Books on study design & statistical methods useful for environmental studies
  
Batschelet, E. 1976. Introduction to mathematics for life sciences. Springer-Verlag, New York. (The 
kind of book that should be the text for a biologist's calculus course, but never is.)

Berthouex, P.M., and L.C. Brown. 2002. Statistics for environmental engineers, 2nd edition. CRC Press.  
(An odd collection of topics.  I suggest that you look at the table of contents (e.g. on Amazon).)

Cochran, W.G. 1963. Sampling  techniques, 2nd Ed. Wiley, N.Y. (First edition was 1953.  It's still the 
classic reference on the subject.)

Cochran, W.G. 1983. Planning and analysis of observational studies. Wiley, New York. (A nice short 
treatment of principles of design and statistical analysis - including power analysis - for the kinds 
of studies ecologists most commonly do, i.e. studies that are not designed experiments.)

Crawley, M.J. 1993. GLIM for ecologists. Blackwell, Oxford. (See comments on McCullagh and Nelder 
1983.)

Crowder, M.J., and D.J. Hand. 1990. Analysis of repeated measures. Chapman and Hall, London. (This 
is the bible on the subject.  There are worked examples, discussion of procedures as implemented in 
various statistical packages, and discussion of assumptions and consequences of their violation.)

Draper, N.R., and H. Smith. 1981. Applied regression analysis, 2nd Ed. Wiley, New York.  (The bible on 
regression analysis and modelling, examining residuals, nonlinear models, etc.  Worked examples.)

Edgington, E.S. 1995. Randomization tests, 3rd Ed.  Marcel Dekker, New York. (See comments on Manly 
1991.)

Elliott, J.M. 1977. Some methods for the statistical analysis of samples of benthic invertebrates. FBA 
Sci. Publ. No.25, Windermere, Cumbria, U.K.  (A "best buy".  Good on spatial distributions, sampling 
designs, transformations.)

Gilpin, A. 2006. Environmental impact assessment (EIA). Cambridge. (Soft cover.  Bureaucratic to my taste, 
oriented to describing a government-driven process.  But interesting.)

Green, R.H. 1979. Sampling design and statistical methods for environmental biologists. Wiley, N.Y.  
(A "handbook" with examples, univariate and multivariate approaches, my prejudices, and a large 
topic-coded bibliography - now rather out of date. A new book is in prep, the draft title & outline
of which is similar to this workshop.)

Harris, R.J. 1985. A primer of multivariate statistics, 2nd Ed. Academic Press, N.Y. (A good multivariate 
stats text and reference, but “primer” is misleading. Don't start with it - start with Pielou 1984, 
Pimentel 1978 or Manly 1994.)

Helsel, P.R., and USGS. 2004. Nondetects and data analysis: statistics for censored environmental data. 
Wiley-Interscience, New York.  (This is an important problem and there isn't a lot out there on how to 
deal with it.) 

Hunt, R. 1978. Plant growth analysis. Edward Arnold, London.  (Thin soft cover treatment of quantitative 
models of growth and form - generally applicable - not just to plants.)

Jongman, R.H.G., C.J.F. ter Braak and O.F.R. van Tongeren. 1987. Data analysis in community and landscape 
ecology. Pudoc Wageningen, The Hague.  (The latest crazes in statistical analysis of biological community 
data.  The offspring of a mating between a Dutch school and the Cornell school.  Trendy methods such as 
Canonical Correspondence Analysis plus coverage of traditional methods such as Principal Components 
Analysis and analysis of spatial pattern.  Cajo ter Braak wrote a statistical package called CANOCO that 
does most of it.  If you are interested in analysis of community data, do not begin with this one.  Read 
Pielou 1984 or Manly 1994 first.)

Keough, M.J., and B.D. Mapstone. 1995. Protocols for designing marine ecological monitoring programs 
associated with BEK mills. Report No. 11. CSIRO, Canberra. (It’s a book-size report. Excellent coverage 
of general principles and practice of environmental study design. A non-commercially published 
word-of-mouth gem, like Elliott 1977. If you write to CSIRO in Canberra they’ll probably mail you a 
copy for free. But see Quinn and Keough 2002 for similar, often more extensive, coverage.)

