BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, andresearch funders in the common goal of maximizing access to critical research.

Unraveling the Relationships between Freshwater Invertebrate Assemblages andInteracting Environmental FactorsSource: Freshwater Science, 33(4):1148-1158.Published By: The Society for Freshwater ScienceURL: http://www.bioone.org/doi/full/10.1086/677898

BioOne (www.bioone.org) is a nonprofit, online aggregation of core research in the biological, ecological, andenvironmental sciences. BioOne provides a sustainable online platform for over 170 journals and books published bynonprofit societies, associations, museums, institutions, and presses.

Your use of this PDF, the BioOne Web site, and all posted and associated content indicates your acceptance of BioOne’sTerms of Use, available at www.bioone.org/page/terms_of_use.

Usage of BioOne content is strictly limited to personal, educational, and non-commercial use. Commercial inquiries or rightsand permissions requests should be directed to the individual publisher as copyright holder.

Unraveling the relationships between freshwaterinvertebrate assemblages and interactingenvironmental factors

Anne Pilière1,4, Aafke M. Schipper1,5, Ton M. Breure1,2,6, Leo Posthuma2,7, Dick de Zwart2,8,Scott D. Dyer3,9, and Mark A. J. Huijbregts1,10

1Radboud University Nijmegen, Institute for Water and Wetland Research, Department of Environmental Science, P.O. Box 9010,6500 GL Nijmegen, The Netherlands

2National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands3The Procter and Gamble Company, P.O. Box 599, Cincinnati, Ohio 45202 USA

Abstract: Disentangling the influences of multiple environmental factors on ecosystem integrity is not straight-forward because environmental factors may interact and biotic responses may be nonlinear. We aimed to under-stand better the relationships between freshwater invertebrate assemblages and multiple, interacting environmentalfactors. We analyzed stream monitoring data for 689 sampling sites in the state of Ohio (USA) with Boosted Re-gression Trees (BRTs). We used 16 environmental predictors covering geography, water chemistry, physical-habitatquality, and toxic pressure. We represented freshwater invertebrate assemblages by the Invertebrate CommunityIndex (ICI) and its 10 component metrics. The ICI was mainly related to physical-habitat quality, nutrient con-centrations (P and N), and pH. Responses of the ICI component metrics to physical-habitat quality and water-chemistry variables were similar and were associated with amplified importance of these predictors for the ICI,whereas heterogeneous responses of the component metrics to geographic variables appeared to cancel eachother out at the level of the ICI. Models including predictor interactions explained 22 to 54% of the deviationin the biotic endpoints, whereas the no-interactions models explained 14 to 47%. The gain in predictive powerwas largest between the no- and the pairwise interaction models and decreased rapidly for each additional in-teraction level. We conclude that a focus on pairwise interactions is a good compromise between higher predictivepower and interpretability of the results.Key words: benthic macroinvertebrates, predictor interactions, Boosted Regression Trees, variable aggregation,multimetric index, streams, Ohio

Stream biotic assemblages are shaped by a variety of nat-ural and anthropogenic factors originating from the land-scape through which the stream flows (Allan 2004). Therelationships between biotic assemblages and anthropo-genic factors are used in bioassessments, wherein the en-vironmental quality of a site is judged from the species itsupports. Benthic macroinvertebrates have a long historyof use as bioindicators of stream quality, and several bio-assessment indices have been derived from the structureand composition of invertebrate assemblages (e.g., Barbouret al. 1996, Wallace et al. 1996, Norton et al. 2000, Klemmet al. 2002, Ogbeibu and Oribhabor 2002). Multimetricindices, such as the Invertebrate Community Index (ICI;Ohio EPA 1987), typically are based on the responses of

various taxonomic or functional groups to various stress-ors or along a stressor gradient. The deviation of thosemetrics from values observed in desired conditions (ref-erence sites) is quantified, and the results are aggregatedinto an index expressing overall community integrity (OhioEPA 1987, Applegate et al. 2007, Weigel and Dimick 2011,Verdonschot et al. 2012).Reducing or avoiding loss of biotic integrity requires

knowledge of the relationships between environmental fac-tors and biotic integrity. This information helps managersunderstand environmental impacts, identify major risk fac-tors, and design appropriate management measures (Rush-ton et al. 2004, de Zwart et al. 2006, Kapo and Burton 2006,Kapo et al. 2008). Statistical models based on field monitor-

4E-mail addresses: a.piliere@science.ru.nl; 5a.schipper@science.ru.nl; 6t.breure@science.ru.nl; 7leo.posthuma@rivm.nl; 8dick.de.zwart@rivm.nl; 9dyer.sd@pg.com; 10m.huijbregts@science.ru.nl

DOI: 10.1086/677898. Received 15 August 2013; Accepted 29 January 2014; Published online 14 July 2014.Freshwater Science. 2014. 33(4):1148–1158. © 2014 by The Society for Freshwater Science.

