[also see Notable papers for Ecology, Ecological Applications, Frontiers and Ecosphere]

As part of ESA’s Centennial celebration we are looking back at some of the most notable papers published in ESA journals, since Ecology first rolled out the presses in 1920. All these papers will be freely available through the end of 2016.

Only “objective” measures were used to make these selections. The listed articles for each journal are weighted 90% by their number of citations. Since newer papers have not had the opportunity to gather as many citations, 10% of the weight is given to the relative number of times an article has been accessed online. Those with a very high number of downloads are likely to be more cited in the future, and thus adding the 10% weighting helps to bring in some of the relatively more recent standouts. After applying this metric, the papers are listed in chronological order for each journal. The total number on the list for each journal is roughly in proportion to the number of papers published in the lifetime of that journal.

Citations and downloads are not guaranteed measures of quality, but the high-scorers are likely to be articles which have had significant impact on the science of ecology. The lists are a starting point for reflection and discussion. We’d like to invite your observations on the value of these papers, whether it’s an observation on the impact of the article, or how things have evolved in that field, to a personal reflection, or even an explanation on why a particular article has been over-rated/over-cited. The lists were first sent to past and present presidents and editors of the journals who were invited to submit commentaries, some of which are accompanying the lists. Now is your opportunity. We invite members to submit short commentaries on these articles. We will publish as many as we can in a rotating fashion, and hope to also keep a list of previous entries. Send your contributions (one short paragraph please) related to a particular article to [email protected] with the subject line “Centennial special”. If there happens to be a figure or other image associated with the paper, or even an image evocative of the paper, or your commentary, please send that along as well. We have space for one image per journal, and it can be switched out periodically. It’s not necessary to suggest an image though.

Hope you can find something among these to comment on, as we reflect on where we have been in ecology and where we are going.

 

Vegetation of the Great Smoky Mountains
R. H. Whittaker
Ecological Monographs, Volume 26, Issue 1 (January 1956) pp. 1-80
Abstract | Full Text at JSTOR

It is ironic that a project originally intended to study insects for a degree in zoology led to the publication of one of the most well-known papers in plant ecology. Yes, Whittaker’s (1956) Monograph is a standard reference for vegetation patterns in an iconic National Park, but that is not what gave this paper its legs. This dataset allowed Whittaker to test the community-unit hypothesis (Clements 1928), which proposed that dominant species characterize the community-type and create the conditions in which subordinate species can establish and become part of an inter-connected unit. The emergent pattern in the data was strikingly different than he expected: floristic composition of communities changed continuously over environmental gradients and the distributions of dominant and subordinate species seemed to be independent from one another. Whittaker concluded that “community-units are more ‘arbitrary’ products of classification than ‘natural’ units clearly defined in the field (p 24).” This provided strong empirical evidence in favor of Gleason’s (1926) proposal that species respond ‘individualistically’ to the environment. Although my students inevitably shudder when I assign 80 page monographs for discussion, I believe it is critical for young ecologists to see how classic theories in ecology were erected, how they evolved, and how some come tumbling down. This paper (and many of Whittaker’s others) set the stage for more than five decades of gradient analysis, and although we still classify vegetation types to facilitate communication for ecosystem management, we acknowledge that these classifications are ‘arbitrary’, thanks to him. Our current understanding of species distributions can be traced back to this landmark paper.

Clements, F. E. 1928. Plant succession and indicators: a definitive edition of plant succession and plant indicators. Wilson, New York.
Gleason, H. A. 1926. The individualistic concept of the plant association. Bulletin of the Torrey Botanical Club 53:7-26.

~~ Daniel C. Laughlin, University of Waikato. July 15, 2015

Effects of Competition, Predation by Thais lapillus, and Other Factors on Natural Populations of the Barnacle Balanus balanoides
Joseph H. Connell
Ecological Monographs, Volume 31, Issue 1 (January 1961) pp. 61-104
Abstract | Full Text at JSTOR