Kirk, R.E. 1982. Experimental design: procedures for the behavioral sciences. Brooks/Cole, Monterey, 
California.  (Another good reference on experimental and observational study design.  His fans,  who 
tend to be fanatics, always ask "But what does Captain Kirk say?"  (apologies to Trekkies).  One of his 
cleverer efforts is to show how to do "pseudo-F tests" by constructing composite error terms.  SAS will 
sometimes do this for complex designs without being asked. See Winer 1971 or Underwood 1997 for a 
more classical treatment of the subject.)

Legendre, L., and P. Legendre. 1983. Numerical ecology. Elsevier, Amsterdam.  (Very thorough 
text/reference. Covers matrix algebra diversity indices, multivariate analyses, time series, matrix 
population models, etc.)

Manly, B.F.J. 1991. Randomization and Monte Carlo methods in biology. Chapman and Hall, London. 
(Randomization tests have largely replaced “nonparametric” tests. This is a good reference to them, 
and Manly’s RT package implements them. Also see Edgington 1995.)

Manly, B.F.J. 1994. Multivariate statistical methods: a primer. Chapman and Hall, London. (Another good 
one is Pielou 1984.)

Mead, R. 1988. Statistical principles for practical applications. Cambridge Univ. Press. (Some say it’s 
the best general reference on principles in study design for applied studies.)

McCullagh, P., and J.A. Nelder. 1983. Generalized linear models. Chapman and Hall, London. (Most texts 
and statistical packages limit "linear models" to ones with normally distributed error, such as ANOVA and 
ordinary least squares regression.  Be sure you're on top of those sorts of linear models first, then 
look at how this book brings ”other-than-normal error distribution” models such  as probit analysis, log-
linear analysis of contingency tables, logistic models, and proportional hazards models for survival data, 
underneath the "linear models" umbrella.  The statistical package GLIM implements these methods.  
GLIM repels many people with its different philosophy and strange syntax, but it is worth knowing how to 
use. See Crawley 1993.)

Pielou, E.C. 1984. The interpretation of ecological data. Wiley, N.Y.  (The subtitle is "A primer on 
classification and ordination".  It's a good introduction to descriptive multivariate statistics 
applied to ecology. There are clear worked examples. Another good one is Manly 1994.)

Pimentel, R.A. 1978. Morphometrics: The multivariate analysis of biological data.  Kendall/Hunt, Dubuque, 
Iowa.  (A good introduction to multivariate statistics. Measurements on painted turtles feature a lot in 
his examples, hence the title. Lots of errata but good value nonetheless.)

Popper, K. 1980. The logic of scientific discovery, 10th Ed. Hutchinson, London. (It is the statement of 
what the philosophy of science is, what the scientific method is. For expression of this philosophy in 
ecological study design and statistical analysis see Underwood 1997. Popper is worth reading and 
understanding because this sense of “what science is” is currently under attack by Bayesian “risk 
analysis” types, e.g. Suter 1993, Suter 1996 Human and Ecological Risk Assessment 2: 331-347, and 
Stewart-Oaten in Schmitt and Osenberg 1996 in Detecting Ecological Impacts: Concepts and Applications in 
Coastal Habitats, Schmitt and Osenberg, eds.,  p. 17-27, Academic, New York. See Dennis 1996 Ecol. Appl. 
6: 1095-1103, for a good response i.e. a critique of Bayesians in ecology.)

Quinn, G.P., and M.J. Keough. 2002. Experimental design and data analysis for biologists. Cambridge. 
(This is probably the best recent book on the subject.  Available in soft cover.) 

Ripley, B.D. 1981. Spatial statistics. Wiley, New York.  (As the preface says, "This is a guide to the 
analysis of spatial data".  Relevant topics are spatial autocorrelation and how to deal with it, and 
testing hypotheses about spatial patterns of organisms.  See also chapter 7 of Jongman et al. 1987.)

Schmitt, R.J., and C.W. Osenberg. 1996. Detecting ecological impacts caused by human activities. Academic 
Press, New York. (It’s a mixed bag, with chapters by various people. But the people are mostly very good
 - e.g. Stewart-Oaten, Underwood, Keough, Jones & Kaly, Mapstone, Kingsford - and the topics are mostly 
very current and important. I totally disagree with some of it, but that’s OK - some of the chapter 
authors disagree with other chapter authors within the book.)  

