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 $<$ 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). Generalized Additive Models. CRC Press.
  • Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric Regression. Cambridge University Press.

Landscape

Data

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

• Analysis of 2004 surveys, plus a 1998 survey.
• Results from the most recent (2011) survey (currently used for management purposes to set things like PBR, the number of sperm whales that can be killed) and details of that methodology.

Introduction to distance sampling

Field methods, survey design etc

• Buckland et al (2015) chapters 2 & 4
• Strindberg, S., & Buckland, S. T. (2004). Zigzag survey designs in line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics, 9(4), 443–461. http://doi.org/10.1198/108571104X15601
• Thomas, L., & Williams, R. (2007). Designing line transect surveys for complex survey regions. Journal of Cetacean Research and Management 9(1):1–13.
• Camphuysen, C.J., Fox, A.D., Leopold, M., Petersen, I.K., 2004. Towards standardized seabirds at sea census techniques in connection with environmental impact assessments for offshore wind farms. In: UK COWRIE 1 Report. Royal Netherlands Institute for Sea Research, Texel, Netherlands.

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

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
  • Example analysis of minke whales with a stratified survey area by Eric Rexstad

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
• Regarding negative binomial and the “quadratic assumption” in the variance. This is from “Zero-inflated models and generalized linear mixed models with R” by Zuur et al p. 78-79:
  • “We applied a Poisson GLM and GAM on the data and discovered overdispersion; therefore we applied NB GLM and GAM models. These were better, and the NB GAM seems satisfactory but the estimated value of k in the NB variance term is relatively small, meaning that the model is allowing for variance. In other words, the large overdispersion in the Poisson GLM (and GAM) is being captured by the quadratic variance term in the NB distribution. That is like shooting a mosquito with a cannon. It does the job if it kills the mosquito, but do we really need it? Perhaps there is a reason for the overdispersion, and hiding it behind a quadratic variance component may mean that we miss some important information. In the simulation study of chapter 2, we saw that if the data are zero-inflated, which can cause overdispersion, an NB GLM will give biased parameters.”

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

• 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

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

Prediction

• 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

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)
• (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.

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

R and RStudio

Introductory

• R for cats is a good introduction to R
• RStudio cheatsheets

Packages

• Intro to ggplot2
• lubridate a package to make date/time handling easier in R – very useful!

Using GIS/spatial data in R

• Applied Spatial Data Analysis with R the definitive book on spatial data in R, written by the authors of the most popular R spatial packages
• Barry Rowlingson’s R spatial cheatsheet
• James Cheshire’s links on using R as a GIS

Reproducable research

• Using RMarkdown to generate docx Word documents

Advanced topics

• Data analysis using a simple strategy: Wickham, H. (2011). The split-apply-combine strategy for data analysis. Journal of Statistical Software. paper
• More technical guide to R: Advanced R

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).