I was working as a firefighter and surveyor for the Forest Service in 1980 on the north rim of the Grand Canyon. It was in many ways the best job a young person could have – all day every day in the woods at 8000 feet in the middle of the North Kaibab. The only drawback was that when the weather turned cold and snows came the job was over for the year. In the Winter’s I went to Tucson and took classes at the University of Arizona and one of the classes was Marine Ecology. This was a class that went to Mexico on a field trip to expose students to the closest saltwater to Tucson. It was life changing trip mainly because I hung out with my teaching assistant Curt Lively who convinced me to give up surveying and fire-fighting and to come help him with his research in Mexico. His questions were primarily evolutionary but his approach to use experiments to illuminate processes affecting barnacle demography, morphology and development was experimental, incredibly effective and the legacy of the seminal work by Joe Connell best characterized by his 1961 Ecological Monographs paper. Based on the power of the approach, and Joe’s ability to make giant steps in our understanding of the organization of ecological communities, I applied to his lab and started graduate school with him in 1982. While working with Joe as a graduate student and later as a professor of marine ecology I came to understand how much of paradigm shift occurred in large part based on the work of Connell and also Paine and Dayton. Generations of graduate students who now inhabit Universities around the world as professors embraced the experimental approach as a valid and even essential element to not just lab but also field studies. Like all shifts there has been some backlash to the experimental approach but it remains to most as the “gold” standard for ecological and evolutionary field research. Joe Connell made many fundamental contributions to our field, but, in my opinion, his demonstration of the power of elegant yet simple experimental approaches is his most important.

~~ Peter T. Raimondi, University of California - Santa Cruz. December 7, 2015

The Niche Exploitation Pattern of the Blue-Gray Gnatcatcher
Richard B. Root
Ecological Monographs, Volume 37, Issue 4 (October 1967) pp. 317-350
Abstract | Full Text at JSTOR

Effects of Forest Cutting and Herbicide Treatment on Nutrient Budgets in the Hubbard Brook Watershed-Ecosystem
Gene E. Likens, F. Herbert Bormann, Noye M. Johnson, D. W. Fisher, Robert S. Pierce
Ecological Monographs, Volume 40, Issue 1 (January 1970) pp. 23-47
Abstract | Full Text at JSTOR

Competition, Disturbance, and Community Organization: The Provision and Subsequent Utilization of Space in a Rocky Intertidal Community
Paul K. Dayton
Ecological Monographs, Volume 41, Issue 4 (October 1971) pp. 351-389
Abstract | Full Text at JSTOR

Energy Flow in Bear Brook, New Hampshire: An Integrative Approach to Stream Ecosystem Metabolism
Stuart G. Fisher, Gene E. Likens
Ecological Monographs, Volume 43, Issue 4 (October 1973) pp. 421-439
Abstract | Full Text at JSTOR

Organization of a Plant-Arthropod Association in Simple and Diverse Habitats: The Fauna of Collards (Brassica Oleracea)
Richard B. Root
Ecological Monographs, Volume 43, Issue 1 (January 1973) pp. 95-124
Abstract | Full Text at JSTOR

Organization of the New England Rocky Intertidal Community: Role of Predation, Competition, and Environmental Heterogeneity
Bruce A. Menge
Ecological Monographs, Volume 46, Issue 4 (October 1976) pp. 355-393
Abstract | Full Text at JSTOR

Intertidal Landscapes: Disturbance and the Dynamics of Pattern
R. T. Paine, Simon A. Levin
Ecological Monographs, Volume 51, Issue 2 (June 1981) pp. 145-178
Abstract | Full Text at JSTOR

Herbivory and Defensive Characteristics of Tree Species in a Lowland Tropical Forest
Phyllis D. Coley
Ecological Monographs, Volume 53, Issue 2 (June 1983) pp. 209-234
Abstract | Full Text at JSTOR

Pseudoreplication and the Design of Ecological Field Experiments
Stuart H. Hurlbert
Ecological Monographs, Volume 54, Issue 2 (June 1984) pp. 187-211
Abstract | Full Text at JSTOR

For those of us working on long-term monitoring of marked individuals, Hurlbert’s 1984 monograph about pseudoreplication was initially a bit of a shock. Everything I’d been doing, or at least analyzing, suddenly seemed wrong. In the end, this monograph actually pointed out a way to do things better, as people developed statistical methods to transform repeated measures of the same individual from a statistical liability to a biological asset.