ing data frequently are developed to understand the effectsof individual environmental factors on biotic assemblages.However, this process is not straightforward. Species dif-fer in their sensitivity to particular environmental condi-tions, so results may differ among biotic endpoints. Further-more, modeling is challenging because of the large numberof environmental factors and potential correlations or in-teractions among these factors (Austin 2002, 2007, Guisanand Thuiller 2005, Araujo and Guisan 2006, Elith and Leath-wick 2009). A few studies have shown that interactions be-tween environmental factors can significantly affect bioticresponses, (e.g., Leathwick et al. 2006), but these interactionsoften are ignored in models describing the relationships be-tween biota and the environment (Guisan et al. 2006).Most statistical techniques rely on a priori mathemati-

cal assumptions about the shape of ecological relationships(e.g., linear, unimodal). Thus, biotic responses with othershapes could be overlooked or misestimated (Austin 2007).Nonparametric machine learning techniques like BoostedRegression Trees (BRTs), which do not rely on such as-sumptions, are increasingly used to model complex eco-logical relationships (e.g., Clapcott et al. 2012). BRTs areinsensitive to outliers and data transformation, and the re-sults are stable despite potential correlations among pre-dictors. Furthermore, BRTs can fit predictor interactionswithout the need to specify a priori which ones to investi-gate (Friedman 2001, Friedman and Meulman 2003, De’ath2007, Elith et al. 2008).Our goal was to unravel the relationships between the

integrity of freshwater invertebrate assemblages and mul-tiple interacting environmental factors. We evaluated theextent to which interactions among environmental factorsexplain variation in the integrity of freshwater invertebrateassemblages by applying BRT modeling to a monitoringdatabase containing abiotic and biotic information fromstream sites across Ohio (USA). We used 16 environmentalfactors, covering geography, physical-habitat quality, waterchemistry, and toxic pressure. We described the inverte-brate assemblages with the multimetric Invertebrate Com-munity Index and its 10 components (richness or abundanceof particular invertebrate groups).

METHODSBiotic communitiesData on macroinvertebrate assemblages and correspond-

ing environmental conditions were available for 689 streamsites sampled acrossOhio (Fig. 1). All samples were collectedbetween 2000 and 2008 by the Ohio Environmental Pro-tection Agency (EPA) and associated teams (Appendix S1).We selected the multimetric Invertebrate Community In-dex (ICI) and its component metrics as biotic endpointsfor our analysis (Table 1). The ICI component metrics con-sist of 10 structural characteristics of the invertebrate com-

munity and correspond to either the number of taxa withina specific group or the number of individuals of a specificgroup relative to all individuals in the sample. The com-ponent metrics are either taxonomy based (e.g., number ofEphemeroptera taxa) or representative of organisms show-ing a similar sensitivity to disturbance (e.g., % tolerant in-dividuals). Nine of the component metrics are based on in-vertebrate samples collected with modified Hester–Dendyartificial substrates, which are left in streams for inver-tebrate colonization for 6 wk typically between June andSeptember. The richness of Ephemeroptera, Plecoptera, andTrichoptera (EPT) taxa is measured in samples obtained bykick-sampling all substrates present at the site (Ohio EPA1989). Specific taxonomic groups are thought to have dis-tinct tolerance ranges and, thus, are expected to show char-acteristic responses to particular stressors. For example,Ephemeroptera often disappear first in case of chemical pol-lution, whereas Tanytarsini midges tend to be more toler-ant (Ohio EPA 1987). Most component metrics are relatedto taxonomic/functional groups that respond negatively toenvironmental pressure, but 2 tolerant groups respond posi-tively (other Diptera and noninsects, and so-called toler-ant taxa; Table 1).To compute the ICI, the raw value (richness, %) of each

component metric obtained from field data is assigned ascore according to the degree of deviation from the valueexpected at a site in a stream of similar size in a similargeographic region with minimal human influence (hence-forth, reference sites). Each component metric can take thevalues 0, 2, 4, or 6, and the tolerant groups are valued re-versely, i.e., low raw values are assigned a high score (OhioEPA 1987). The overall ICI score is obtained by summingall metric scores and ranges from 0 (extremely degraded)to 60 (reference condition-like integrity). For easier com-parison of the results and especially of the interaction rank-

Figure 1. Sampling sites across the state of Ohio, USA(n = 689).

Volume 33 December 2014 | 1149

ings, we rescaled the ICI and its component metrics torange from 0 to 100.

Environmental predictorsWe included 16 environmental factors belonging to

4 categories: geography, water chemistry, toxic pressure,and physical-habitat quality (Table 2). Three of the geo-graphical predictors were used as surrogates for factorsthat were unavailable but potentially relevant. Watershedarea is associated with stream size and, thus, is a surrogatefor the amount and variety of physical habitat and dis-charge volume. Altitude is a surrogate for substrate typeand water temperature, and average catchment slope is asurrogate for current velocity. We included latitude andlongitude to account for spatial autocorrelation and large-scale biogeographical patterns. Examples include precipita-tion or anthropogenic factors like impervious cover of thesoil, which influences the hydrologic regime. We evaluatedthe residual spatial autocorrelation of each model as Moran’sI autocorrelation coefficient, based on deviance residuals,with the ape package in R (Paradis et al. 2004). The resid-ual spatial autocorrelation was ∼0 for all endpoints (datanot shown).We expressed physical-habitat quality with the Quali-

tative Habitat Evaluation Index (QHEI) score. The QHEI isa multimetric index of physical macrohabitat quality in run-ning waters of Ohio, and originally was developed for fish.It is composed of 7 metrics related to particular character-istics of the stream habitat: substrate type and quality, typeand amount of in-stream cover, channel morphology, ripar-

ian zone and bank erosion, pool and glide quality, riffle andrun quality, and local gradient (elevation drop throughthe sampled area of the stream). The metrics are evalu-ated in the field according to a standard evaluation sheet,and their scores are summed to arrive at an overall scoreranging from 0 (poorest quality) to 100 (maximum quality;Ohio EPA 2006).The water-chemistry category included 9 predictors:

pH, O2 demand (biological and chemical [BOD, COD]),nutrient concentrations (total [T] N and TP), dissolved andsuspended solids, specific conductance, and hardness. Toxicpressure of mixtures in the samples was expressed as themultisubstance Potentially Affected Fraction of species(msPAF) derived for various substance groups at the EC50-level: industrial products (heavy metals and NH3), house-hold products, synthetic estrogens, and pharmaceuticals).An EC50-based toxic pressure of 25% means that toxi-cant concentrations in that water sample are expected toinduce effects in 25% of the tested species with a mag-nitude of response of ≥50%. Concentrations of industrialproducts were measured in field samples. Concentrationsof other substances were calculated with the GIS RIVERROUT model and data about sales volumes, populationdensity, degradation rates, treatment efficiencies, and in-stream dilution on a stretch-by-stretch basis (Dyer andCaprara 1997, Wang et al. 2000, 2005). The mixture toxicpressure of all compounds together was derived from thesite-specific concentrations (measured or modeled), bio-availability assessment, and mixture modeling accordingto the procedures described by Posthuma et al. (2002), deZwart et al. (2006), and Posthuma and de Zwart (2006).

Table 1. Biotic endpoints and their respective ranges across sites (n = 689). The Invertebrate Community Index (ICI) is a multimetricbioassessment tool assembling several submetrics related to structural and functional attributes of the invertebrate community (OhioEPA 1987). Min = minimum, max = maximum.

Biotic endpoints CodeExpected responseto disturbance Mean Min 1st quartile Median 3rd quartile Max

Ohio-specific invertebrate community index ICI Decrease 41.8 0.0 36.0 44.0 50.0 60.0

ICI submetrics

Taxon richness Taxa Decrease 62.9 7.0 52.0 64.0 72.0 113.0

Number of Ephemeroptera taxa #Eph Decrease 6.5 0.0 4.0 7.0 9.0 15.0

Number of Trichoptera taxa #Tri Decrease 4.4 0.0 2.0 4.0 6.0 13.0

Number of Ephemeroptera, Trichoptera,and Plecoptera taxa #EPT Decrease 13.5 0.0 9.0 13.0 18.0 32.0

Number of Diptera taxa #Dip Decrease 17.7 3.0 14.0 18.0 21.0 33.0

% Ephemeroptera individuals PctEph Decrease 18.0 0.0 4.6 14.3 27.5 81.4

% Trichoptera individuals PctTri Decrease 14.3 0.0 2.3 8.7 23.4 71.3

% Tanytarsini individuals PctTan Decrease 24.5 0.0 9.6 19.6 34.9 84.7

% other Diptera (excluding Tanytarsini)and noninsect individuals PctOth Increase 40.6 3.7 22.4 35.4 55.4 100.0

% tolerant individuals PctTol Increase 8.5 0.0 1.2 3.4 9.5 99.3

1150 | Quantifying environmental predictor interactions A. Pilière et al.

More details about the toxic pressure derivation are pro-vided in Appendix S1.

Boosted regression treesRegression trees explain the variation in a single re-

sponse variable by repeatedly splitting the data into in-creasingly homogeneous groups, choosing for every splitthe best predictor and split value to minimize the predic-tion error (De’ath and Fabricius 2000). Boosted Regres-sion Trees (BRT) use a boosting algorithm to combine alarge number of simple regression trees, typically severalhundreds to several thousands (Friedman 2001, Friedmanand Meulman 2003, De’ath 2007, Elith et al. 2008). A 1st

regression tree is built using the set of selected predictorsand a given response variable. Then a 2nd tree is fitted onthe residuals between the 1st model’s fitted values and theobserved response, then a 3rd on the residuals of the pre-vious global model, and so on, each time giving more weightto the samples poorly modeled during the former steps(De’ath 2007, Elith et al. 2008). Addition of trees typicallystops when maximal predictive power is reached, i.e., whenadditional trees no longer improve predictive accuracy.BRTs can be used to derive partial dependency plots, whichrepresent the effect of a single predictor on the response,

based on the splits in which the predictor was involvedacross the whole series of trees, given the averaged ef-fect of the other predictors. BRTs intrinsically account forpredictor interactions because the selected predictor andvalue for each split of a tree depend on the previous splits.The maximum order of interactions (i.e., number of pre-dictors interacting together) that can be fit by the model de-pends on the user-defined complexity (number of splits)of individual trees. In binary (single-split) trees, no interac-tion is taken into account, whereas models based on 2-splittrees can fit pairwise interactions, and so on. However, themodel will fit interactions only if they are supported by thedata (see Elith et al. 2008 for more details).We constructed BRTs in R (version 2.14.0; R Project for

Statistical Computing, Vienna, Austria) with the gbm pack-age and additional BRT functions written by Elith et al.(2008). We varied the tree complexity of our models from1 to 10 to assess the effect of fitting interactions on overallprediction performance. For each model, we selected opti-mal parameter values by systematically varying the learn-ing rate (0.001, 0.005, 0.01, 0.05) and the bag-fraction (0.55,0.65, 0.75). Thus, for each of our 11 endpoints, we built 10 ×3 × 4 = 120 models representing all possible combinationsof 10 tree-complexity values, 3 bag-fractions, and 4 learn-

Table 2. Environmental factors and their respective ranges across the sampled sites (n = 689). Slope was quantified as the slopelength factor (Revised Universal Soil Loss Equation; Kirk 1950). The quality habitat evaluation index is an expert-judgment-basedmultimetric index used to express physical macrohabitat quality in running waters (Ohio EPA 2006). Total N (TN) was quantified byKjeldahl’s method (Renard et al. 1991). Min = minimum, max = maximum.