Schneider, D.C. 1994. Quantitative ecology: spatial and temporal scaling. Academic Press, San Diego. 
(Nice coverage of how temporal and spatial scales of observations, and of what is being observed, 
influences results and interpretations of studies.)

Seber, G.A.F. 1984. Multivariate observations. Wiley, New York.  (A "bible" for multivariate 
statistics, including the math theory and algorithms that underlie it.)

Shaw, P.J.A. 2003. Multivariate statistics for the environmental sciences. Hodder Arnold.  (I have only 
had a chance to glance through this book.  The coverage appears to be good.) 

Snedecor, G.W., and W.G. Cochran. 1989. Statistical methods, 8th ed.  Iowa State Univ. Press, Ames, Iowa.  
(Perhaps the best biologically oriented statistics text.  See Zar 1996 for a more introductory level text.)

Sokal, R.R., and F.J. Rohlf. 1995. Biometry: the principles and practice of statistics in biological research, 
3rd Edition. (A good 2nd level text.  Their smaller "Biostatistics" is introductory.  Topics this one is 
good on include: problems with derived ratio variables, and rules for pooling error terms in Model II 
(nested) ANOVA, and ANOVA designs in general.  I think this book is better as a reference than as a text for 
learning statistics.) 

Suter, G. W. 1996. Ecological risk assessment, 2nd Edition. Lewis, Boca Raton, Florida. (See also Suter 
1996 Human and Ecological Risk Assessment 2: 331-347. He advocates a Bayesian-style “risk analysis”approach to 
environmental management decision-making, as opposed to the traditional Popperian scientific method 
approach. Also see Stewart-Oaten in Schmitt and Osenberg 1996 in Detecting Ecological Impacts: Concepts 
and Applications in Coastal Habitats, Schmitt and Osenberg, eds.,  p. 17-27, Academic, New York. I’m not a 
fan, but it’s currently fashionable scientifically and politically. For an antidote see Popper 1980, 
Underwood 1997, and Dennis 1996 Ecol. Appl. 6: 1095-1103. The last is a short and easy read.) 

Underwood, A.J. 1997. Experiments in ecology: their logical design and interpretation using 
analysis of variance. Cambridge Univ. Press. (Finally we have the book which brings together all the 
ecological study design and statistics principles scattered throughout Underwood’s papers. Available in 
soft cover.  You should seriously consider buying it.)

Wald, A. 1947. Sequential analysis. Wiley, New York  (A not-well-known and under-used sampling/analysis 
design. Dover soft-cover edition 1973.  See Anderson and Thompson 2004 paper for a multivariate version.) 
    
Winer, B.J. 1971. Statistical principles in experimental design, 2nd Ed. McGraw-Hill, New York.  (One of 
the classics on experimental design, from a social sciences perspective but that doesn’t matter very much.)

Zar, J.H. 1996. Biostatistical analysis, 3rd Ed. Prentice-Hall, Englewood Cliffs, N.J.  (Introductory 
biostatistics, and a well-done job.  Besides a good introduction to all the obvious things, there is 
also power analysis, circular distribution statistics (e.g., times of day, stages of the tide, 
directions of animal movement and orientation, etc.), nested ANOVA, and other gems.)







































Papers on study design & statistical methods useful for environmental studies 
   (some have comments, some don’t)

Anderson, M.J., R.B. Millar, W.M. Blom, and C.E. Diebel. 2005. Nonlinear multivariate models of successional 
change in community structure using the von Bertalanffy curve. Oecologia 146: 279-286.  (It is worth checking out 
Marti Jane Anderson's website at www.stat.auckland.ac.nz/~mja/ for publications and computer programs.)

Anderson, M.J., and A.A. Thompson. 2004. Multivariate control charts for ecological and environmental monitoring. 
Ecological Applications 14: 1921-1935. ( Control charts are a version of sequential analysis.  See Wald's 1947 
book.) 
  
Anderson, M. J., and J. Robinson. 2003. Generalized discriminant analysis based on distances. Australian & New 
Zealand Journal of Statistics 45: 301-318.  

Anderson, M.J. and T.J. Willis. 2003. Canonical analysis of principal coordinates: a useful method of constrained 
ordination for ecology. Ecology 84: 511-525.  