~~ Marco Festa-Bianchet, Université de Sherbrooke. July 15, 2015

 

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Hurlbert (1984) drew much needed attention to several forms of confounding in experimental design and execution, during a period of burgeoning use of experiment in ecology. This at a time when ecology had to find its way from experiments in agro-ecosystems (R.A. Fisher’s habitat) to ecosystems that cannot be forced to relatively uniform plots in fields. In my experience as someone who teaches statistics, this publication has, unfortunately, been a consistent source of reviewer error in rejecting manuscripts and theses, over decades; for example, among many, "..this study was carried out in a single field and so is pseudoreplicated." My experience is not unique. Statisticians recommend avoiding the term. Quinn and Keough (Experimental Design and Data Analysis for Biologists 2002 p159) avoid the term: "… in part to encourage you to learn enough of experimental design to understand problemdesigns, but also because the term is a little ambiguous." Schanke andKoehnle (Journal of Comparative Psychology 123: 421–433) analyze Hurlbert(1984) and then recommend that "reviewers and editors not use the term 'pseudoreplication' as a criterion for evaluating experimental research." I look to that bright future in ecology where editors expect reviewers to help authors by stating the form of confounding in manipulative experiments as well as observational studies.

~~ David C. Schneider, Memorial University. July 20, 2015

 

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Hurlbert makes a strong case for replication and interspersion not just in field experiments but in experimental design more generally. His arguments are based on good scientific and statistical reasoning and are couched in the language of error terms for statistical hypothesis tests. Here I want to translate them into the language of models.

Take Hurlbert's examples comparing leaf decomposition on the 1m and 10m isobaths of a lake. Eight bags of maple leaves are placed on each isobath. In his Example 3, all eight bags are placed at a single site on each isobath. It should be obvious to most readers today that even if we find a substantial difference between the 1m bags and the 10m bags, that difference might be, in Hurlbert's words, “no greater than we would have found if the two sets of eight bags had been placed at two locations on the same isobath.” Attributing that difference to a difference between isobaths is what Hurlbert calls “pseudoreplication,” or “testing for treatment effects with an error term inappropriate to the hypothesis being considered.”

I prefer to think about models rather than tests and error terms. Call the measurements on the 1m bags x1, …, x8 and the measurements on the 10m bags x9, …, x16 and suppose that x1, …, x8 are similar to each other but different from x9, …, x16, which are also similar to each other. Several models we might consider are:

Model A. x1, …, x16 ~ iid N(m,s)

Model B. x1, …, x8 ~ iid N(m1,s); x9, …, x16 ~ iid N(m2,s)

Model C. x1, …, x8 ~ iid N(m + d1,s); x9, …, x16 ~ iid N(m + d2,s); (d1,d2) ~ iid N(0,s2)

Model D. x1, …, x16 ~ MVN(m,s*C) where C is not the identity correlation matrix

If there is a substantial difference between the 1m and 10m bags, then Model A does not describe the data very well and we would search for an alternate explanation. Model B is a standard fixed-effects model; Model C is a standard mixed effects, or hierarchical, model; while Model D, if the correlation matrix C is block-diagonal, says that the first 8 measurements are correlated with each other, but not with the last 8 measurements, which are likewise correlated within themselves. Each of B, C, and D describes our data reasonably well.

From a statistical point of view, there may be no reason to favor one of B, C, or D over another, but the models say different things about the world. B would be appropriate if we thought the decomposition rates at the two sites do not change over time. C also says that the rates do not change, but says further that we view the rates at those two sites as random; C would be appropriate if we thought that the decomposition rates at other sites are like random draws from the same distribution without regard to depth. D says that the measurements at each site are similar to each other but different from the measurements at other sites; D would be appropriate if we thought that the rates at each site might change over time, but the measurements at each site would still be similar to each other.

What does this have to do with replication and interspersion? It's that without replication and interspersion the data don't help us distinguish between B, C, and D. Had we replicated and, as Hurlbert recommends, placed the bags at 8 different locations on the 1m and 10m isobaths and, posssibly, replicated over time, then models like C and D could have been seen to be implausible. That's why, to a modeller, replication and interspersion are important: they help distinguish between models. We reach the same conclusion as Hurlbert, though starting from a different point of view.

~~ Michael Lavine, University of Massachusetts. September 13, 2015

 

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This paper quickly received a positive reception from both statisticians and biologists, and was given the American Statistical Association’s G.W. Snedecor award for the best paper in biometry in 1984. The origins of the paper and its adventures in the review process were recounted in Hurlbert (1993a). One detail I learned only some years later, from Larry Crowder, was the identity of the reviewer who recommended to EM editor Nelson Hairston Sr. that the manuscript be reduced to a letter to the ESA Bulletin because “there is nothing new here, neither as to ecology or applied statistics.” That reviewer was the same North Carolina State prof whose advice was responsible for pseudoreplication in some of Hairston’s own papers on salamander competition (see Hurlbert 2012). (So, profs, be careful of what secrets you divulge to students in your graduate seminar classes – and, of course, elsewhere too.)