Environmental factors Code Unit Mean Min 1st quartile Median 3rd quartile Max

Geography

Watershed area upstream of the sampled point WA km2 248.2 1.0 28.0 63.0 201.5 7995.0

Altitude of the sampled point Alt m 250.7 138.9 214.2 248.6 289.0 370.5

Average slope of the watershed Slope % 5.8 0.4 1.9 3.4 7.2 44.2

Latitude Lat degree 40.2 38.7 39.6 40.2 40.9 41.8

Longitude Long degree −82.7 −84.8 −83.6 −82.8 −81.8 −80.7

Physical-habitat quality

Ohio-specific qualitative habitat evaluation index QHEI – 68.2 20.0 60.9 69.5 78.5 99.0

Water chemistry

pH pH SU 7.9 3.2 7.7 7.9 8.1 9.7

Biological O2 demand (5 d) BOD5 mg/L 2.6 1.0 1.0 1.0 3.4 21.0

Chemical O2 demand COD mg/L 26.8 5.0 15.0 22.0 30.0 258.0

Total N TN mg/L 0.9 0.1 0.5 0.7 1.0 8.4

Total P TP mg/L 3.3 0.0 0.1 0.2 0.4 128.0

Total dissolved solids TDS mg/L 503.9 86.0 356.0 450.0 577.5 3910.0

Total suspended solids TSS mg/L 62.9 2.5 12.0 27.0 61.8 1590.0

Specific conductance Cond μS/cm 801.8 122.0 578.3 732.0 903.0 5060.0

Hardness (total CaCO3 concentration) CaCO3 mg/L 317.5 51.0 240.0 312.0 370.0 1700.0

Toxic pressure

Multisubstances potentially affected fraction of species msPAF – 11.8 0.5 2.1 4.7 14.2 90.3

Volume 33 December 2014 | 1151

ing rates. The maximum number of trees per individualmodel was set at 50,000, and this number was never reached.We eliminated a posteriori the models based on <1000trees, following the rule of thumb suggested by Elith et al.(2008).To prevent overfitting, we built each of the 120 mod-

els per endpoint after a 10-fold cross-validation procedure.In this procedure, 10 BRT models are built in parallel us-ing 10 random subsets of the data, each consisting of 9/10th of the data set. The predictive performance of each ofthese models is tested on the remaining 1/10th of the dataset (i.e., the validation data). The 10 validation data setsare mutually exclusive (i.e., each observation is used fortesting only once). The predictive power is assessed aftereach addition of trees, and no further trees are added whenthe predictive power no longer improves. The final BRTmodel is obtained by averaging the predictive power andpredictions of the 10 models.The predictive power of the BRT models, assessed as

the total % deviation explained, is given by the predictivesquared correlation coefficient Q2 (Eq. 1) of the regressionof predicted vs observed values (Legates and McCabe 1999),averaged across the cross-validation models:

Q2 ¼ 1−∑N

i¼1ðypred;i−yiÞ2∑N

i¼1ðyi−ymeanÞ2(Eq. 1)

where N is the number of data points, ypred,i is the pre-dicted value for point i, yi is the observed value for point iof the validation set, and ymean is the mean observed valuein the validation set.For each endpoint and each value of tree complexity,

we selected the best model as the model with the highestpredictive power among the 12 combinations of learningrate and bag-fraction power (Friedman 2001, Elith et al.2008). The relative importance of a predictor for a par-ticular response variable was calculated by summing thedeviation explained by each split in which the predictorwas involved relative to the total deviation explained bythe model, averaged across cross-validation sets. Partial-dependency plots illustrating single predictor–response re-lationships were constructed based on the best model perendpoint. Interaction strength was quantified using thegbm.interactions function, which compares the predictionsof the BRT model along the gradients of 2 interacting predic-tors with the predictions of a model assuming no interactionsbetween those 2 predictors (Elith et al. 2008). Interactiveeffects in the best models are illustrated by plotting bioticendpoint response curves to a given predictor for differ-ent values of a 2nd predictor found to interact with the 1st.

RESULTSThe best BRT models explained 35.5% of the devi-

ation in the ICI and between 21.8 (% Tanytarsini) and

54.3% (number of EPT taxa) of the deviation in its com-ponent metrics (Table 3). The predictive power generallywas larger for response variables pertaining to richnessthan for those describing abundance (% individuals). Thebest models had a tree complexity varying between 5 and10 (i.e., interactions involving a maximum 5–10 predictorswere fitted). Compared to no-interactions models, the gainin predictive power when fitting interactions varied from3.1 to 10.0% of additional deviation explained (Table 3).The largest increase in explained deviation was observedfor pairwise (2nd-order) interactions (Fig. 2).The ICI was mainly related to TP (4.4% of variation

explained), physical-habitat quality (4.2%), and pH (3.3%),followed by TN (2.9%) (Table 3). The ICI component met-rics were mainly related to geographical variables (water-shed area and longitude) and physical-habitat quality, fol-lowed by latitude, TP, and pH. ICI tended to increasewith watershed area, physical-habitat quality, and pH > 7.5(Fig. 3). The ICI decreased with increasing TN and de-creased steeply with TP > 50 mg/L. The response curvesfor latitude and longitude were the most complex andshowed variable trends. Response curves derived for theICI component metrics are given in Appendix S2. In gen-eral, a predictor’s contribution to the ICI was larger whenthe predictor–response relationships were similar across allcomponent metrics (e.g., for pH, TN, and TP). Dissimilarpredictor–response relationships across the ICI componentmetrics, e.g., for longitude, yielded with a smaller contribu-tion of this predictor to the ICI and a flatter ICI responsecurve.The most important interactions for the ICI involved

the 4 main predictors of the ICI (QHEI, TP, pH, and TN)and latitude. The interactive effects between each pair ofpredictors are shown in Fig. 4A–D. For example, the ICIshowed a stronger decrease in response to a decreasingpH at high (2 mg/L) than low (0.025 mg/L) TP (Fig. 4A).The ranking of pairwise interactions for the ICI and itscomponent metrics are provided in Appendix S3. Theinteraction plots for the ICI component metrics are pro-vided in Appendix S4.

