Pugnaire F.I. Valladares F. Functional Plant Ecology
Подождите немного. Документ загружается.
Myneni, R.B. and D.L. Williams, 1994. On the relationship between FAPAR and NDVI. Remote
Sensing of Environment 49: 200–211.
Myneni, R.B., S.O. Los, and G. Asrar, 1995. Potential gross primary productivity of terrestrial
vegetation from 1982–1990. Geophysical Research Letters 22: 2617–2620.
Myneni, R.B., C.D. Keeling, C.J. Tucker, G. Asrar, and R.R. Nemani, 1997. Increased plant growth in
the northern high latitudes from 1981–1991. Nature 386: 698–702.
Nemani, R. and S. Running, 1997. Land cover characterization using multitemporal red, near-IR, and
thermal-IR data from NOAA=AVHRR. Ecological Applications 7: 79–90.
Nobel, P.S., 1991. Physicochemical and Environmental Plant Physiology. Academic Press, San Diego, CA.
Norman, J.M. and G.S. Campbell, 1989. Canopy structure. In: R.W. Pearcy, J. Ehleringer, H.A.
Mooney, and P.W. Rundel, eds. Plant Physiological Ecology: Field Methods and Instrumenta-
tion. Chapman and Hall, London, pp. 301–325.
Oechel, W.C., S.J. Hastings, G. Vourlitis, M. Jenkins, G. Riechers, and N. Grulke, 1993. Recent change
of Arctic tundra ecosystems from a net carbon dioxide sink to a source. Nature 361: 520–523.
Oechel, W.C., G.L. Vourlitis, S.J. Hastings, R.C. Zulueta, L. Hinzman, and D. Kane, 2000. Acclimation
of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming.
Nature 406(6799): 978–81.
Pearcy, R.W., 1989. Radiation and light measurements. In: R.W. Pearcy, J. Ehleringer, H.A. Mooney,
and P.W. Rundel, eds. Plant Physiological Ecology: Field Methods and Instrumentation.
Chapman and Hall, London, pp. 97–116.
Pen
˜
uelas, J. and I. Filella, 1998. Visible and near-infrared reflectance techniques for diagnosing plant
physiological status. Trends in Plant Science 3: 151–156.
Pen
˜
uelas, J., I. Filella, C. Biel, L. Serrano, and R. Save, 1993. The reflectance at the 950–970 nm region
as an indicator of plant water status. International Journal of Remote Sensing 14: 1887–1905.
Pen
˜
uelas, J., J.A. Gamon, A.L. Fredeen, J. Merino, and C.B. Field, 1994. Reflectance indices associated
with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of
Environment 48: 135–146.
Pen
˜
uelas, J., F. Baret, and I. Filella, 1995a. Semi-empirical indices to assess carotenoids=chlorophyll a
ratio from leaf spectral reflectance. Photosynthetica 31: 221–230.
Pen
˜
uelas, J., I. Filella, and J.A. Gamon, 1995b. Assessment of photosynthetic radiation-use efficiency
with spectral reflectance. New Phytologist 131: 291–296.
Pen
˜
uelas, J., J. Llusia, J. Pin
˜
ol, and I. Filella, 1997a. Photochemical reflectance index and leaf photo-
synthetic radiation-use efficiency assessment in Mediterranean trees. International Journal of
Remote Sensing 18: 2863–2868.
Pen
˜
uelas, J., J. Pin
˜
ol, R. Ogaya, and I. Filella, 1997b. Estimation of plant water concentration by the
reflectance Water Index WI (R900=R970). International Journal of Remote Sensing 18: 2869–2875.
Perry, C.R. and L.F. Laughtenschlager, 1984. Functional equivalence of spectral vegetation indices.
Remote Sensing of Environment 14: 169–182.
Peterson, D.L., J.D. Aber, P.A. Matson, D.H. Card, N. Swanberg, C. Wessman, and M. Spanner, 1988.
Remote sensing of forest canopy and leaf biochemical content. Remote Sensing of Environment
24: 85–108.
Pfu
¨
ndel, E. and W. Bilger, 1994. Regulation and possible function of the violaxanthin cycle. Photosyn-
thesis Research 42: 89–109.