Making use of Urquhart’s (1981) term evaluation unit, definitions of the three main types of pseudoreplication – simple, temporal and sacrificial – were clarified, and many additional examples of pseudoreplication were discussed in my own later papers with colleagues on the topic (Hurlbert 1990, Hurlbert & White 1993b, Lombardi & Hurlbert 1996, García-Berthou & Hurlbert 1999, Hurlbert & Meikle 2003, Hurlbert & Lombardi 2004, Kozlov & Hurlbert 2006, Hurlbert 2009) and in papers by many others as well.

What has been the impact of this statistical education project? The 1984 paper documented that 48 percent of the experimental studies examined that employed inferential statistics (n=101) committed pseudoreplication. Now jump ahead 20-30 years. Kozlov (2003) found pseudoreplication in 62 percent of experimental studies using inferential statistics (n=65) in the Russian ecological literature. Ramage et al. (2012) found at least 68 percent of papers on effects of logging on tropical forest biodiversity (n=77) contained pseudoreplication. On the aquatic side, Wernberg et al. (2012) found 45 percent of recent ‘marine climate change experiments’ (n=110) had “a lack of treatment replication or various kinds of pseudo-replication.”

Pseudoreplication errors are about the simplest kinds of statistical errors one can make, but it would seem that current researchers, referees and editors are collectively no better able to avoid or detect them than were their pre-1980s predecessors. Some major causes of this lack of progress are evident.

First, very quickly after 1984, pseudoreplication was being and continues to be incorrectly defined or mischaracterized in large numbers of papers mentioning or discussing the concept. Unwarranted criticisms also have been leveled at authors by thesis advisors, manuscript reviewers and editors who themselves misunderstood pseudoreplication. My services as referee of disputes were enlisted on many occasions. Tony Underwood (1998) eventually suggested that, “Authors who cite Hurlbert would do better if they had read his paper!” To which could have been added, “and Hurlbert & White (1993) and a few others as well.” Even most post-2010 authors referencing the problem of pseudoreplication seem unaware of any post-1984 papers on the topic.

Second, lack of progress reflects long-standing errors in textbooks and reference manuals. Sokal and Rohlf (2012), perhaps the most widely used statistics book by biologists over the last half century, has never discussed pseudoreplication. But it has, in all its editions, contained worked examples of recommended procedures that, in fact, constitute pseudoreplication (as discussed in Hurlbert 2004, 2009, 2013b, Kozlov & Hurlbert 2006). Of those books that do mention pseudoreplication a large percentage incorrectly define or mischaracterize the error (e.g. Heath 1995, Underwood 1996, Zar 1999, Grafen & Hails 2002, Quinn & Keough 2002, Ruxton & Colegrave 2003, Hawkins 2005, Casella 2008). For example, Underwood (1996) synonymizes the term with the broader, vaguer concept of “confounding”, and Quinn & Keough (2002) and Casella (2008) define it as “subsampling” of experimental units. Our reviews of those three books noted these problems (Hurlbert 1997, 2013a, Hurlbert & Lombardi 2003).

Third, lack of progress, indeed retrogression, in collective understanding of these matters can in part be attributed to two focused attacks on the 1984 paper and the term and concept of pseudoreplication. Oksanen (2001) called it a “pseudoissue” reflecting “a totally outdated epistemology.” Schank & Koehnle (2009; also Koehnle & Schank 2009) called it a “pseudo-problem” and “a flawed methodological doctrine.” These papers contained numerous errors on a wide variety of topics. These were pointed out and corrected by myself and numerous other biologists and statisticians (Hurlbert 2004, 2009, 2010, and references cited therein) but not before many innocent souls had been chummed in. Later, in personal correspondence, Oksanen admitted he had never read a book on experimental design, and the editor who handled the Schank & Koehnle (2009) manuscript admitted he had been unable to find a professional statistician willing to review it. I was not given an opportunity to comment on either manuscript even though each was solely a critique of my work.

Fourth, lack of progress also has deeper roots relating 1) to how introductory statistics courses are often, perhaps usually, taught, accompanied by little to no introduction to basic experimental and sampling design concepts, and 2) to the disdain of model-focused, language-dismissive professional statisticians for maintaining clear terminologies, definitions and conceptual frameworks that could be used across all disciplines using statistics. My recent thoughts on those matters can be found in Hurlbert (2009, 2012, 2013a).