DISCUSSIONEnvironmental influences on invertebrate assemblagesWe used BRTs to investigate the responses of fresh-

water invertebrate assemblage integrity to multiple inter-acting environmental factors. Our results concur with thefindings of several other authors. For example, our find-ings are in line with the results obtained by Griffith et al.(2003) using clustering analyses. Those authors found de-creasing tolerant taxon abundance and increasing speciesrichness, EPT richness, and Ephemeroptera and Trichopteraabundances with increasing stream physical-habitat qual-ity. Weigel and Robertson (2007) used regression tree anal-yses and proposed ‘breakpoints’ above which assemblages

1152 | Quantifying environmental predictor interactions A. Pilière et al.

Table 3. Results of the Boosted Regression Trees best models. The contribution of each environmental predictor is expressed as the% deviation it explains in the biotic endpoint of concern. Contributions of ≥4% of explained variation and predictors showing suchcontribution for ≥1 endpoint are shown in bold. Partial dependency plots for those predictors are shown in Fig. 3 for the InvertebrateCommunity Index (ICI) and Appendix S2 for ICI component metrics (biotic endpoints). See Table 1 for metric abbreviations andTable 2 for predictor abbreviations.

Environmental predictor

Biotic endpoints

ICI Taxa #Eph #Tri #EPT #Dip PctEph PctTri PctTan PctOth PctTol

Geography

WA 2.3 2.1 4.5 15.3 7.0 4.9 3.2 9.2 1.7 4.8 1.6

Lat 2.6 4.3 2.3 3.0 4.1 1.5 1.9 1.9 1.7 2.1 1.3

Long 1.6 6.0 3.2 2.8 4.9 1.3 4.5 2.6 4.5 1.8 1.7

Alt 1.5 1.8 2.4 2.5 2.6 1.5 1.4 1.8 1.0 1.6 0.9

Slope 2.2 1.4 2.1 1.6 2.5 1.9 2.0 2.3 1.7 1.7 1.8

Physical-habitat quality

QHEI 4.2 3.2 3.3 4.1 8.6 2.0 1.9 2.5 1.4 2.2 3.2

Water chemistry

BOD5 1.7 1.4 1.2 0.7 1.0 1.1 1.0 0.9 0.7 1.6 1.3

COD 1.6 1.4 1.3 0.8 1.9 0.9 1.0 0.8 0.7 1.0 0.8

pH 3.3 3.3 2.6 2.9 2.9 2.6 1.1 2.4 1.0 1.2 1.7

TSS 1.5 1.0 1.6 0.8 1.7 1.0 1.2 0.9 0.5 1.3 1.0

TN 2.9 1.9 2.0 1.9 2.6 1.4 1.0 1.3 2.2 1.4 2.0

TP 4.4 7.1 2.5 1.8 3.4 2.3 1.6 1.1 0.7 1.8 2.3

TDS 1.4 2.0 1.7 0.9 3.5 0.9 1.1 1.0 0.6 1.0 0.7

CaCO3 1.6 1.5 2.3 1.3 1.7 1.4 1.2 1.5 1.2 0.6 1.0

Cond 1.4 3.0 3.0 0.8 3.3 1.2 1.4 1.9 0.6 0.9 1.3

msPAF 1.5 2.2 2.0 1.5 2.7 1.1 1.9 1.1 1.6 0.8 0.9

Total deviationexplained by thebestmodel (%)

35.5 43.9 38.1 42.6 54.3 27.0 27.3 33.2 21.8 25.8 23.6

Standarddeviation acrossvalidationsubsets (%)

6.9 6.4 9.0 9.7 4.4 3.2 7.7 9.8 8.9 8.2 8.5

Tree complexityof the bestmodel (/)

10 10 10 9 8 10 10 10 10 5 10

Number of treesbuilt by the bestmodel (/)

2150 1550 1600 1150 2200 4700 1050 4200 3200 2100 2800

Learning ratevalue for thebest model (/)

0.005 0.005 0.005 0.005 0.01 0.001 0.005 0.001 0.001 0.005 0.005

Bag-fractionvalue for thebest model (/)

0.75 0.75 0.5 0.65 0.65 0.5 0.5 0.5 0.75 0.75 0.65

Total deviationexplained by theno-interactionsmodel (%)

28.4 38.2 31.8 37.5 46.9 23.2 21.6 30.0 18.7 22.3 13.6

were consistently affected by Kjeldahl N (0.77 mg/L) andTP (0.15 mg/L). However, according to our response curves,TN starts affecting invertebrate assemblages at even lowerconcentrations (∼0.25 mg/L). We also did not observe anegative effect of TP on the ICI at low concentrations. Yuanand Norton (2003) used General Additive Models (GAMs)to investigate responses of invertebrate metrics, includingspecies richness, abundance of tolerant taxa, and Ephem-eroptera richness, to gradients of P, physical-habitat quality,specific conductance, and dissolved Al. The trends of mostof their response curves are consistent with our results.