Pinzon, J.E., S.L. Ustin, C.M. Castenada, and M.O. Smith, 1998. Investigation of leaf biochemistry
by hierarchical foreground=background analysis. IEEE Trans. Geoscience and Remote Sensing
36: 1913–1927.
Potter, C.S., J.T. Randerson, C.B. Field, P.A. Matson, P.M. Vitousek, H.A. Mooney, and
S.A. Klooster, 1993. Terrestrial ecosystem production: a process model based on global satellite
and surface data. Global Biogeochemical Cycles 7: 811–841.
Prince, S.D., 1991. A model of regional primary production for use with coarse-resolution satellite data.
International Journal of Remote Sensing 12: 1313–1330.
Prince, S.D. and S.N. Goward, 1995. Global primary production: a remote sensing approach. Journal of
Biogeography 22: 815–835.
Quattrochi, D.A. and M.A. Goodchild, eds., 1997. Scale in Remote Sensing and GIS. CRC Press, Boca
Raton, FL.
Francisco Pugnaire/Functional Plant Ecology 7488_C022 Final Proof page 680 18.4.2007 11:51am Compositor Name: VAmoudavally
680 Functional Plant Ecology
Rahman A.F., V.D. Cordova, J.A. Gamon, H.P. Schmid, and D.A. Sims, 2004. Potential of MODIS
ocean bands for estimating CO
2
flux from terrestrial vegetation: A novel approach. Geophysical
Research Letters, 31, L10503, doi:10.1029=2004GL019778.
Raven, P.H., H.J. Thompson, and B.A. Prigge, 1986. Flora of the Santa Monica Mountains, California,
2nd edn. Southern California Botanists, Special Publication No. 2. University of California,
Los Angeles.
Richards, J.A., 1993. Remote Sensing Digital Image Analysis: An Introduction. Springer-Verlag, Berlin.
Roberts, D.A., J.B. Adams, and M.O. Smith, 1993. Discriminating green vegetation, non-photosyn-
thetic vegetation and soils in AVIRIS data. Remote Sensing of Environment 44: 255–270.
Roberts, D.A., R.O. Green, and J.B. Adams, 1997. Temporal and spatial patterns in vegetation and
atmospheric properties from AVIRIS. Remote Sensing of Environment 62: 223–240.
Roberts, D.A., M. Gardner, R. Church, S. Ustin, G. Scheer, and R.O. Green, 1998. Mapping chaparral
in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote
Sensing of Environment 65: 267–279.
Ruimy, A., B. Saugier, and G. Dedieu, 1994. Methodology for the estimation of terrestrial primary
production from remotely sensed data. Journal of Geophysical Research 99: 5263–5283.
Ruimy, A., G. Dedieu, and B. Saugier, 1996. TURC: a diagnostic model of continental gross primary
productivity and net primary productivity. Global Biogeochemical Cycles 10: 269–285.
Running, S.W. and E.R. Hunt Jr., 1993. Generalization of a forest ecosystem process model for other
biomes, BIOME-BGC, and an application for global-scale models. In: J.R. Ehleringer and
C.B. Field, eds. Scaling Physiological Processes: Leaf to Globe. Academic Press, San Diego,
CA, pp. 141–158.
Running, S.W. and R.R. Nemani, 1988. Relating seasonal patterns of the AVHRR vegetation index to
simulated photosynthesis and transpiration of forests in different climates. Remote Sensing of
Environment 24: 347–367.
Running, S.W., R.R. Nemani, F.A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, 2004.
A continuous satellite-derived measure of global primary production. BioScience 54(6):
547–560.
Runyon, J., R.H. Waring, S.N. Goward, and J.M. Welles, 1994. Environmental limits on net primary
production and light-use efficiency across the Oregon Transect. Ecological Applications 4:
226–237.
Russell, G., P.G. Jarvis, and J.L. Monteith, 1989. Absorption of radiation by canopies and stand
growth. In: G. Russell, B. Marshall, and P.G. Jarvis, eds. Plant Canopies: Their Growth,
Form, and Function. Cambridge University Press, Cambridge, pp. 21–39.
