modified whittaker plots as an assessment and monitoring tool for vegetation in a lowland tropical...

MODIFIED WHITTAKER PLOTS AS AN ASSESSMENT ANDMONITORING TOOL FOR VEGETATION IN A

LOWLAND TROPICAL RAINFOREST

PATRICK CAMPBELL1, JAMES COMISKEY1, ALFONSO ALONSO1,FRANCISCO DALLMEIER1, PERCY NUÑEZ2, HAMILTON BELTRAN3,SEVERO BALDEON3, WILLIAM NAURAY2, RAFAEL DE LA COLINA2,

LUCERO ACURIO2 and SHANA UDVARDY1

1 Smithsonian Institution Monitoring and Assessment of Biodiversity Program, ConservationalResearch Center, National Zoological Park, 1100 Jefferson Drive, SW, Suite 3123, Washington, DC,20560-0705, U.S.A.; 2 Departmento de Botanica, Facultad de Ciencias Biologicas, Universidad SanAntonio Abad del Cusco, Cusco, Peru; 3 Museo de Historia Natural, Universidad Nacional Mayor

de San Marcos, Avenida Arenales 1256, Jesus Maria, Lima, Apartado 14–0434, Lima, 14 Peru

Abstract. Resource exploitation in lowland tropical forests is increasing and causing loss of biod-iversity. Effective evaluation and management of the impacts of development on tropical forestsrequires appropriate assessment and monitoring tools. We propose the use of 0.1-ha multi-scale,modified Whittaker plots (MWPs) to assess and monitor vegetation in lowland tropical rainforests.We established MWPs at 4 sites to: (1) describe and compare composition and structure of the sitesusing MWPs, (2) compare these results to those of 1-ha permanent vegetation plots (BDPs), and(3) evaluate the ability of MWPs to detect changes in populations (statistical power). We recordedmore than 400 species at each site. Species composition among the sites was distinctive, while meanabundance and basal area was similar. Comparisons between MWPs and BDPs show that they recordsimilar species composition and abundance and that both perform equally well at detecting rarespecies. However, MWPs tend to record more species, and power analysis studies show that MWPswere more effective at detecting changes in the mean number of species of trees ≥10 cm in diameterat breast height (dbh) and in herbaceous plants. Ten MWPs were sufficient to detect a change of 11%in the mean number of herb species, and they were able to detect a 14% change in the mean numberof species of trees ≥10 cm dbh. The value of MWPs for assessment and monitoring is discussed,along with recommendations for improving the sampling design to increase power.

Keywords: assessment, modified Whittaker plots, monitoring, tropical forests

1. Introduction

Resource exploitation by ever increasing human populations is leading to thehighest rates of loss in Earth’s biodiversity ever recorded (Whitmore, 1997). Threatsto biodiversity stemming from exploitation of tropical forests are particularly dis-tressing. Tropical lowland forests, which harbor most of the world’s biodiversity(Lovejoy, 1997), are being cleared at alarming rates. In South America, for ex-ample, tropical forest cover declined by 23 277 000 hectares (ha) a loss of 2.7%

Environmental Monitoring and Assessment 76: 19–41, 2002.© 2002 Kluwer Academic Publishers. Printed in the Netherlands.

20 P. CAMPBELL ET AL.

from 1990 through 1995 (FAO, 1997). This decline in forest cover is inevitablyleading to loss of wildlife habitat and, ultimately, loss of biodiversity (Myers,1986). The need for sound scientific data regarding the distribution, abundanceand dynamics of populations of organisms has never been greater.

Assessment and monitoring programs provide a means to obtain this data, andadaptive management principles provide the framework within which managerscan make appropriate decisions regarding the use of forest resources (Spellerberg,1992; Dallmeier and Comiskey, 1998). Under adaptive management principles,clear research objectives are established. Assessment and monitoring are the meansto evaluate and meet the objectives. The assessment provides baseline data relev-ant to the site of interest and should consist of field surveys, inventories (includ-ing identification and classification of species) and literature reviews (Spellerberg,1991; Dallmeier and Comiskey, 1998). Monitoring is the repeated measuring andsampling of species over time and comparing the results to the baseline, notingany deviation from an expected norm (Hellawell, 1991). Thus, assessment andmonitoring act as early warning tools to determine forest conditions, formulateadditional research hypotheses and, most importantly, measure progress toward orsuccess at meeting objectives. In the end, the evidence exists to adapt managementstrategies and actions, continue them or cease altogether (Holling, 1978; Dallmeierand Comiskey, 1998; Elzinga et al., 1998).

Biologists use a variety of methodologies to assess vegetation, and they havemade attempts to standardize techniques at national and global scales (Dallmeierand Comiskey, 1996; Ashton, 1998). This allows cross-site comparisons to increaseour understanding of taxonomic distributions (Terborgh and Andresen, 1998) andtrends (Phillips and Miller, in press). Common methods may include simple col-lections, which provide a checklist of species present, or more systematic methodssuch as plots or transects, which provide quantitative information and thus arevaluable components of both assessments and monitoring.

