08.001
    • Sequeira AMM, Bouchet PJ, Yates KL, Mengersen K, Caley MJ. 2018. Transferring biodiversity models for conservation: Opportunities and challenges. Methods in Ecology and Evolution, 9(5): 1250-1264. https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12998

Practical advice

  • Paper looking at segment size and covariate resolution (also paper with a separate group size model)
    • Redfern, J. V., Barlow, J., Ballance, L. T., Gerrodette, T., & Becker, E. A. (2008). Absence of scale dependence in dolphin–habitat models for the eastern tropical Pacific Ocean. Marine Ecology Progress Series, 363, 1–14. http://doi.org/10.3354/meps07495
  • Good text book on prediction-based modelling, lots of information on cross validation (also availble free online)
    • Hastie, T., Tibshirani, R., & Friedman, J. (2013). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer New York.
  • Paper on a mixture model formulation for the detection function, highlighting how sample size is not the only thing that matters
    • Miller, D. L., & Thomas, L. (2015). Mixture models for distance sampling detection functions. PLoS ONE. http://doi.org/10.6084/m9.figshare.1293041

Advanced topics

Smoothers

  • Cyclic smooths
    • Section 4.1.3 of Wood (2006)
  • Duchon splines are covered in
    • Miller, D. L., & Wood, S. N. (2014). Finite area smoothing with generalized distance splines. Environmental and Ecological Statistics, 21(4), 715–731. http://doi.org/10.1007/s10651-014-0277-4
  • Soap film smoothing:
    • Wood, S. N., Bravington, M. V., & Hedley, S. L. (2008). Soap film smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5), 931–955. http://doi.org/10.1111/j.1467-9868.2008.00665.x
    • Marra, G., Miller, D. L., & Zanin, L. (2011). Modelling the spatiotemporal distribution of the incidence of resident foreign population. Statistica Neerlandica, 66(2), 133–160. http://doi.org/10.1111/j.1467-9574.2011.00500.x
    • Manual page ?smooth.construct.so.smooth.spec
    • Check that your soap film boundary and knots work with this code
  • Tensor products
    • Section 4.1.8 of Wood (2006)
    • Marra, Miller and Zanin (2011) also cover using a tensor product of space and time, as do:
    • 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. http://doi.org/10.1002/env.2196
  • Spatial autocorrelation
    • Manual page is gamm()
    • Understanding the possible correlation structures:
      • Pinheiro, J., & Bates, D. (2010). Mixed-Effects Models in S and S-PLUS. Springer.
    • Complex spatio-temporal modelling with tensors and autocorrelation:
      • Augustin, N. H., Musio, M., Wilpert, von, K., Kublin, E., Wood, S. N., & Schumacher, M. (2009). Modeling spatiotemporal forest health monitoring data. Journal of the American Statistical Association, 104(487), 899–911. http://doi.org/10.1198/jasa.2009.ap07058
  • Temporal trends
    • Marra, Miller and Zannin (2011)
    • Augustin et al (2013)
  • Parallel processing for GAMs
    • Manual ?bam
    • Wood, S. N., Goude, Y., & Shaw, S. (2015). Generalized additive models for large data sets. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64(1), 139–155. http://doi.org/10.1111/rssc.12068

Other approaches

  • Comparison of different techniques for spatial modelling
    • 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. http://doi.org/10.1016/j.biocon.2011.11.013
  • Bayesian approaches
    • Niemi, A., & Fernández, C. (2010). Bayesian spatial point process modeling of line transect data. Journal of Agricultural, Biological, and Environmental Statistics, 15(3), 327–345. http://doi.org/10.1007/s13253-010-0024-8
    • Schmidt, J. H., Rattenbury, K. L., Lawler, J. P., & Maccluskie, M. C. (2011). Using distance sampling and hierarchical models to improve estimates of Dall’s sheep abundance. The Journal of Wildlife Management, 76(2), 317–327. http://doi.org/10.1002/jwmg.216
    • Conn, P. B., Laake, J. L., & Johnson, D. S. (2012). A hierarchical modeling framework for multiple observer transect surveys. PLoS ONE. http://doi.org/10.1371/journal.pone.0042294.g001
    • Moore, J. E., & Barlow, J. (2011). Bayesian state-space model of fin whale abundance trends from a 1991-2008 time series of line-transect surveys in the California Current. Journal of Applied Ecology, 48(5), 1195–1205. http://doi.org/10.1111/j.1365-2664.2011.02018.x
  • Other frequentist approaches
    • Johnson, D. S., Laake, J. L., & Ver Hoef, J. M. (2009). A Model-based approach for making ecological inference from distance sampling data. Biometrics, 66(1), 310–318. http://doi.org/10.1111/j.1541-0420.2009.01265.x
    • Ver Hoef, J. M., Cameron, M. F., Boveng, P. L., London, J. M., & Moreland, E. E. (2013). A spatial hierarchical model for abundance of three ice-associated seal species in the eastern Bering Sea. Statistical Methodology, 1–44. http://doi.org/10.1016/j.stamet.2013.03.001

Misc other approaches

  • O’Brien, S. H., Webb, A., Brewer, M. J., & Reid, J. B. (2012). Use of kernel density estimation and maximum curvature to set Marine Protected Area boundaries: Identifying a Special Protection Area for wintering red-throated divers in the UK. Biological Conservation, 156(C), 15–21. http://doi.org/10.1016/j.biocon.2011.12.033
  • Melville, G. J., & Welsh, A. H. (2014). Model-based prediction in ecological surveys including those with incomplete detection. Australian & New Zealand Journal of Statistics, 56(3), 257–281. http://doi.org/10.1111/anzs.12084
  • Beale, C. M., Brewer, M. J., & Lennon, J. J. (2014). A new statistical framework for the quantification of covariate associations with species distributions. Methods in Ecology and Evolution, 5(5), 421–432. http://doi.org/10.1111/2041-210X.12174
  • Kinlan, B.P., Menza, C., Huettmann, F., 2012. Predictive modeling of seabird distribution patterns in the New York Bight. In: Menza, C., Kinlan, B.P., Dorfman, D.S., Poti, M, Caldow, C. (Eds.), A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, Maryland (Chapter 6).