Importance of interactions amongenvironmental factorsThe predictive performance of our BRT models con-

sistently increased with the order of predictor interac-tions included. The additional deviation explained by upto 10th-order interactions ranged as high as 10.0%, with the

Figure 2. Predictive power of the Boosted Regression Tree(BRT) models in relation to tree complexity. A tree complexityof 1 corresponds tomodels without interactions, a tree complexityof 2 to models including pairwise interactions, etc. See Table 1for variable codes.

Figure 3. Partial dependency plots extracted from the best Boosted Regression Tree (BRT) model for the Invertebrate CommunityIndex (ICI), illustrating single predictor–response relationships for each of the 16 environmental predictors. x-axes of clearly positivelyskewed predictors were log10(x + 1)-transformed. The deciles of the predictor’s distribution are represented by the tick marks at thetop of the plots. See Table 2 for variable codes.

1154 | Quantifying environmental predictor interactions A. Pilière et al.

largest gain in predictive power for main (pairwise) interac-tions. Leathwick et al. (2006) used a similar approach andobtained an increase of 12% in the deviation explained whenfitting up to 5th-order interactions, a result similar to ours.These results suggest that fitting interactions between pre-dictors in ecological models leads to substantially improvedpredictive power of the models. Moreover, the occurrenceof multiple interacting, nonadditive effects suggests thatconsidering the influence of single predictors only may leadto biased interpretation of the impact of environmentaldisturbance on the biota. For example, impacts of P on in-

vertebrate community integrity may go unnoticed at lowpH values and become evident only when the pH increases(Fig. 4A).However, the predictors involved in the most impor-

tant interactions differed among endpoints (Appendix S3).Thus, our results do not allow identification of a few inter-acting predictors that consistently affect freshwater inver-tebrate assemblages. Moreover, although we found that thepredictive power of our models increased even until 10th-level interactions were fitted, the gain in predictive powerwas largest between the no- and pairwise interaction models

Figure 4. Interaction plots for pH and total P (A), the qualitative habitat evaluation index (QHEI) and total P (B), total N andQHEI (C), and QHEI and latitude (D), the 4 main pairs of predictors interacting in the best model for the Invertebrate CommunityIndex (ICI). The solid and dotted lines show the responses of the ICI to a particular predictor for given values of a 2nd predictor. Thevalues for the 2nd predictor correspond to the 5th and 95th percentiles of its distribution. x-axes of clearly positively skewed predictorswere log10(x + 1)-transformed. The deciles of the predictor’s distribution are represented by the tick marks at the top of the plots.

Volume 33 December 2014 | 1155

and decreased rapidly for each additional interaction level.Interactive effects involving >3 predictors also are difficultto interpret and nearly impossible to visualize. BRTs can fithigh levels of interactions because of their intrinsic tree struc-ture, but only pairwise interactions can be investigated interms of relative importance (ranking). We suggest that fo-cusing on pairwise interactions is a good compromise be-tween gain in predictive power and the ease of interpreta-tion. Controlled laboratory experiments involving multipleenvironmental factors are one way to obtain independentdata to verify the interactions fitted by BRT models.

Comparison of the responses of the ICIand underlying componentsIn general, models of richness-related endpoints had

higher predictive power than models of abundance-relatedendpoints (Table 3). Several potential explanations existfor this difference in predictive power. First, factors likethe amount of food resources, period of the year, and bioticinteractions, such as competition and predation, may havea stronger influence on the abundance of a taxon than onits mere presence or absence. Second, abundance estimatesare generally less precise than richness estimates. In addi-tion, the abundance endpoints included 3 less sensitiveor tolerant endpoints (% Tanytarsini, % other Diptera, and% tolerant organisms). More tolerant groups have a broaderecological niche and can occur in a broader range of envi-ronmental conditions. As a consequence, they are likelyto show weaker responses than sensitive taxa to environ-mental variables, resulting in less predictive power in ourmodels.The low contribution of watershed area to % deviation

explained in the ICI relative to the large contributions ofseveral components, may be explained by opposite re-sponses of EPT and Diptera (Appendix S2) or by the scor-ing procedure, in which reference assemblages differ withstream size, and score attribution criteria vary accordingly.Thus, differences in taxonomic composition from head-waters to lowland reaches may be controlled out in the ICIscore.

Implications for management practicesOur results suggest that priority measures for improving

the biological integrity of freshwater invertebrate assem-blages in Ohio should be to increase physical-habitat qual-ity (QHEI), reduce nutrient concentrations, and increase pHwhere needed. The QHEI was originally developed to quan-tify fish habitat quality, but our results indicate that it is auseful habitat-quality indicator for invertebrates, too. How-ever, the various habitat characteristics included in this in-dex (e.g., quality of the riparian area, quality of the streamsubstrate) may have different influences on the biologicalendpoints, depending, for example, on the dependence of

the taxa of concern on a given habitat type. Consideringthe importance of the QHEI for the biotic integrity offreshwater invertebrate assemblages in our models, furtherinvestigation could be focused on identifying the QHEImetrics that are pertinent to invertebrate community in-tegrity.The ICI is used to assess the amount of impairment

caused by human activities at a given site. To that end, thecomponent metrics are attributed a score by comparingtheir values to the metric values observed in reference sites.However, in our results, geographical factors tended to havea relatively high predictive power for the ICI and its com-ponent metrics (Table 3). Further investigation is requiredto define whether these correlations are a result of externalhuman-induced factors correlated to our geographical pre-dictors or whether the ICI component metrics calculationneeds to be further adjusted for natural gradients.Investigation of the predictor–response curves for the