Transects have been used extensively to assess vegetation. They are often es-tablished along obvious environmental gradients, the point being to describe max-imum variation over the shortest distance in a minimum amount of time (Kentand Coker, 1992). Gentry (1982, 1988a, 1995) developed a transect method thathe used to characterize the diversity of vegetation throughout the tropics. Gentry’sapproach allows for the collection of quantitative floristic data from a wide rangeof sites using consistent protocols, which enables comparisons of tropical forestbiodiversity along latitudinal and rainfall gradients (Gentry, 1982, 1995; Clinebellet al., 1995). A disadvantage is that most transects miss important patches (orhotspots) of diversity (Stohlgren et al., 1997) and are not permanent which preventsthe collection of temporal data.

Permanent plots, which allow researchers to follow a population of markedtrees over time, offer the greatest potential as a long-term monitoring tool. Acommon method is the 1-hectare (ha) biodiversity plot (BDP; Dallmeier, 1992)used by the Smithsonian Institution’s Monitoring and Assessment of Biodiversity

MODIFIED WHITTAKER PLOTS AS AN ASSESSMENT AND MONITORING TOOL 21

Program (SI/MAB). Initially, a BDP provides an assessment of the species presentin a particular habitat type, including an inventory of tree species and a detaileddescription of the structure of the forest (Dallmeier and Alonso, 1997; Alonso andDallmeier, 1998; Comiskey et al. 2001). These plots are recensused periodically,lending a dynamic aspect to the survey. By tracking the history of known indi-viduals, managers can observe changes in the forest’s structure and compositionthrough measurement of mortality, recruitment, changes in relative abundance andgrowth rates. They can also investigate species replacement patterns and ultimatelymake predictions about future forest composition (Hubbell and Foster, 1987).

Biologists have also established a worldwide network of permanent 50 ha plots(Condit, 1995; Ashton, 1998). Most species are rare in tropical forests (Hubbell andFoster, 1986), and to obtain valuable information on the dynamics of a wide rangeof species, it is necessary to sample larger areas and size classes (Clark and Clark,1992). Such large sample sizes provide a unique experimental site for examiningtheories on the maintenance of species diversity (Hubbell, 1998) and monitoringcommunity change at the species level in response to climatic fluctuations (Con-dit et al., 1995). However, these large plots can be labor intensive and costly tomaintain (Hubbell and Foster, 1992).

BDPs and the 50 ha plots are typically established as single sampling unitsbecause of time and cost constraints. The lack of replication means that they failto detect spatial variability over larger scales (Condit, 1995), and this makes itdifficult to draw conclusions about the changes in an entire region. Furthermore,the plots fail to indicate whether what was measured is typical of the surroundinghabitat (Stohlgren et al., 1997).

Modified Whittaker Plots (MWPs) were developed by Stohlgren et al. (1995),based on the original nested vegetation sampling method developed by R. H. Whit-taker and described by Shmida (1984). These small, 0.1 ha, multi-scale plots allowinvestigators to examine the temporal and spatial aspects of vegetation at a perman-ently marked site. Shmida (1984) used ‘Whittaker’ plots to record species richnessdata at multiple spatial scales to investigate species accumulation with increasingarea. Stohlgren et al. (1995) modified this design to reduce the autocorrelation ofthe nested plots that was inherent in the original design and found that the modifieddesign was more effective at estimating species/area relationships compared to theolder design. A series of permanent MWPs at an assessment and monitoring sitemay provide the temporal and spatial data necessary for an effective monitoringprogram.

For any monitoring program to be successful, the methods used must be care-fully evaluated. Since an objective is to detect changes in population parameters,we need to test the effectiveness of potential sampling designs and efforts to detectsuch change. For this reason, Type II statistical errors are of concern to thoseinvolved in monitoring biodiversity. A Type II error is when a researcher failsto reject the null hypothesis when, in fact, it is false or when a researcher failsto detect a change when, in fact, it did occur. Statistical power analysis (Cohen,


1977) provides a means to investigate the probability of correctly rejecting a nullhypothesis that is false (Steidl et al., 1997). During the early phases of a monitoringprogram, power analysis can provide insight into the effort necessary to achievea sufficient level of power or to determine the probability that the effect size ofinterest will be detected with a certain sample size. This is known as prospectivepower analysis (Peterman, 1990).

In 1996, SI/MAB undertook a study of biodiversity in the Lower Urubambaregion of southeast Peru. At the time, SI/MAB was working jointly with ShellProspecting and Development, Peru (SPDP) in a unique venture aimed at achievingenvironmentally responsible development of natural gas and condensate resources(Dallmeier and Alonso, 1997; Alonso and Dallmeier, 1998, 1999). As part of theassessment of vegetation, we established 11 1-ha BDPs in 3 vegetation types inthe region (Comiskey et al., 2001). We also established sets of 10 MWPs aroundeach of 4 of the BDPs to evaluate spatial variability in species composition. Tothe best of our knowledge, this represents the first published use of MWPs in atropical forest ecosystem. In this article, we primarily focus on the use of MWPsas assessment and monitoring tools based on 3 objectives: (1) describe and comparecomposition and structure of the 4 sites using MWPs, (2) compare the results ofMWPs to those of the BDP methodology (for example, number of individuals,total basal area and number of species) and (3) evaluate the ability of MWPs todetect changes in populations (statistical power). The project was not designed asan experiment with hypotheses for testing. Still it allowed us to investigate thevalue and uses of MWPs.