Anderson, M.J. 2001. Permutation tests for univariate or multivariate analysis of variance and regression. 
Canadian Journal of Fisheries and Aquatic Sciences 58: 629-636.  

Anderson, M.J. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26: 32-46. 

Chapman, P.M. 1996. Presentation and interpretation of Sediment Quality Triad data. Ecotoxicology 5:327-339.
(Chapman's updating of the SQT in response to other papers on it.)

Clarke, K. R. and R. H. Green. 1988. Statistical design and analysis for a 'biological effects' study. Mar. 
Ecol. Prog. Ser. 46: 213-226.

Clarke, K.R., P.J. Somerfield, L. Airoldi, R.M. Warwick. 2006. Exploring interactions by second-stage community 
analyses. J. Exp. Mar. Biol. Ecol. 338: 179-192. (Anything Bob Clarke publishes is worth taking seriously.  
This addresses the problems of interpreting interactions in factorial ANOVA designs, of which the BACI design is 
one.) 

Dennis, B. 1996. Discussion: Should ecologists become Bayesians? Ecol. Appl. 6: 1095-1103.

Douglas, M. E., and J. A. Endler. 1982. Quantitative matrix comparisons in ecological and evolutionary investigations.
J. Theor. Biol. 99: 777-795. (A great example (Trinidad stream fish) of using matrix descriptions of biological
response and various classes of predictor variables and then applying Mantel's procedure to such data. The appendix
is the worked example.   

Eberhardt, L. L. 1976. Quantitative ecology and impact assessment. J. Envir. Man. 4: 27-70. (A classic that 
should be more famous than it is. Many current issues were considered in this paper two and a half decades 
ago.  I didn’t know this paper when I wrote my 1979 book.) 

Fairweather, P. G. 1991. Statistical power and design requirements for environmental monitoring. Austr. J. 
Mar. Freshw. Res. 42: 555-567.

Graham, M.H. 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84: 2809-2815.
(The problem when observational data are used to predict biological responses from values of environmental 
variables, which are inevitably correlated. )

Gray, J.S., P. Dayton, S. Thrush, and M.J. Kaiser. 2006. On effects of trawling, benthos and sampling design. 
Mar. Pollut. Bull. 52: 840-843. (These are good authors.  The paper is on bottom trawling done 
experimentally, demonstrated effects on the benthic community, and misinterpretation of results.)
 
Green, R. H. 1984. Statistical and nonstatistical considerations for environmental monitoring studies. 
Environ. Monit. Assessm. 4: 293-301.

Green, R. H. 1989. Power analysis and practical strategies for environmental monitoring. Environm. Res. 50: 
195-205.

Green, R. H. 1993. Application of repeated measures designs in environmental impact and monitoring studies. 
Austr. J. Ecol. 18: 81-98.

Green, R. H. 1994. Aspects of power analysis in environmental monitoring. In “Statistics in Ecology and 
Environmental Monitoring”, D. J. Fletcher and B. F. J. Manly, eds., p. 173-182, Otago Conference Series. 
University of Otago Press, Otago, New Zealand.

Green, R.H., J.M. Boyd, and J.S. Macdonald.  1993.  Relating sets of variables in environmental studies: 
the Sediment Quality Triad as a paradigm.  Environmetrics 4:439-457.  (The data analyzed here are from 
Vancouver Harbour.)

Green, R.H., and P. A. Montagna. 1996. Implications for monitoring: study designs and interpretation 
of results. Can. J. Fish. Aquat. Sci. 53:2629-2636. (In the GOOMEX 8-paper set. Three applications to 
GOOMEX data: sediment quality triad, increased error variance as result of impact, future study 
design recommendations.)

Green, R.H. and S. R. Smith. 1997. Sample program design and environmental impact assessment on coral 
reefs. Proc. 8th Int. Coral Reef Symp. 2: 1459-1464. (Repeated measures design for coral reefs, with 
examples.)

Green, R. H., and R. C. Young. 1993. Sampling to detect rare species. Ecol. Appl. 3: 351-356. (This 
resulted from a request to help with a contract from the U.S. Office of Endangered Species, re. how 
to sample when trying to find such a species in a habitat.  The answer turned out to be simple and 
elegant. The database is unionid molluscs in Tennessee and Virginia rivers.  I have had lots of reprint 
requests for this one.)