References
(pdfs of most of my own are available at http://www.bio.sdsu.edu/pub/stuart/stuart.html ; I can supply others also)

García-Bertou, E. & Hurlbert, S.H. Pseudoreplication in hermit crab shell selection experiments: Comment to Wilbur. Bulletin of Marine Science 65:893-895.
Grafen, A. & Hails, R. 2002. Modern statistics for the life sciences. Oxford University Press, Oxford UK. Hawkins, D. 2005. Biomeasurement: Understanding, analyzing and communicating data in the biosciences. Oxford University Press, Oxford UK.
Heath, D. 1995. An introduction to experimental design and statistics for biology. UCL Press, London UK.
Hurlbert, S. H.  1990.  Pastor binocularis:  Now we have no excuse [review of Design of experiments by R. Mead]. Ecology 71: 1222-1228.
Hurlbert, S.H. 1993a.  Dragging statistical malpractice into the sunshine [Citation Classic:  Pseudoreplication and the design of ecological field experiments].  Current Contents 1993, No. 12, March 22: 18.
Hurlbert, S. H. 1997.  Experiments in ecology [Review of book by same title by A.J. Underwood]. Endeavour 21: 172-173.
Hurlbert, S. H. 2004. On misinterpretations of pseudoreplication and related matters: A reply to Oksanen. Oikos 104: 591-597.
Hurlbert, S. H. 2009. The ancient black art and transdisciplinary extent of pseudoreplication. Journal of Comparative Psychology 123: 434-443.
Hurlbert, S.H. 2010. Pseudoreplication capstone: Correction of 12 errors in Koehnle & Schank (2009). Department of Biology, San Diego State University, San Diego, California. 5 pp.
Hurlbert, S.H. 2012. Pseudofactorialism, response structures, and collective responsibility. Austral Ecology 38: 646-663 + suppl. inform.
Hurlbert, S.H. 2013a. Affirmation of the classical terminology for experimental design via a critique of Casella's Statistical Design. Agronomy Journal 105: 412-418 + suppl. inform. 
Hurlbert, S.H. 2013b. [Review of Biometry, 4th edn, by R.R. Sokal & F.J. Rohlf]. Limnology and Oceanography Bulletin 22(2): 62-65. 
Hurlbert, S.H. & Lombardi, C. M. 2003. Design and analysis: Uncertain intent, uncertain result [Review of Experimental design and data analysis for biologists, by G.P. Quinn & M.J. Keough].  Ecology 83: 810-812.
Hurlbert, S.H. & Lombardi, C.M. 2004. Research methodology: experimental design sampling design, statistical analysis. In M.M.  Bekoff (ed.),  Encyclopedia of Animal Behavior 2: 755-762. Greenwood Press, London.
Hurlbert, S.H. & Meikle, W.G. 2003. Pseudoreplication, fungi, and locusts. Journal of Economic Entomology 96: 533-535.
Hurlbert, S. H. & White, M. D. 1993.  Experiments with freshwater invertebrate zooplanktivores: Quality of statistical analyses. Bulletin of Marine Science 53:128-153.
Koehnle, T.J. & Schank, J. C. 2009. An ancient black art. Journal of Comparative Psychology 123: 452-458.
Kozlov, M. V. 2003. Pseudoreplication in Russian ecological publications. Bulletin of the Ecological Society of America 84: 45-47. [Condensation of original article published in Russian in Zhurnal Obstchei Biologii [Journal of Fundamental Biology], 64, 292-397].
Kozlov, M. V. & Hurlbert, S.H. 2006. Pseudoreplication, chatter, and the international nature of science: A response to D. V. Tatarnikov. Zhurnal Obstchei Biologii [Journal of Fundamental Biology] 67(2): 128-135 [In Russian; English translation available as pdf].
Lombardi, C. M. & Hurlbert, S. H. 1996.  Sunfish cognition and pseudoreplication. Animal Behaviour 52: 419-422.
Quinn, G. P. & Keough, M. J. 2002. Experimental design and data analysis for biologists. Cambridge University Press, Cambridge UK.
Oksanen, L. 2001. Logic of experiments in ecology: Is pseudoreplication a pseudoissue? Oikos 94: 27-38.
Ramage, B.S. et al. 2012. Pseudoreplication in tropical forests and the resulting effects on biodiversity conservation. Conservtion Biology 27: 364-372.
Schank, J. C. & Koehnle, T. J. 2009. Pseudoreplication is a pseudoproblem. Journal of Comparative Psychology 123: 421-433
Underwood, A.J. 1996. Experiments in ecology. Cambridge University Press, Cambridge UK
Underwood, A.J. 1998. Design, implementation, and analysis of ecological and environmental experiments. Pp. 325-349 in W.J. Resetarits Jr. & J. Bernardo (eds), Experimental ecology: Issues and Perspectives. Oxford University Press, Oxford UK
Wernberg, T., Smale, D.A. & Thomsen, M.S. 2012. A decade of climate change experiments on marine organisms: Procedures, patterns and problems. Global Change Biology. doi: 10.1111/j.1365-2486.2012.02656.x
Zar, J.H. 1999. Biostatistical analysis, 4th edn. Prentice-Hall, Upper Saddle River NJ.