Sabins, F.F., 1997. Remote Sensing: Principles and Interpretation, 3rd edn. W.H. Freeman and
Company, NY.
Salerno, A.E., 1976. Aerospace photography. In: E. Schanda, ed. Remote Sensing for Environmental
Sciences. Springer-Verlag, Berlin, pp. 11–83.
Schulze, E.-D. and H.A. Mooney, eds., 1994. Biodiversity and Ecosystem Function. Springer-Verlag,
Berlin.
Sellers, P.J., 1987. Canopy reflectance, photosynthesis, and transpiration. II. The role of biophysics in
the linearity of their interdependence. Remote Sensing of Environment 21: 143–183.
Sellers, P.J., C.J. Tucker, G.J. Collatz, S.O. Los, C.O. Justice, D.A. Dazlich, and D.A. Randall, 1994.
A global 18 by 18 NDVI data set for climate studies. Part 2: the generation of global fields of
terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing 15:
3519–3545.
Sellers, P.J., B.W. Meeson, F.G. Hall, G. Asrar, R.E. Murphy, R.A. Schiffer, F.P. Bretherton,
R.E. Dickinson, R.G. Ellingson, C.B. Field, K.F. Huemmrich, C.O. Justice, J.M. Melack,
N.T. Roulet, D.S. Schimel, and P.D. Try, 1995. Remote sensing of the land surface for studies
of global change: models—algorithms—experiments. Remote Sensing of Environment 51: 3–26.
Sellers, P.J., L. Bounoua, G.J. Collatz, D.A. Randall, D.A. Dazlich, S.O. Los, J.A. Berry, I. Fung,
C.J. Tucker, C.B. Field, and T.G. Jensen, 1996a. Comparison of radiative and physiological
effects of doubled atmospheric CO
2
on climate. Science 271: 1402–1406.
Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J. Collatz, and D.A. Randall, 1996b.
A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II: the generation
Francisco Pugnaire/Functional Plant Ecology 7488_C022 Final Proof page 681 18.4.2007 11:51am Compositor Name: VAmoudavally
Ecological Applications of Remote Sensing at Multiple Scales 681
of global fields of terrestrial biophysical parameters from satellite data. Journal of Climate
9: 706–737.
Serrano, L., S.L. Ustin, D.A. Roberts, J.A. Gamon, and J. Penuelas, 2000. Deriving water content of
chaparral vegetation from AVIRIS data. Remote Sensing of Environment 74: 570–581.
Sims, D.A. and J.A. Gamon, 2002. Relationships between leaf pigment content and spectral reflectance
across a wide range of species, leaf structures and developmental stages. Remote Sensing of
Environment 81: 337–354.
Sims, D.A. and J.A. Gamon, 2003. Estimation of vegetation water content and photosynthetic tissue
area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll
absorption. Remote Sensing of Environment 84: 526–537.
Sims, D.A., H. Luo, S. Hastings, W.C. Oechel, A.F. Rahman, and J.A. Gamon, 2006. Parallel
adjustments in vegetation greenness and ecosystem CO
2
exchange in response to drought in a
Southern California chaparral ecosystem. Remote Sensing of Environment 103: 289–303.
Smith, L.C., 1997. Satellite remote sensing of river inundation area, stage, and discharge: a review.
Hydrological Processes 11: 1427–1440.
Smith, T.M., H.H. Shugart, and F.I. Woodward, eds., 1997. Plant Functional Types: Their Relevance to
Ecosystem Properties and Global Change. Cambridge University Press, Cambridge.
Solomon, A.M. and H.H. Shugart, eds., 1993. Vegetation Dynamics and Global Change. Chapman and
Hall, NY.
Steinmetz, S., M. Guerif, R. Delecolle, and F. Baret, 1990. Spectral estimates of the absorbed photo-
synthetically active radiation and light-use efficiency of a winter wheat crop subjected to
nitrogen and water deficiencies. International Journal of Remote Sensing 11: 1797–1808.
Strack, D., 1997. Phenolic metabolism. In: P.M. Dey and J.B. Harbourne, eds. Plant Biochemistry.