Hernandez-Arana, H.A., A.A. Rowden, M.J. Attrill, R.M. Warwick, and G. Gold-Bouchot. 2003. Large-scale 
environmental influences on the benthic macrofauna of the southern Gulf of Mexico. Estuar. Coast. Shelf 
Sci. 58: 825-841.  (Here is the Plymouth Lab (formerly IMER) group doing their usual good work, in 
fact doing about as well as can be done with observational field data only.)

Hewitt, J.E., M.J. Anderson, and S.F. Thrush. 2005. Assessing and monitoring ecological community health 
in marine systems. Ecological Applications 15: 942-953.

Jones, G.P. and Kaly, U.L. 1996. Criteria for selecting marine organisms in biomonitoring studies. 
In: “Detecting Ecological Impacts: Concepts and Applications in Coastal Habitats”, R. J. Schmitt and C. W. 
Osenberg, eds., p. 29-48. Academic, New York.

Kennicutt, M.C., II, R.H. Green, P. Montagna and P.F. Rosigno.  1996.  Gulf of Mexico Offshore Operations 
Monitoring Experiment (GOOMEX), Phase I: sublethal responses to contaminant exposure - introduction 
and review.  Can. J. Fish. Aquat. Sci. 53:2540-2553.  (See comments on Peterson et al 1996, below.  This 
one is the introductory paper of the 8-paper set.  It presents the overall study design and planned 
statistical analysis, which was my responsibility in the project.)

Lincoln-Smith, M.P., K.A. Pitt, J.D. Bell, and B.D. Mapstone. 2006. Using impact assessment methods to 
determine the effects of a marine reserve on abundances and sizes of valuable tropical invertebrates. Can. 
J. Fish. Aquat. Sci. 63: 1251-1266.  (An example of a "beyond-BACI design, with different spatial 
scales.)

Mapstone, B. D. 1995. Scalable decision rules for environmental impact studies: effect size, Type I, and 
Type II errors. Ecol. Appl. 5: 401-410.

McDonald, L. L. and W. P. Erickson. 1994. Testing for bioequivalence in field studies: Has a disturbed 
site been adequately reclaimed? In “Statistics in Ecology and Environmental Monitoring”, D. J. Fletcher 
and B. F. J. Manly, eds., p. 183-197, Otago Conference Series. University of Otago Press, Otago, New 
Zealand.

Olsgard, F., P. J. Somerfield, and M. R. Carr. 1997. Relationships between taxonomic resolution and 
data transformations in analyses of a macrobenthic community along a established pollution gradient. Mar. 
Ecol. Prog. Ser.: 1-9. (Patterns of macrobenthic data in vicinity of North Sea oilfield show high degree 
of consistency up to taxonomic level of order.)

Peterman, R. M. 1990. Statistical power analysis can improve fisheries research and management. Can. J. 
Fish. Aquat. Sci. 47: 2-15.

Peterson, C. H. 1993. Improvement of environmental impact analysis by application of principles derived 
from manipulative ecology: Lessons from coastal marine case studies. Austr. J. Ecol. 18: 21-52.

Peterson, C.H., M.C. Kennicutt, II, R.H. Green, P. Montagna, D.E. Harper, Jr., E.N. Powell, and P.F. 
Rosigno. 1996. Ecological consequences of environmental perturbations associated with offshore hydrocarbon 
production:  a perspective on long-term exposures in the Gulf of Mexico.  Can. J. Fish. Aquat. Sci. 53:
2637-2654.  (This is the “summary of results & implications” paper, the 8th of an 8-paper set reporting 
the results of a 2-3 year study by a half-dozen principal investigators.  This set of papers has had a 
major impact, and its conclusions are somewhat controversial -- for example that there is no apparent 
biological impact beyond a few hundred meters from drilling platforms.)

Peterson, C.H., L.L. Macdonald, R.H. Green, and W.P. Erickson.  2001.  Sampling design begets conclusions: 
the statistical basis for detection of injury to and recovery of shoreline communities after the Exxon 
Valdez oil spill.  Marine Ecology Progress Series 210: 255-283. (This is a major review paper.  Its main 
theme is that the Exxon-funded study design whatever the intentions were and whatever their design’s other 
virtues, was bound to have low power to detect impacts by the oilspill on the biological community.  And it 
did, in comparison with the government-funded & supervised studies.  Mind you, I was Chair of the 
Statistical Working Group for the latter and Peterson was Chief Scientist, so we can’t claim to be 
disinterested.)