2. Study Area

The study was conducted in the Peruvian Amazon, one of the most biologicallydiverse regions in the world (Gentry, 1988b, 1990). The study area, approximately20 × 30 kilometers (km), is situated at 12◦S latitude and 73◦W longitude betweenManu National Park and the Apurimac Reserve Zone. The rugged terrain is charac-terized by a series of plateaus separated by steep sloping hills that vary in elevationfrom approximately 300 to 600 meters (m). A detailed description of the study areacan be found in Dallmeier and Alonso (1997).

The dominant vegetation of the area is lowland tropical rainforest. Initially,4 sites near gas drilling operations were chosen for assessment and monitoring:San Martin-3 (Sanm-3), Cashiriari-2 (Cash-2), Cashiriari-3 (Cash-3) and Pagoreni(Pag). Each was located in old-growth terra firme forest (sensu Prance, 1989), ahabitat common in lowland neotropical rainforest. Terra firme forest is an uplandhabitat described as having high species richness and an abundance of large-staturetrees (Campbell et al., 1986; Gentry, 1988b; Korning et al., 1991; Valencia etal., 1994; Comiskey et al., 2001). Species composition was similar among the


sites and all were dominated by the arborescent palm Iriartea deltoidea. Naturaldisturbances were common in the area (Comiskey et al., 2001).

Temperatures varied little across the study region and throughout the year, witha mean of about 24◦ C, and relative humidity typically exceeded 80%. Rainfallaveraged between 3000 and 3500 millimeters a year and occurred mostly duringthe wet season, October through April. (Alonso and Dallmeier, 1999).

3. Methods

3.1. FIELD METHODS

In 1998, we established 4 permanent 100 × 100 m BDPs in four nonadjacent unitsof mature terra firme forest. The BDPs were subjectively placed in what botanistsviewed as the most homogeneous part of the habitat. Because of rugged terrain,accessibility was also a consideration. Plots were established according to Dall-meier (1992). The process included locating, measuring, marking and mappingall trees with a diameter at breast height (dbh) ≥4 centimeters (cm); dbh wasmeasured immediately above the tree buttress when present. Further details alongwith comprehensive results for all plots are described in Comiskey et al. (2001).Voucher specimens for all species recorded in this study have been deposited atthe Museo de Historia Natural, Universidad Nacional Mayor de San Marcos inLima, Peru and the Smithsonian Institution, National Museum of Natural History,Washington, DC.

Surrounding each of the BDPs within the same habitat unit and between 50 and300 m from the well site, we randomly established 10 0.1-ha MWPs for a total of40 MWPs. At each well site, 4 trails were cut into the forest extending out fromthe well site in the cardinal directions. To identify a plot site, we first randomlyselected a trail, then randomly selected a distance between 50 and 300 m alongthe trail, a random direction between 1 and 360◦ and then a random distance fromthe trail in that direction between 0 and 100 m. Plots were oriented with the longaxis in the east-west direction. MWPs could not be located within or overlap theboundaries of a BDP, they could not be placed on an incline of more than 50◦ andthey had to be within a similar vegetation type as that for the BDP.

The MWPs (Figure 1) followed the configuration of Stohlgren et al. (1995).Each consisted of fourteen multi-scale, rectangular, nested subplots of consist-ent proportional dimensions, which were corrected for slope. All trees ≥10 cmdbh (large trees) were measured, marked and identified throughout the 0.1 ha(50 × 20 m) MWP (D). In the center 20 × 5-m subplot (C), all trees ≥5 cm dbh(medium trees) were measured, marked and identified. In a similar fashion, alltrees ≥1 cm dbh (small trees) were measured in the two 5 × 2-m subplots (B1 andB2). All individual stems of herbaceous plants (herbs) were identified and counted,but not marked, in the 10 2 × 0.5-m subplots (A). For further details see Alonso etal. (1999).


Figure 1. Diagram of modified Whittaker plot and subplot establishment.

3.2. DESCRIPTIVE AND COMPARATIVE DATA ANALYSIS

For each set of 10 MWPs, total number of individuals, total basal area and totalnumber of species for each size class were generated. In addition, for each para-meter the mean values per MWP and standard errors (SE) were estimated. Total andmean number of individuals and total and mean basal area can also be generated foreach species, but these results are not presented in this manuscript. Means amongthe four sets of MWPs were then compared using one-way analysis of variance(ANOVA).

We used detrended correspondence analysis (DCA; Hill and Gauch, 1980) toexamine the similarity in the species composition of the sampling plots. Speciespresence and absence data for each individual MWP and BDP were used in theanalysis. Because of possible effects caused by discrepancies in size between indi-vidual MWPs and BDPs, we also conducted a DCA using cumulative presence andabsence data for each set of MWPs together with individual BDPs in the analysis.