Academic Press, San Diego, CA, pp. 387–416.
Tilman, D., J. Knops, D. Wedin, P. Reich, M. Ritchie, and E. Siemann, 1997. Optimization of end-
members for spectral mixture analysis. Science 277: 1300–1302.
Tompkins, S., J.F. Mustard, C.M. Pieters, and D.W. Forsyth, 1997. Optimization of endmembers for
spectral mixture analysis. Remote Sensing of Environment 59: 472–489.
Townshend, J.R.G., 1994. Global data sets for land applications from the Advanced Very
High Resolution Radiometer: an introduction. International Journal of Remote Sensing 15:
3319–3332.
Tucker, C.J., I.Y. Fung, C.D. Keeling, and R.H. Gammon, 1986. Relationship between atmospheric
CO
2
variations and a satellite-derived vegetation index. Nature 319: 195–199.
Tucker, C.J., H.E. Dregne, and W.W. Newcomb, 1991. Expansion and contraction of the Sahara Desert
from 1980 to 1990. Science 253: 299–301.
Turner, M.G., and R.H. Gardner, eds., 1991. Quantitative Methods in Landscape Ecology. Springer-
Verlag, NY.
Ustin, S.L., M.O. Smith, and J.B. Adams, 1993. Remote Sensing of ecological processes: a strategy for
developing and testing ecological models using spectral mixture analysis. In: J.R. Ehleringer and
C.B. Field, eds. Scaling of Physiological Processes: Leaf to Globe. Academic Press, San Diego,
CA, pp. 339–357.
Ustin, S.L., D.A. Roberts, J.E. Pinzon, S. Jacquemoud, G. Scheer, C.M. Castenada, and A. Palacios,
1998. Estimating canopy water content of chaparral shrubs using optical methods. Remote
Sensing of Environment 65: 280–291.
Ustin, S.L., D.A. Roberts, J.A. Gamon, G.P. Asner, and R.O. Green, 2004. Using imaging spectroscopy
to study ecosystem processes and properties. BioScience 54(6): 523–533.
Valentini, R., J.A. Gamon, and C.B. Field, 1995. Ecosystem gas exchange in a California grassland:
seasonal patterns and implications for scaling. Ecology 76: 1940–1952.
Vanderbilt, V.C., L. Grant, and S.L. Ustin, 1990. Polarization of light by vegetation. In: J. Ross and
R.B. Myneni, eds. Photon–Vegetation Interactions: Applications in Optical Remote Sensing
and Plant Ecology. Springer-Verlag, Berlin, pp. 194–228.
Vitousek, P.M., 1994. Beyond global warming: ecology and global change. Ecology 75: 1861–1876.
Walker, B. and W. Steffen, eds., 1996. Global Change and Terrestrial Ecosystems. Cambridge University
Press, Cambridge.
Francisco Pugnaire/Functional Plant Ecology 7488_C022 Final Proof page 682 18.4.2007 11:51am Compositor Name: VAmoudavally
682 Functional Plant Ecology
Wardle, D.A., O. Zackrisson, G. Ho
¨
rnberg, and C. Gallet, 1997. The influence of island areas on
ecosystem properties. Science 277: 1296–1299.
Welles, J.M., 1990. Some indirect methods of estimating canopy structure. Remote Sensing Reviews
5: 31–43.
Wessman, C.A., 1990. Evaluation of canopy biochemistry. In: R.J. Hobbs and H.A. Mooney, eds.
Remote Sensing of Biosphere Functioning. Springer-Verlag, NY, pp. 135–156.
Wessman, C.A., 1992. Spatial scales and global change: bridging the gap from plots to GCM grid cells.
Annual Review of Ecology and Systematics 23: 175–200.
Wessman, C.A., J. Aber, D. Peterson, and J. Melillo, 1988. Remote sensing of canopy chemistry and
nitrogen cycling in temperate forest ecosystems. Nature 335: 154–156.
Wessman, C.A., C.A. Bateson, and T.L. Benning, 1997. Detecting fire and grazing patterns in tallgrass
prairie using spectral mixture analysis. Ecological Applications 7: 493–511.