ICI compared to the submetrics showed how specific re-sponses can be blurred at a higher level of aggregation. Mul-timetric indices are commonly used to summarize bothenvironmental factors (e.g., QHEI score, msPAF) and bio-logical assemblages (e.g., ICI). They merge several dimen-sions of the environment or biota into single indices, mak-ing them easy to compare. However, such aggregation isexpected to result in loss of information (King and Baker2010). Amplification of the importance of predictors nega-tively or positively affecting all submetrics is meaningful foroverall community integrity assessment, but effects on par-ticular endpoints might be obscured in the overall index ifother taxonomic groups show opposite responses to the en-vironmental factor of concern. A similar phenomenon mayexplain why msPAF had limited predictive power for any ofthe endpoints in our study despite high levels of toxic pres-sure in some reaches (Table 1). Toxic impacts might occurmainly at the level of individual species, with effects can-celled out at the level of ICI submetrics. Particular species orgenera showed (highly) significant associations with msPAFvalues in studies by de Zwart et al. (2006) and Posthumaand de Zwart (2012).Our results further showed that a substantial (>5%

deviation explained) increase in predictive power may beachieved by fitting predictor interactions, and that thisgain is a consequence of a variety of predictor interactionsof potentially high orders. This finding suggests that envi-ronmental interactions should not be neglected in futurestudies. However, the gain in predictive power was largestwhen pairwise interactions were included, so we suggestthat focusing on pairwise interactions is a good compro-mise between gaining predictive power and enabling inter-pretation. More knowledge on predictor interactions mightprove especially useful for management purposes by pro-viding better insight to the effect of disturbance on biotaand improving the efficiency of remediation measures. For

1156 | Quantifying environmental predictor interactions A. Pilière et al.

example, taking into account the interaction between Pand pH and acting on these 2 factors simultaneously maylead to increased benefits in terms of biological integrityrelative to addressing either factor separately.

ACKNOWLEDGEMENTSWe thank Dennis Mischne (Ohio EPA), Charlotte White-

Hull (Proctor and Gamble, retired), Katherine Kapo (MontaniRun,Waterborne Environmental, Inc.), Christopher Holmes (Wa-terborne Environmental, Inc.), Christian Mulder (National Insti-tute for Public Health and the Environment [RIVM]), and ArieVonk (University of Amsterdam) for their contributions andfruitful discussions. We also thank Associate Editor Lester Yuanand anonymous referees for their constructive comments.

LITERATURE CITEDAllan, J. D. 2004. Landscapes and riverscapes: the influence ofland use on stream ecosystems. Annual Review of Ecology,Evolution, and Systematics 35:257–284.

Applegate, J. M., P. C. Baumann, E. B. Emery, and M. S. Wooten.2007. First steps in developing a multimetric macroinvertebrateindex for the Ohio River. River Research and Applications 23:683–697.

Araujo, M. B., and A. Guisan. 2006. Five (or so) challenges forspecies distribution modelling. Journal of Biogeography 33:1677–1688.

Austin, M. P. 2002. Spatial prediction of species distribution:an interface between ecological theory and statistical model-ling. Ecological Modelling 157:101–118.

Austin, M. P. 2007. Species distribution models and ecologicaltheory: a critical assessment and some possible new approaches.Ecological Modelling 200:1–19.

Barbour, M. T., J. Gerritsen, G. E. Griffith, R. Frydenborg, E.McCarron, J. S. White, and M. L. Bastian. 1996. A frame-work for biological criteria for Florida streams using benthicmacroinvertebrates. Journal of the North American Bentholog-ical Society 15:185–211.

Clapcott, J. E., K. J. Collier, G. De’ath, E. O. Goodwin, J. S.Harding, D. Kelly, J. R. Leathwick, and R. G. Young. 2012.Quantifying relationships between land-use gradients and struc-tural and functional indicators of stream ecological integrity.Freshwater Biology 57:74–90.

De’ath, G. 2007. Boosted trees for ecological modelling and pre-diction. Ecology 88:243–251.

De’ath, G., and K. E. Fabricius. 2000. Classification and regres-sion trees: a powerful yet simple technique for ecological dataanalysis. Ecology 81:3178–3192.

de Zwart, D., S. D. Dyer, L. Posthuma, and C. P. Hawkins.2006. Predictive models attribute effects on fish assemblagesto toxicity and habitat alteration. Ecological Applications 16:1295–1310.

Dyer, S. D., and R. J. Caprara. 1997. A method for evaluating con-sumer product ingredient contributions to surface and drink-ing water: boron as a test case. Environmental Toxicologyand Chemistry 16:2070–2081.

Elith, J., and J. R. Leathwick. 2009. Species distribution models:Ecological explanation and prediction across space and time.

Annual Review of Ecology, Evolution, and Systematics 40:677–697.

Elith, J., J. R. Leathwick, and T. Hastie. 2008. A working guideto boosted regression trees. Journal of Animal Ecology 77:802–813.

Friedman, J. H. 2001. Greedy function approximation: a gra-dient boosting machine. Annals of Statistics 29:1189–1232.

Friedman, J. H., and J. J. Meulman. 2003. Multiple additive re-gression trees with application in epidemiology. Statistics inMedicine 22:1365–1381.

Griffith, M. B., P. Husby, R. K. Hall, P. R. Kaufmann, and B. H.Hill. 2003. Analysis of macroinvertebrate assemblages in re-lation to environmental gradients among lotic habitats of Cal-ifornia’s central valley. Environmental Monitoring and Assess-ment 82:281–309.