~~ Stuart Hurlbert, San Diego State University, September 23, 2015

Comparisons of Treatments After an Analysis of Variance in Ecology
R. W. Day, G. P. Quinn
Ecological Monographs, Volume 59, Issue 4 (December 1989) pp. 433-463
Abstract | Full Text at JSTOR

Geostatistical Tools for Modeling and Interpreting Ecological Spatial Dependence
Richard E. Rossi, David J. Mulla, Andre G. Journel, Eldon H. Franz
Ecological Monographs, Volume 62, Issue 2 (June 1992) pp. 277-314
Abstract | Full Text at JSTOR

Leaf Life-Span in Relation to Leaf, Plant, and Stand Characteristics among Diverse Ecosystems
P. B. Reich, M. B. Walters, D. S. Ellsworth
Ecological Monographs, Volume 62, Issue 3 (September 1992) pp. 365-392
Abstract | Full Text at JSTOR

Cross-Scale Morphology, Geometry, and Dynamics of Ecosystems
C. S. Holling
Ecological Monographs, Volume 62, Issue 4 (December 1992) pp. 447-502
Abstract | Full Text at JSTOR

Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies
Jean-Dominique Lebreton, Kenneth P. Burnham, Jean Clobert, David R. Anderson
Ecological Monographs, Volume 62, Issue 1 (March 1992) pp. 67-118
Abstract | Full Text at JSTOR

Long-term studies of marked individuals have contributed substantial advances in population ecology, evolutionary ecology and conservation biology. The 1992 paper by Lebreton et al. formalized the need to account for sightability (or detection probability, or whatever you wish to call it: when it’s there but you don’t see it) in these programs. At about that time I started collaborating with a student of Jean-Dominique, Jean-Michel Gaillard. Both Jean-Michel and Jean-Dominique were astonished (to use a polite word) by my claim of 100% sightability in my bighorn sheep and mountain goat studies. The new methods gave satisfaction to all: my sighting rate for ewes was indeed 100%, but for rams it was about 96% - mostly because of temporary emigrants. This monograph has had an enormous positive impact on long-term monitoring programs, forcing the reconsideration of some results. It showed how to do better science and provide better advice for the conservation of biodiversity.

~~ Marco Festa-Bianchet, Université de Sherbrooke. July 15, 2015

Quantitative Effects of Grazing on Vegetation and Soils Over a Global Range of Environments
D. G. Milchunas, W. K. Lauenroth
Ecological Monographs, Volume 63, Issue 4 (November 1993) pp. 327-366
Abstract | Full Text at JSTOR

Avian Life History Evolution in Relation to Nest Sites, Nest Predation, and Food
Thomas E. Martin
Ecological Monographs, Volume 65, Issue 1 (February 1995) pp. 101-127
Abstract | Full Text at JSTOR

SPECIES ASSEMBLAGES AND INDICATOR SPECIES:THE NEED FOR A FLEXIBLE ASYMMETRICAL APPROACH
Marc Dufrêne, Pierre Legendre
Ecological Monographs, Volume 67, Issue 3 (August 1997) pp. 345-366
Abstract | Full Text | PDF (373 KB)

EFFECTS OF BIODIVERSITY ON ECOSYSTEM FUNCTIONING: A CONSENSUS OF CURRENT KNOWLEDGE
D. U. Hooper, F. S. Chapin III, J. J. Ewel, A. Hector, P. Inchausti, S. Lavorel, J. H. Lawton, D. M. Lodge, M. Loreau, S. Naeem, B. Schmid, H. Setälä, A. J. Symstad, J. Vandermeer, D. A. Wardle
Ecological Monographs, Volume 75, Issue 1 (February 2005) pp. 3-35
Abstract | Full Text | PDF (392 KB)