Whittaker, R.H., 1972. Evolution and measurement of species diversity. Taxon 21: 213–251.
Wiegand, C.L., A.J. Richardson, D.E. Escobar, and A.H. Gerbermann, 1991. Vegetation indices in crop
assessments. Remote Sensing of Environment 35: 105–119.
Yoder, B.J. and R.E. Pettigrew-Crosby, 1995. Predicting nitrogen and chlorophyll content and concen-
tration from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sensing of
Environment 53: 199–211.
Yost, E. and S. Wenderoth, 1969. Ecological applications of multispectral color aerial photography. In:
P.L. Johnson, ed. Remote Sensing in Ecology. University of Georgia Press, Athens, GA,
pp. 46–62.
Young, A. and G. Britton, eds., 1993. Carotenoids in Photosynthesis. Chapman and Hall, London.
Zhang, M., S.L. Ustin, E. Rejmankova, and E.W. Sanderson, 1997. Remote sensing of salt marshes:
potential for monitoring. Ecological Applications 7: 1039–1053.
Francisco Pugnaire/Functional Plant Ecology 7488_C022 Final Proof page 683 18.4.2007 11:51am Compositor Name: VAmoudavally
Ecological Applications of Remote Sensing at Multiple Scales 683
Francisco Pugnaire/Functional Plant Ecology 7488_C022 Final Proof page 684 18.4.2007 11:51am Compositor Name: VAmoudavally
23
Generalization in Functional
Plant Ecology: The Species-
Sampling Problem, Plant
Ecology Strategy Schemes,
and Phylogeny*
Mark Westoby
CONTENTS
Introduction ....................................................................................................................... 685
Inference Rules for Generalizing from a Sample ............................................................... 686
Application of the Inference Rules to Generalization across Species—An Issue
That Has Been Underestimated...................................................................................... 687
Criteria on Which to Compare Species .............................................................................. 688
Habitat............................................................................................................................689
Traits of the Species Themselves—Ecological Strategy Schemes ....................................... 689
Phylogeny ........................................................................................................................... 693
Conclusion.......................................................................................................................... 698
References .......................................................................................................................... 699
INTRODUCTION
This chapter is about how we generalize across species, and how we choose species for study.
If a process is universal, it should not matter in which species we study it. An expectation
that the processes under study should be universal is implicit in much physiological or
developmental genetic work. Correspondingly a single species is studied, chosen for experi-
mental convenience, perhaps tobacco, or Arabidopsis. In functional ecology, on the other hand,
most generalizations are conditional and comparative. Functional ecology aims to work out
how things operate differently in pioneer versus shade-tolerant species, on low-nutrient versus
high-nutrient soils, among monocots versus dicots, in rare compared with widespread species.
Functional ecology makes progress through the struggle to generalize, to understand what
is similar and what is different about species and situations. Progress is manifested at two
levels in publication. In primary journal publications reporting fresh data, comparisons
may be between as few as two species, up to tens of species, depending which traits were
* This chapter has not been updated since the first edition of the Handbook.
Francisco Pugnaire/Functional Plant Ecology 7488_C023 Final Proof page 685 2.5.2007 10:29am Compositor Name: BMani
685
studied and how difficult and time-consuming it was to characterize them. However, later on,
in literature reviews or meta-analyses, numbers of such primary studies are gathered together
and generalizations are sought. It is really at the stage of compiling literature reviews that
knowledge can be said to become consolidated and reliable. At this stage evidence
may be available about tens to hundreds of species (e.g., edited review books about ecophysio-
logy such as Lange et al 1984, Lambers et al 1989, Roy and Garnier 1994, Schulze and
Caldwell 1994, Mulkey et al 1996). Similarly many hundreds of field experiments have now
been conducted on the major interactions between species such as competition, herbivory,
and predation (Connell 1983, Crawley 1983, Schoener 1983, Sih et al 1985, Price et al
1986, Hairston 1989, Goldberg and Barton 1992, Gurevitch et al 1992, Wilson and Agnew
1992, Goldberg 1996). Generalizing across the many primary studies has now become an
outstanding problem for ecology.