Species Importance Values (SIV) were used to describe the ten most dominantspecies in each BDP and each set of MWPs. The SIV is equal to the sum of therelative density (total individuals of a species/total number of individuals of allspecies), the relative basal area (total basal area of a species/total basal area of allspecies) and the relative frequency (number of plots in which a species occurs/totalnumber of plots sampled) for each species. Relative frequency was equal to 1 incalculations for the BDPs. Species were then ranked according to SIV, and thespecies with the highest SIV was considered the most ‘important’ in the plot.

3.3. STATISTICAL POWER ANALYSIS

We used prospective statistical power analysis (Cohen, 1977), to investigate theeffectiveness of MWPs in detecting changes in mean number of species and mean


TABLE I

Total and mean number of species and standard deviation for 10 0.1-ha modified Whittaker plots(MWP) by size class in the Lower Urubamba region, Peru (size classes based on diameter at breastheight [dbh], measurements in centimeters, means compared by One-Way Analysis of Variance[ANOVA])

Site Total Mean (Standard Deviation) per MWP by size class (dbh) Total

≥10 cm ≥10 ≥5, <10 ≥1, <5a, b Herbaceousa, c all size

dbh classes

Cashiriari-2 198 43.3 (5.6) 9.1 (3.0) 8.1 (2.0) 53.6 (5.4) 419

Cashiriari-3 183 36.3 (5.1) 6.5 (3.6) 6.2 (3.6) 49.0 (8.0) 403

Pagoreni 234 43.0 (7.6) 7.5 (3.1) 11.6 (4.9) 68.2 (8.2) 509

San Martin-3 192 40.1 (7.0) 8.7 (4.4) 9.0 (4.1) 72.2 (13.4) 435

a p < 0.05 (ANOVA).b Pairwise comparisons based on Bonferroni ad hoc test: Pagoreni > Cashiriari-3.c Pairwise comparisons based on Bonferroni ad hoc test: Pagoreni > Cashiriari-2 and Cashiriari-3.San Martin-3 > Cashiriari-2 and Cashiriari-3.

number of individuals. There are 5 parameters of interest when examining stat-istical power: sample size, or number of plots (n); effect size, or the degree ofchange we want to detect (ES); a measure of within-sample variability (σ 2); andthe probabilities of Type I (α) and Type II (β) errors. In this study, n = 10 in all casesand variance was estimated from initial study results. We also used statistical poweranalysis to investigate the effectiveness of MWPs to detect changes in populationparameter estimates for the single species Iriartea deltoidea in the ≥10 cm dbhsize class and Philodendron guttiferum in the herb class. I. deltoidea was chosenbecause it was the most abundant tree in this class at all sites, while P. guttiferumwas the most common species in the herbaceous layer throughout the Cash-2 siteand thus provided adequate data. Statistical power analyses were done on data fromthe Cash-2 site only. We used PC-SIZE: Consultant software version 1.02 (Dallal,1990) to calculate number of plots necessary to achieve selected effect sizes and tocalculate statistical power based on ES.

4. Results

4.1. SPECIES RICHNESS

The initial assessment of the vegetation in the Lower Urubamba region supportsthe premise that the forests of western Amazonia are among the most species richon Earth. At each of the 4 sites, MWPs recorded more than 400 species. The mostspeciose site was Pag, which recorded 509 total species and 234 species of largetrees in 1 ha of total sampling area (Table I). Mean species richness for large and


TABLE II

Total and mean number of individuals and standard deviation for 10 0.1-ha modified Whit-taker plots (MWP) by size class in the Lower Urubamba region, Peru (size classes based ondiameter at breast height [dbh], measurements in centimeters, means compared by One-WayAnalysis of Variance [ANOVA])

Site Total Mean (Standard Deviation) per MWP by size class (dbh)

≥10 cm dbh ≥10 ≥5, <10 ≥1, <5a, b Herbaceousa, c

Cashiriari-2 657 65.7 ( 9.8) 10.3 (3.2) 8.5 (2.7) 119.8 (28.5)

Cashiriari-3 608 60.8 ( 5.6) 7.6 (4.3) 6.7 (3.9) 138.1 (37.3)

Pagoreni 588 58.8 (12.4) 8.7 (3.9) 13.2 (5.5) 193.5 (44.1)

San Martin-3 647 64.7 (14.3) 11.7 (6.1) 9.8 (4.0) 214.2 (54.5)

a p < 0.05 (ANOVA).b Pairwise comparisons based on Bonferroni ad hoc test: Pagoreni > Cashiriari-3.c Pairwise comparisons based on Bonferroni ad hoc test: Pagoreni > Cashiriari-2 andCashiriari-3. San Martin-3 > Cashiriari-2 and Cashiriari-3.