Pinedo, S., M. Garcia, M.P. Satta, M. De Torres, and E. Ballesteros. 2007. Rocky-shore communities as 
indicators of water quality: a case study in the Northwestern Mediterranean.  Mar. Pollut. Bull. 55: 126-135. 
(Example of using MV stats to interpret patterns of community taxa.)

Sims, M., S. Wanless, M.P. Harris, P.I. Mitchell, and D.A. Elston. 2066. Evaluating the power of monitoring 
plot designs for detecting long-term trends in the numbers of common guillemots. J. Applied Ecology 43: 
537-546. (Power to detect time trends in monitoring design options, by assessing sources and sizes of 
variance components.  Better to count birds in more plots than increasing the number of counts at existing 
plots.)

Skilleter, G.A., A. Pryor, S. Miller, and B. Cameron. 2006. Detecting the effects of physical disturbance 
on benthic assemblages in a subtropical estuary: a Beyond BACI approach. (Nice on addressing different 
spatial scales, and an example of where BACI has gone.) 

Stewart-Oaten, A. 1996. Goals in environmental monitoring. In: “Detecting Ecological Impacts: 
Concepts and Applications in Coastal Habitats”, R. J. Schmitt and C. W. Osenberg, eds., p. 17-27. Academic, 
New York.

Suter, G. W. 1996. Abuse of hypothesis testing statistics in ecological risk assessment. Human and 
Ecological Risk Assessment 2: 331-347.

Underwood, A. J. 1981. Techniques of analysis of variance in experimental marine biology and ecology. Ann. 
Rev. Oceanogr. Mar. Biol 19: 513-605.

Underwood, A. J. 1991. Beyond BACI: experimental designs for detecting human environmental impacts on 
temporal variations in natural populations. Austr. J. Mar. Freshw. Res. 42: 569-587.

Underwood, A. J. 1992. Beyond BACI: the detection of environmental impacts on populations in the real, but 
variable, world. J. Exp. Mar. Biol. Ecol. 161: 145-178.

Underwood, A. J. 1993. The mechanics of spatially replicated sampling programmes to detect environmental 
impacts in a variable world. Austr. J. Ecol. 18: 99-116..

Underwood, A. J. 1994. Things environmental scientists (and statisticians) need to know to receive (and 
give) better statistical advice. 33-61.

Underwood, A. J. 1994. On beyond BACI: sampling designs that might reliably detect environmental 
disturbances. Ecol. Appl. 4: 3-15.

Underwood, A.J., and M.G. Chapman. 2003. Power, precaution, Type II error and sampling design in assessment 
of environmental impacts. J. Exper. Mar. Ecol. 296: 49-70.  (Good general recommendations on design 
choices for detection of different kinds of impacts.)

Verlaan, P.A. 2007. Experimental activities that intentionally perturb the marine environment: implications 
for the marine environmental protection and marine scientific research provisions of the 1982 United Nations 
Convention on the Law of the Sea.  Marine Policy 31: 210-216. (I argue that experimental manipulation is 
valuable, even necessary, to do good science.  Experimental oil spills would be an example, but so would 
mesocosm experiments or field recoprocal transplant experiments.  Field observational studies are often not 
enough. )
 
Walters, C.J. and R.H. Green. 1997.  Valuation of experimental management options for ecological systems.  
J. Wildl. Man. 61(4):987-1006.  (Roughly bashed together during a sabbatical period I spent at UBC, then 
finished by back-and-forth fax and email.  Walters and I come from opposite philosophical directions re. 
estimation vs. testing, and Bayesian vs. Fisherian.  This paper is an attempt to reconcile these 
philosophically different approaches in ecological applications.  In retrospect I'm not sure that it 
succeeded very well.)