This chapter argues in the following sequence. First, the reader is briefly reminded
of the rules for inferring generalizations from samples. These rules are familiar to
ecologists in contexts such as vegetation sampling, but curiously, they have been almost
completely ignored in the context of selecting species for study. Some problems in applying
the generalization rules to species selection are acknowledged, and the distinction between
papers reporting primary data collection and subsequent review and meta-analysis papers
becomes important here. Then to arrive at a conditional generalization, species have to be
categorized in some way. The three main types of categorization are habitat, ecological
strategy (traits of the species itself), and phylogeny. For primary data collection, anyone
could use their own basis for categorizing species, but during subsequent meta-analyses, only
widely-adopted categorization schemes can come into play. Hence, achieving consensus on at
least some categorization schemes should be an important part of the research agenda of
comparative functional ecology. A new ecological strategy scheme is proposed for this
purpose. Finally phylogeny: species with recent common ancestors are more likely to have
similar traits. A brief outline is provided of the recent debate about whether phylogeny should
be regarded as an alternative explanation to present-day functionality. More constructively,
phylogeny provides a potential tool for choosing study species to arrive more efficiently at
generalizations, and to place better-defined boundaries around the generalizations.
INFERENCE RULES FOR GENERALIZING FROM A SAMPLE
The rules for inferring a generalization from a sample have been well appreciated by ecolo-
gists for many years, in the context for example of describing vegetation by means of quadrat
sampling. The rules are
.
Quadrats are placed at random, in other words each possible location within some
category should have an equal chance of being sampled.
.
Accordingly, the scope and boundaries of the category itself need to be defined. Is it
a particular patch of forest that is described, or is it all patches of that forest-type
within a continent? As well as conceptual decisions about the scope of the category,
there usually need to be some exclusion rules—perhaps sites are not sampled on cliffs
because of the practical inconvenience, or in the middle of forest tracks because these
are not thought interesting.
.
There needs to be some replication of the samples, to give a sense of the range of
variation. I will not digress to discuss different indices of the range of variation;
biometry texts provide recipes for calculating these and discuss their merits and
assumptions. The point is only that all recipes require replication, because the logic
by which we generalize about a category requires some indication of the range of
variation within the category.
Francisco Pugnaire/Functional Plant Ecology 7488_C023 Final Proof page 686 2.5.2007 10:29am Compositor Name: BMani
686 Functional Plant Ecology
These rules come into play during the design of a sampling procedure. Generalization
rules forge the link between the sampling procedure and the scope of the generalizations
that can subsequently be inferred. The design of the study is shaped by contemplating
what conclusions one might be seeking to draw. The decision what category to sample,
together with the exclusion rules, draws boundaries on the conclusions that can be drawn.
The legitimacy of the conclusions depends on the equal-chance-of-being-sampled principle,
and the replication allows the strength of the conclusions to be assessed.
APPLICATION OF THE INFERENCE RULES TO GENERALIZATION ACROSS
SPECIES—AN ISSUE THAT HAS BEEN UNDERESTIMATED
In contexts such as vegetation sampling and manipulative field experiments, the inference
rules have been thoroughly assimilated into the practices of ecological researchers. All the
more remarkable, then, that choice of species to study still seems not to be perceived as a
sampling problem where the rules for generalization need to be applied. Some researchers
may quite legitimately argue that they have chosen particular species because they are
interested just in them, they have no interest in generalizing. Alternatively they may believe
the mechanism they are studying is universal, so it does not matter in which species they study it.