TABLE III

Total and mean basal area (m2/ha) and standard deviation for 10 0.1-ha modified Whittaker plots(MWP) by size class in the Lower Urubamba region, Peru (size classes based on diameter atbreast height [dbh], measurements in centimeters, means compared by One-Way Analysis ofVariance [ANOVA])

Site Total Mean (Standard Deviation) per MWP by size class (dbh)

≥10 cm dbh ≥10 ≥5, <10 ≥1, <5a, b

Cashiriari-2 29.7 3.0 (0.6) 0.04 (0.02) 0.004 (0.002)

Cashiriari-3 39.7 4.0 (0.7) 0.03 (0.02) 0.003 (0.002)

Pagoreni 30.1 3.0 (1.4) 0.04 (0.02) 0.006 (0.003)

San Martin-3 33.1 3.3 (1.0) 0.04 (0.02) 0.004 (0.002)

a p < 0.05 (ANOVA).b Pairwise comparisons based on Bonferroni ad hoc test: Pagoreni > San Martin-3.

medium trees was similar among the 4 terra firme sites, although Pag tended to bericher in small trees while both Pag and Sanm-3 tended to be richer in herbs (p <0.05, ANOVA); (Table I).

4.2. FOREST STRUCTURE

The 4 sites were structurally similar as well. Total abundance of large trees for eachset of MWPs (total area per set = 1 ha) ranged from 588 to 657, while total basalarea varied from 29.7 to 39.7 m2 ha−1. There were no differences among sites (p >0.05, ANOVA) in the mean number of individuals or mean total basal area betweenthe 2 largest size classes (Tables II and III). However, Pag and Sanm-3 had a greater


TABLE IV

Summary results for the composition and structure of trees ≥10 cm diameter at breast heightin 4 1-ha biodiversity plots (BDP), and the total accumulation for corresponding sets of 100.1-ha modified Whittaker plots (MWP) in the Lower Urubamba region, Peru (source data forBDP from Comiskey et al., 2001)

Site # of individuals Total basal area (m2/ha) # of species

BDP MWP BDP MWP BDP MWP

Cashiriari-2 592 657 34.9 29.7 155 198

Cashiriari-3 564 608 28.6 39.7 159 183

Pagoreni 575 588 27.8 30.1 185 234

San Martin-3 481 647 22.2 33.1 138 192

mean abundance of individuals than the other 2 sites among small trees and herbs(Tables II and III).

Modified Whittaker Plots and BDPs revealed similar patterns in the structureof the vegetation. Direct comparisons between the 2 methods were restricted tolarge trees (dbh ≥10 cm) because this is the only category where both methodscollected the same data on the same scale; that is, number of species, number ofindividuals and total basal area per 1 ha. In all cases, MWPs recorded more species,more individuals and greater total basal area for trees of the same size over thesame area than the BDPs (Tables I–IV). However, both methods described Pag asbeing the most speciose and Cash-2 as having the highest abundance of individuals(Tables I–IV). Results for BDPs are reported in more detail in Comiskey et al.(2001).

4.3. ORDINATION

Similarity of species composition among all plots based on the results of the DCAusing species presence and absence data is shown in Figures 2 and 3. Plots thatare closer together on the graph are more similar in species composition and viceversa. When all 40 MWPs and 4 BDPs are analyzed individually (Figure 2), wecan see that each set of 10 MWPs were distinctive and tended to be more similar toeach other than to any of the other sets. And in all cases, except for Pag, the speciescomposition of the BDPs was closely related to the corresponding set of MWPs.Because of differences in area and because the environmental gradients crossedby the 2 plot designs were probably not the same between individual MWPs andBDPs, there may have been erroneous results. So we also analyzed the data usingthe combined species composition for each set of MWPs versus each BDP. In thiscase, the DCA again revealed that the species composition of the BDPs was closelyrelated to the corresponding set of MWPs, except for Pag (Figure 3).


Figure 2. Results of a Detrended Correspondence Analysis for 40 0.1-ha modified Whittaker plots(MWPs) and 4 1-ha biodiversity plots based on species presence/absence data for trees ≥10 cm,diameter at breast height, in the Lower Urubamba region, Peru. Filled shapes represent BDPs, openshapes represent MWPs. Eigenvalue for Axis 1 = 0.43, eigenvalue for Axis 2 = 0.35.

4.4. COMMONNESS AND RARITY

Species richness was high, and many species were rare. At all sites, 40% or moreof all species among all size classes encountered were found in only 1 MWP, andfewer than 13% were found in more than 5 MWPs (Figure 4). There was also alarge proportion of species that were represented by a single individual. Amongall size classes, 34% of the species encountered in the MWPs were representedby a single individual, and nearly half the species ≥10 cm dbh were singletons(Table V). A similar pattern was apparent in the BDPs where at least 41% of species≥10 cm dbh were represented by a single individual.

Because rarity is common, we would not expect much overlap in species com-position among sites. Therefore, it may be of more value to examine and compareonly important species (high SIV) at each site (Table VI). The two most importantspecies in each of the BDPs were always among the most important in the MWPs.The large palm I. deltoidea was the most important species ≥10 cm dbh in thestudy. MWPs ranked it the most important species at 3 sites and second at Pag,while BDPs ranked it as most important at all 4 sites. In addition to I. deltoidea,


Figure 3. Results of a Detrended Correspondence Analysis comparing species presence/absence datafor trees ≥10 cm, diameter at breast height at 4 sites in the Lower Urubamba region, Peru. At eachsite, species composition was recorded in 10 0.1-ha Modified Whittaker Plots (open symbols) and4 1-ha biodiversity plots (filled symbols). Data from each set of 10 modified Whittaker plots wassummed and total area therefore represents 1 ha. Eigenvalue for Axis 2 = 0.33.