Zettler, M.L., D. Schiedek, and B. Bobertz. 2007. Benthic biodiversity indices versus salinity gradient in 
the southern Baltic Sea. Mar. Pollut. Bull. 55:258-270.  (I'm not a fan of biotic indices rather think 
that MV analyses should be used in most cases.  One problem is often confounding with natural environmental 
gradients.  This is an example of govt-driven biotic indices used where there's a salinity gradient.)





















































Papers on biomonitoring especially with bivalve molluscs 
   (some have comments, some don’t)

Bailey, R. C., and R. H. Green. 1988. Within-basin variation in the shell morphology and growth rate 
of a freshwater mussel. Can. J. Zool. 66: 1704-1708.

Green, R. H. 1971. A multivariate statistical approach to the Hutchinsonian niche: bivalve molluscs of 
central Canada. Ecology 52: 543-556.

Green, R. H. 1972. Distribution and morphological variation of Lampsilis radiata in some central 
Canadian lakes: a multivariate statistical approach. J. Fish. Res. Board Can. 29: 565-1570.

Green, R. H., R. C. Bailey, S. G. Hinch, J. L. Metcalfe, and V. H. Young. 1989. Use of freshwater mussels
to monitor the nearshore environments of lakes. J. Great Lakes Res. 15: 635-644.

Green, R. H., S. M. Singh and J. M. McCuaig. 1983. An arctic intertidal population of Macoma balthica: 
genotypic and phenotypic components of population structure. Can. J. Fish. Aquat. Sci. 40: 1360-1371.

Green, R. H., S. M. Singh, and R. C.  Bailey. 1985. Bivalve molluscs as response systems for modelling 
spatial and temporal environmental patterns. Sci. Tot. Envir. 46: 147-169.

Hinch, S. G., and R. C. Bailey. 1988. Within- and among-lake variation in shell morphology of the 
freshwater clam Elliptio complanata. Hydrobiologia 157: 27-33. 

Hinch, S. G. and L. A. Stephenson. 1987. Size and age specific patterns of trace metal concentrations in 
freshwater clams from an acid sensitive and a circumneutral lake. Can. J. Zool. 65: 2436-2442.

Hinch, S. G., and R. H. Green. 1988. Shell etching on clams from softwater Ontario lakes: a physical or 
chemical process? Can. J. Fish. Aquat. Sci. 45: 2110-2113.

Hinch, S. G., and R. H. Green. 1989. The effects of source and destination on growth and metal uptake in 
freshwater clams transplanted among south-central Ontario lakes. Can. J. Zool. 67: 855-863.

Hinch, S. G., L. J. Kelly, and R. H. Green. 1989. Morphological variation of Elliptio complanata in 
softwater lakes exposed to acidic deposition. Can. J. Zool. 67: 1895-1899.

Honkoop, P.J.C., B.L. Bayne, A.J. Underwood, and S. Svensson. 2003. Appropriate experimental design for 
transplanting mussels (Mytilus sp.) in analyses of environmental stress: an example in Sydney Harbour 
(Australia). J. Exp. Mar. Biol. and Ecol. 297: 253-268.  

Lau, P.S., and H.L. Wong. 2003. Effect of size, tissue parts, and location on six biochemical markers in 
the green-lipped mussel, Perna viridis. Mar. Pollut. Bull. 46: 1563-1572. 

McCuaig, J. M., and R. H. Green. 1983. Unionid growth curves derived from annual rings: a baseline model 
for Long Point Bay, Lake Erie. Can. J. Fish. Aquat. Sci. 40: 436-442.

Metcalfe-Smith, J. L., and R. H. Green. 1992. Ageing studies on three species of freshwater mussels from a 
metal-polluted watershed in Nova Scotia, Canada. Can. J. Zool. 70: 1284-1291.

Metcalfe-Smith, J.L., R.H. Green and L.C. Grapentine. 1996.  Influence of biological factors on 
concentrations of metals in the tissues of freshwater mussels (Elliptio complanata and Lampsilis radiata 
radiata) from the St. Lawrence River.  Can. J. Fish. Aquat. Sci. 53:205-219. (Brings together the 
literature, integrating the marine work with our freshwater work, and showing how powerful statistical 
methods can elucidate different components of biological variation caused by metal vector patterns.)

Smith, A. L., R. H. Green & A. Lutz. 1975. Uptake of mercury by freshwater clams (Family Unionidae). 
J. Fish. Res. Board Can.32: 1297-1304.