However, many studies of the functional ecology or ecophysiology of plants seek to interpret
their results by references to categories of plants—shade-tolerant versus pioneer, low versus
high altitude, annual versus perennial, inhabitants of low-nutrient versus more fertile soils,
rare and endangered versus widespread, and constitutively slow-growing versus capable of
rapid growth—in other words, the species are to be interpreted as samples from some
category. Nevertheless, these studies rarely address explicitly the representativeness
and replication criteria. If the inference rules were followed, the methodology of these
papers would first define categories of species—listing all the low-nutrient species in NSW,
for example—then indicate exclusion rules that had to be applied—for example, only species
available from seed merchants could be considered, and among those, some had to be
eliminated because they could not be persuaded to germinate in reasonable numbers within
a few days of each other—and then within those boundaries, would confirm that the species
studied were chosen at random. However, in reality, most papers in comparative ecophysiology
hardly comment at all on the choice of species, and it is quite common for two categories to be
compared by means of two species, with no replication. Imagine if a manuscript were
submitted in which two vegetation types were compared by means of one quadrat in each,
placed at the location the investigator thought most representative or convenient?—it would
get short shrift from referees and editors. However, many papers that have been successfully
published and are respectfully cited in comparative ecophysiology do exactly this with regard
to choice of species.
How have these differences in research culture, in expectations about sampling, come
about? Probably one factor has been the physiologist’s expectation that truly interesting
processes occur reliably in whatever species is studied. Surely the other main factor must
have been the sheer difficulty and laboriousness of some types of measurement. Remember
also that for an investigator seeking to dissect mechanisms, there are always strong incentives
to measure more processes and at more frequent intervals within one species, rather than to
extend to further species. Consider investigations of complete carbon budgets for example,
including dissecting root respiration into growth and maintenance components. Within the
framework of a Ph.D., it is unreasonable to expect more than a couple of species to be
studied. Then since there are too few species for replication within categories, it must seem
unimportant to apply the equal-chance rule within each category. There are also strong
reasons to use species where seeds are available and are known to germinate readily,
Francisco Pugnaire/Functional Plant Ecology 7488_C023 Final Proof page 687 2.5.2007 10:29am Compositor Name: BMani
Generalization in Functional Plant Ecology 687
which can lead to a few species used repeatedly by successive investigators. So in summary,
the tradition of using few species and those not chosen according to explicit rules of sampling
is entirely understandable, within ecophysiology and indeed in other areas such as genetics
and demography of rare species.
Nevertheless, subsequent literature review or meta-analysis has to face the issue: is species
coverage in the primary research reports representative, and of what categories? As more and
more primary research reports accumulate, this issue is becoming more pressing. One way of
defining the problem in species selection is to say that species should be selected to make
better generalizations possible to the person who comes along 5–10 years after and writes the
literature review.
How might this come about? At one extreme could be imagined a grand overall species-
selection design, decided by a well-meaning dictator or committee. Each lab worldwide would
be then instructed which species to select. I feel confident this is not going to happen (and
indeed should not be allowed to, because the slackening of competitive energy would surely
outweigh the benefits of coordination). At the other extreme, and far more likely, is that
primary research publications move toward discussing their species selection explicitly, in
relation to the inference rules. Explicit discussion could be very valuable. Even when there can
be little replication, and when many exclusion rules have been required so that there is only a
small choice of species, compilers of literature reviews will nevertheless be better placed if the
primary studies have spelled out their species-selection criteria explicitly. Related to explicit
discussion, authors should be encouraged to make available as much background informa-
tion as possible about their species. By background, I mean information that is not referred to
in the paper for the purpose of proving a conclusion, but that might subsequently be useful
to reviewers or meta-analysts seeking to investigate different questions. The limited page-
space in journals need no longer discourage authors and editors from reporting background
information about traits and habitats of the study species, since such material can now be
placed at websites.
CRITERIA ON WHICH TO COMPARE SPECIES
If species-sampling is one side of a coin, the other side is categories into which species are
grouped for comparison. Criteria on which species might be compared fall under three main
headings:
.
Habitat
.
Attributes of the species themselves—ecological strategies
.
Phylogeny (or in practice, usually taxonomy)
The distinction between primary research reports and reviews or meta-analyses is important
here also. Individual research groups can and do categorize species according to whatever
criteria they think meaningful for the question in hand. However, at the stage when
knowledge is consolidated across many primary reports, the reviewer can use only categor-
ies that were adopted in common by all the primary reports, or alternatively that are simple
enough for species to be attributed to them by the reviewer. As a consequence, most reviews
group species into rather simple categories: herbaceous versus woody, deciduous versus
evergreen, temperate versus tropical and so forth. For categories to be used at review that
captured a subtler degree of difference between species, consensus on the categorization
scheme would need to be achieved in advance. Developing wider consensus on more
expressive categorization schemes is thus a major priority for improved generalization in
functional ecology.