TABLE V

Number of species and proportion of total represented by 1 individual ineach of 4 1-ha biodiversity plots (BDP) and 4 sets of 10 0.1-ha modifiedWhittaker plots (MWP) at 4 sites in the Lower Urubamba region, Peru(in 1-ha plots, all individuals ≥10 cm diameter at breast height [dbh]; inMWPs, individuals shown as ≥10 cm dbh, all individuals counted)

Site BDP MWP

dbh ≥10 cm dbh ≥10 cm All size classes

Cashiriari-2 74 0.48 100 0.51 177 0.42

Cashiriari-3 65 0.41 89 0.49 158 0.39

Pagoreni 102 0.55 117 0.50 245 0.48

San Martin-3 67 0.49 93 0.48 147 0.34


Figure 4. Proportion of species recorded in a given number of modified Whittaker plots (MWP) at 4sites in the Lower Urubamba region, Peru. Each site is represented by 10 MWPs.

there were other similarities among MWPs and BDPs. Most of the important spe-cies at Cash-2 and Pag were the same in the 2 methods, while 4 species were thesame at Sanm-3; however, only 2 of 10 important species were the same in Cash-3.

4.5. POWER ANALYSIS

Modified Whittaker Plots were most effective in detecting changes in the meannumber of species for large trees and herbs. In our analysis, 10 MWPs would besufficient to detect a change of 11% in the mean number of species of herbs, andthey would be able to detect a 14% change in large trees (α = 0.05, β = 0.20).However, it would be more difficult to detect trends in the 2 middle size classes(Figure 5). Some authors (Elzinga et al., 1998) have suggested increasing α (whichwill increase power) when Type II errors are of concern; in other words, increasingour confidence level if we do not want to miss a significant change. By weakeningour criteria for rejecting a Type I error to α = 0.10, ten MWPs would be sufficientto detect a change of 9% in the mean number of species of herbs, and they wouldbe able to detect an 11% change in large trees. By examining power curves, wesee that the probability of making a Type II error (β = 1 – power) when trying to


TABLE VI

The 10 most important species at each of 4 sites in the Lower Urubambaregion, Peru as determined by modified Whittaker plots (MWP; import-ance based on Species Importance Values, the sum of relative density[total individuals of a species/total number of individuals of all species],relative basal area [total basal area of a species/total basal area of allspecies] and relative frequency [number of plots in which a species oc-curs/total number of plots sampled] for each species; species listed indescending order of importance; also shown, species importance rank ofthe species as determined by 1-ha biodiversity plots [BDP] at each site[Comiskey et al., 2001])

Site Species Rank in BDPa

Cashiriari-2 Iriartea deltoidea 1

Rinorea guianensis 5

Matisia cordata 2

Gustavia peruviana NR

Inga edulis 35

Calatola venezuelana 3

Sapium marmieri 7

Pentagonia parviflora 8

Guarea macrophylla 10

Cecropia engleriana 39

Cashiriari-3 Iriartea deltoidea 1

Otoba parvifolia 71

Bombacopsis sp1 NR

Pterygota amazonica 20

Pseudolmedia laevigata 24

Poulsenia armata NR

Hevea brasiliensis 13

Pseudolmedia laevis 3

Hyeronima alchorneoides NR

Ficus killipii NR

Pagoreni Matisia cordata 4

Iriartea deltoidea 1

Hevea brasiliensis NR

Guarea kunthiana 9

Hura crepitans NR

Pseudolmedia laevigata 18

a NR = Not recorded in the 1-ha biodiversity plot.


TABLE VI

(continued)

Site Species Rank in BDPa

Pagoreni (continued) Guarea pterorhachis 12

Pentagonia parvifolia 2

Socratea salazarii 11

Bauhinia tarapotensis 7

San Martin-3 Iriartea deltoidea 1

Matisia cordata 2

Pentagonia parvifolia 8

Poulsenia armata 3

Otoba parvifolia 19

Lunania parviflora 22

Apeiba membranacea NR

Inga sp1 50

Neea chlorantha 62

Sloanea sp1 26

a NR = Not recorded in the 1-ha biodiversity plot.

detect relatively small changes is low for large trees and herbs, but much higher formedium and small trees (Figure 6).

Modified Whittaker Plots were slightly less effective in detecting changes in themean number of individuals for large trees and herbs. In our analysis, 10 MWPswould be sufficient to detect a change of 25% in the mean number of individualsof herbs, and they would be able to detect a 15% change in large trees (Figure 7);(α = 0.05, β = 0.20). Again, MWPs were less effective at detecting trends in thesmall and medium tree classes. By examining power curves, we see that the prob-ability of making a Type II error (β = 1 – power) when trying to detect relativelysmall changes is low for large trees and herbs, but much higher for medium andsmall trees (Figure 8).

MWPs were least effective in detecting changes in the abundance of individualspecies. The power of MWPs to detect such changes is lower than for the 3 sizeclasses of trees. For example, the population of I. deltoidea trees (mean = 3.5, sd =2.1) could change by 60% and that of P. guttiferum (mean = 5.2, sd = 3.5) by 70%before 10 MWPs would detect the change.


Figure 5. Number of modified Whittaker plots required to detect various amounts of proportionalchange in the mean number of species in each of 4 size classes (classified by diameter at breastheight) present at Cashiriari-2. Effect size is the proportional change in mean number of speciesrelative to the initial estimate. In this case, α = 0.05 and β = 0.20.

5. Discussion

There are 2 critical pieces of information that are necessary when establishingan assessment and monitoring program for vegetation: (1) a comprehensive as-sessment of the species present and (2) an estimate of spatial variability to makecomparisons. In addition, the program should provide some measure of its abilityto detect potential changes in parameters of interest. Our preliminary evaluation ofthe use of MWPs as an assessment and monitoring tool indicate that they are usefulin addressing each of these issues.

5.1. ASSESSMENT

The first step in any long-term monitoring program is the initial assessment. The as-sessment provides the baseline data that allows scientists to identify components ofthe environment in need of monitoring and to set the objectives on which they canbuild the monitoring program. However, it is challenging to obtain these baselinedata in tropical forests where species richness is high (Hubbell and Foster, 1983;Gentry, 1988b) and rarity is the norm (Greig-Smith, 1964; Hubbell and Foster,


Figure 6. Power curves for detecting various proportions of change in mean number of species atCashiriari-2. Species are classified according to size (diameter at breast height). In this case, α =0.05 and n = 10.

1983). Therefore, a technique that best captures the variability in species richnessis preferred. This is important because to detect change we need to have a measureof the natural variability in the sample.

Plot shape and distribution have influence on the number of species sampled.The rectangular shape of the MWPs allows us to detect more of the species presentin an area than other shapes. Rectangular plots sample more species per unit areathan square plots because long sampling units are more likely to cover more di-verse parts of the sampling area and to include species that tend to occur fartherapart (Podani et al., 1993; Stohlgren, 1995; Condit et al., 1996). Furthermore,noncontiguous plots tend to detect more species than contiguous or single plots.This is because plots placed closely together are usually more similar in speciescomposition than plots spaced further apart (Hubbell and Foster, 1983; Stohlgrenet al., 1997). Replicated, rectangular plots spread randomly throughout the studyarea should capture a more representative sample of the variability in the studyarea and thus maximize the number of species detected, which is ultimately theobjective of the assessment phase. In all cases, MWPs recorded more species andmore rare species than their associated square BDP.


Figure 7. Number of modified Whittaker plots required to detect various amounts of proportionalchange in the mean number of individuals in each of 4 size classes (classified by diameter at breastheight) present at Cashiriari-2. Effect size is the proportional change in mean number of individualsrelative to the initial estimate. In this case, α = 0.05 and β = 0.20.

Plot size can also influence the number of species detected and their estim-ated spatial variance. Plots that are too small will under represent large individu-als, while measuring small individuals in large plots can be too time consuming(Kent and Coker, 1994). A nested arrangement, like that used in MWPs, allowsfor sampling of all size classes by using plots that are related to the size of theindividuals being sampled and minimizing the effort involved in sampling the plot(Kent and Coker, 1994; Podani et al., 1993).

The MWPs recorded more individuals over 1 ha than their associated BDPsin all cases (Table IV). This may be an effect of the size and shape of MWPs,but we attribute this outcome to either sampling error in the MWPs or the sub-jective placement of the BDPs. The BDPs tended to be placed on level ground,whereas the MWPs were established randomly across the hilly terrain. In a studythat examined landscape-scale variation in tropical forest structure, Clark and Clark(2000) showed that abundance tended to be higher on slopes than on flat ground.Therefore, the BDPs may be underestimating true abundance. Future studies re-garding methodologies for monitoring of tropical forests should examine this issuein more detail.


Figure 8. Power curves for detecting various proportions of change in mean number of individualsaf Cashiriari-2. Species are classified according to size (diameter at breast height). In this case, α =0.05 and n = 10.

5.2. MONITORING

The purpose of monitoring is to detect changes, if any, from the baseline valuesacquired during the assessment. Detecting changes requires an estimate of spatialvariation in species composition, which is possible through the use of randomlydistributed and replicated plots. A random presentation reduces bias in the results,and through increasing the number of replicates, we obtain a more precise estimateof species composition by decreasing variability in the sample. An estimate ofvariation allows for statistical comparisons between sites spatially and comparis-ons within sites temporally and, ultimately, a measure of change in both speciescomposition and abundance.

The MWPs recorded similar species composition, numbers of individuals andtotal basal area of large trees as did BDPs. The results from this study providea clear example as to the benefits of replicated plots for monitoring. If our studyonly involved non-replicated BDPs, we would have been tempted to conclude thatabundance was higher in Pag and Cash-2 and that basal area was much lowerin Sanm-3 because there was no measure of variability. But abundance estimatesfrom the 4 sets of MWPs show that there is no difference in the mean number of


individuals at these sites. Furthermore, the lower values recorded in Sanm-3 andCash-3 BDPs are well within the range of values obtained at the sample of MWPs.