Francisco Pugnaire/Functional Plant Ecology 7488_C023 Final Proof page 688 2.5.2007 10:29am Compositor Name: BMani
688 Functional Plant Ecology
HABITAT
I will comment only very briefly on habitat descriptors. Rainfall and temperature are not,
nowadays, too much of a problem to attribute to plant species. Maps or climate-interpolating
software can produce estimates of yearly means or various seasonal patterns or extremes, for
a given location where a species is known to occur. Shading under canopies used often to be
described in qualitative categories, but measurements on an absolute scale of PAR have
become more typical over the last couple of decades, and have been important in clarifying
seedling performance in the shade.
Soil nutrients remain an unresolved problem. Many papers have compared species from
infertile versus fertile soils, within particular landscapes. However, synthesis across these
papers is very difficult because they do not share common measurements that describe soil
fertility of the sites where the species is successful. There is some reason to believe that
relatively fertile soils in Australia might fall into an infertile category in northwest Europe,
for instance, and this complicates any attempt to relate Australian to European studies.
Certainly there are many complexities in measuring soil fertility, but still, it would be a step
forward if even one or two lowest-common-denominator measures could be agreed.
TRAITS OF THE SPECIES THEMSELVES—ECOLOGICAL STRATEGY SCHEMES
The literature on plant ecological strategy schemes can be summarized into three main
strands of thinking. One strand categorizes species by reference to distribution (realized
niche) on one or more gradients, for example, Dyksterhuis (1949) for grazing, Noble
and Slatyer (1980) for time after disturbance, Ellenberg (1988) for soil and other habitat
features. A second strand categorizes species according to physiognomy (e.g., Raunkiaer
1934, Dansereau 1951, Mueller-Dombois and Ellenberg 1974, Box 1981, Sarmiento and
Monasterio 1983, Barkman 1988, Orshan 1989, Prentice et al 1992) and has been active
especially within plant geography. In a third strand axes or categories are named according
to concepts (as distinct from naming them according to traits or realized niche).
Examples include the r-K spectrum (Cody 1966, MacArthur and Wilson 1967) and several
schemes that have developed this spectrum into a three-cornered arrangement (Greenslade
1972, 1983, Grime 1974, Whittaker 1975, Southwood 1977). The three-cornered schemes add
a category of opportunities where the physical environment permits only slow acquisition of
resources. This situation is called stress in Grime’s CSR triangle (1974, 1979, Grime et al
1988), the best developed three-cornered scheme for plants. The CSR triangle has two
dimensions, the C–S axis reflecting adaptation to opportunities for rapid growth versus
continuing enforcement of slow growth (Competitors to Stress-tolerators), the R-axis reflecting
adaptation to disturbance (Ruderals).
Among conceptual strategy schemes the CSR triangle is the most widely cited in text-
books (e.g., Cockburn 1991, Colinvaux 1993, Begon et al 1996, Crawley 1996, Ingrouille
1992), reflecting wide acceptance that exploiting opportunities for fast versus slow growth,
and coping with disturbance, are two of the most important forces shaping the ecologies of
plants within landscapes. Yet papers in functional ecology do not routinely report CSR axis
scores for the species studied, for the reason that there is no explicit quantitative protocol for
scoring a species from anywhere worldwide (see qualitative and partly subjective keys
in Grime 1984, Grime et al 1988). In other words, the CSR scheme is widely cited for
conceptual discussion but not widely adopted for practical comparisons. The only general-
purpose scheme that has been widely adopted is Raunkiaer’s life-form categorization (1907,
English translation 1934), based on the location of the buds where regrowth arises after the
unfavorable season of the year. Raunkiaer life-forms are very easily attributed for most
Francisco Pugnaire/Functional Plant Ecology 7488_C023 Final Proof page 689 2.5.2007 10:29am Compositor Name: BMani
Generalization in Functional Plant Ecology 689