5.3. POWER ANALYSIS

The objective when monitoring biodiversity is to determine whether a particularpopulation parameter is increasing or decreasing. As examples, the parameter ofinterest may be abundance, canopy cover or species richness. To meet this objec-tive, managers often ask how many samples are necessary to detect a change andwhat is the probability that a change can be detected if it exists. Statistical poweranalysis (Cohen, 1977) is a methodology that provides a means by which managerscan answer these questions.

Type II errors are of particular concern. The failure to reject a null hypothesiswhen in fact it is false, or the failure to detect a trend in the population parameter ofinterest when it exists, could potentially lead to negative and irreversible impactson the species of interest. It is often better to be cautious and assume a change hastaken place even if it may not be real. As a result, many researchers have begun toadvocate the use of power analysis in monitoring (Toft and Shea, 1983; Gerrodette,1987; Steidl et al., 1997; Van Strien et al., 1997).

We examined the power of MWPs using the data from the Cash-2 site. We baseour example on a power of 0.80, a commonly accepted value (Osenberg et al.,1994; Zielinski and Stauffer, 1996). This means that the probability of correctlyrejecting a null hypothesis that is false is 0.80, or alternatively the probability of aType II error is 0.20.

With just 10 MWPs, we were able to detect changes between 10 and 15% inmean number of species of large trees and herbs. The risk of not detecting wasonly 0.20. This has obvious value to those monitoring biodiversity. If the overallbiodiversity in a region is declining, managers will be able to detect this decline atan early stage and can take management measures to mitigate the decline. MWPsdid not work as well for the middle size classes, perhaps because the smallersampling areas do not capture sufficient individuals. However, detecting changes inthe small trees is of critical importance. Change occurs slowly among large maturetrees, and it may take many years to observe a 10% change in this class. But changeoccurs rapidly in the smaller size classes because of higher mortality rates (Hubbelland Foster, 1990; Clark and Clark, 1992). It is important to employ a method thatcan detect change with the same ability. The flexibility of MWPs offers potentialsolutions, one of which is to add additional MWPs. Increasing the sample sizealways leads to a lower probability of both Type I and Type II errors (Cohen, 1977).In addition, MWPs are quick to establish and survey. In the Lower Urubamba, weestablished and surveyed two MWPs per day with a team of 5 experienced people.Stohlgren et al. (1997) report similar speed with smaller crews. To compare, itrequired a crew of 10 people for 10 days to establish a BDP. Thus to double thesample size of MWPs would have taken us less than 3 weeks.


It is not necessary to establish entire MWPs. In our case, it would require 40plots per site to detect subtle changes (15%) in the medium tree class. Rather thanestablishing all of the plots, there are 2 options to obtain a higher statistical power.The first would be to change the sampling criteria of the subplots. One could recordinformation on medium trees in the largest subplots; that is, count trees 5 to 10 cmdbh in the D plots, then reevaluate the results to see if a change in design providesa more precise estimate. Another solution would be to add additional subplotsoutside of the primary MWP. The entire MWP need not be established becausethe sample size is already sufficient to detect acceptable changes in the larger sizeclass. One could add 30 C subplots to reach a sufficient sample size for mediumtrees (Figure 5).

Although MWPs show evidence of having high statistical power when used fordetecting overall trends, they do not seem to have the power to detect changes insingle species. In our example, 10 MWPs would not detect a trend in the totalnumber of I. deltoidea trees until this value changed by 60% or a change in P.guttiferum herbs until the value changed by 70%. In most cases, this level of powerwould be inadequate. Furthermore, both of these species were common throughoutthe area. Our ability to detect changes in rare species would be even less. Othermethods should be used when the objective is to monitor for a single species. Oneis the plotless method used by Clark and Clark (1992) that aims at monitoring arepresentative number of individuals of different sizes for a single species.

6. Conclusions and Recommendations

Stohlgren et al. (1995) suggest that MWPs may be valuable tools for quantifyingand detecting trends in species richness. Our preliminary investigations suggest thatthis is true. MWPs have value as both short-term and long-term tools for assess-ment and monitoring of biodiversity. They are easy to establish and cost effective,yet they yield abundant information, and their flexibility allows them to be easilymodified to fit any situation. They also detect similar patterns in species abundanceand habitat structure as do standardized BDPs. Ultimately, a monitoring programwould benefit from using a combination of MWPs and larger BDPs. MWPs provideinformation on spatial patterns of species, allow for statistical comparisons and canbe used to detect trends in richness over time. BDPs can be used to examine thestructure of forests and population dynamics, and they allow comparisons to otherstudies by using standard protocols. We believe that a network of MWPs wouldbetter enable managers to determine placement of large BDPs. By understandingthe spatial distribution of species and vegetation types, managers would be betterequipped to choose monitoring sites that are most representative of the area inquestion.


Acknowledgements

The authors extend their appreciation to all who helped make this project a positiveexperience including the Smithsonian Institution Monitoring and Assessment ofBiodiversity Program. We especially wish to thank the staff of Shell Prospectingand Development Peru for their assistance, hospitality and enthusiasm and thereviewers of the manuscript for invaluable comments.

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