Guisan, A., A. Lehmann, S. Ferrier, M. Austin, J. M. C. C.Overton, R. Aspinall, and T. Hastie. 2006. Making better bio-geographical predictions of species’ distributions. Journal ofApplied Ecology 43:386–392.

Guisan, A., and W. Thuiller. 2005. Predicting species distribu-tion: offering more than simple habitat models. Ecology Let-ters 8:993–1009.

Kapo, K. E., and G. A. Burton. 2006. A geographic informationsystems-based, weights-of-evidence approach for diagnosingaquatic ecosystem impairment. Environmental Toxicology andChemistry 25:2237–2249.

Kapo, K. E., G. A. Burton, D. de Zwart, L. Posthuma, and S. D.Dyer. 2008. Quantitative lines of evidence for screening-leveldiagnostic assessment of regional fish community impacts: acomparison of spatial database evaluation methods. Environ-mental Science and Technology 42:9412–9418.

King, R. S., and M. E. Baker. 2010. Considerations for analyz-ing ecological community thresholds in response to anthropo-genic environmental gradients. Journal of the North AmericanBenthological Society 29:998–1008.

Kirk, P. L. 1950. Kjeldahl method for total nitrogen. AnalyticalChemistry 22:354–358.

Klemm, D. J., K. A. Blocksom, W. T. Thoeny, F. A. Fulk, A. T.Herlihy, P. R. Kaufmann, and S. M. Cormier. 2002. Methodsdevelopment and use of macroinvertebrates as indicators ofecological conditions for streams in the Mid-Atlantic Highlandsregion. Environmental Monitoring and Assessment 78:169–212.

Leathwick, J. R., J. Elith, M. P. Francis, T. Hastie, and P. Taylor.2006. Variation in demersal fish species richness in the oceanssurrounding New Zealand: an analysis using boosted regres-sion trees. Marine Ecology Progress Series 321:267–281.

Legates, D. R., and G. J. McCabe. 1999. Evaluating the “goodness-of-fit” measures in hydrologic and hydroclimatic model vali-dation. Water Resources Research 35:233–241.

Norton, S. B., S. M. Cormier, M. Smith, and R. C. Jones. 2000.Can biological assessments discriminate among types of stress?A case study from the Eastern Corn Belt Plains ecoregion. En-vironmental Toxicology and Chemistry 19:1113–1119.

Ogbeibu, A. E., and B. J. Oribhabor. 2002. Ecological impact ofriver impoundment using benthic macroinvertebrates as in-dicators. Water Research 36:2427–2436.

Ohio EPA (Ohio Environmental Protection Agency). 1987. Bio-logical criteria for the protection of aquatic life: Volume II.

Volume 33 December 2014 | 1157

User’s manual for biological field assessment of Ohio surfacewaters. Division of Water Quality Monitoring and Assess-ment, Surface Water Section, Columbus, Ohio. (Available from:http://www.epa.state.oh.us/dsw/bioassess/BioCriteriaProtAqLife.aspx)

Ohio EPA (Ohio Environmental Protection Agency). 1989. Bio-logical criteria for the protection of aquatic life: Volume III.Standardized biological field sampling and laboratory meth-ods for assessing fish and macroinvertebrate communities.Division of Water Quality Monitoring and Assessment, Co-lumbus, Ohio. (Available from: http://www.epa.state.oh.us/dsw/bioassess/BioCriteriaProtAqLife.aspx)

Ohio EPA (Ohio Environmental Protection Agency). 2006. Meth-ods for assessing habitat in flowing waters: using the Quali-tative Habitat Evaluation Index (QHEI). Ohio EPA TechnicalBulletin EAS/2006/06/1. Division of Water Quality Monitor-ing and Assessment, Columbus, Ohio.

Paradis, E., J. Claude, and K. Strimmer. 2004. APE: analyses ofphylogenetics and evolution in R language. Bioinformatics 20:289–290.

Posthuma, L., and D. de Zwart. 2006. Predicted effects of toxicantmixtures are confirmed by changes in fish species assemblagesin Ohio, USA. Environmental Toxicology and Chemistry 25:1094–1105.

Posthuma, L., G. W. Suter, and T. P. Traas. 2002. Species sen-sitivity distributions in ecotoxicology. Lewis Publishers, BocaRaton, Florida.

Posthuma, L., and D. de Zwart. 2012. Predicted mixture toxicpressure relates to observed fraction of benthic macrofaunaspecies impacted by contaminant mixtures. EnvironmentalToxicology and Chemistry 31:2175–2188.

Renard, K. G., G. R. Foster, G. A. Weesies, and J. P. Porter.1991. RUSLE – Revised universal soil loss equation. Journalof Soil and Water Conservation 46:30–33.

Rushton, S. P., S. J. Ormerod, and G. Kerby. 2004. New paradigmsfor modelling species distributions? Journal of Applied Ecol-ogy 41:193–200.

Verdonschot, R. C. M., H. E. Keizer-Vlek, and P. F. M. Verdonschot.2012. Development of a multimetric index based on macroin-vertebrates for drainage ditch networks in agricultural areas.Ecological Indicators 13:232–242.

Wallace, J. B., J. W. Grubaugh, and M. R. Whiles. 1996. Bioticindices and stream ecosystem processes: results from an ex-perimental study. Ecological Applications 6:140–151.

Wang, X. H., M. Homer, S. D. Dyer, C. White-Hull, and C. Du.2005. A river quality model integrated with a web-based geo-graphic information system. Journal of Environmental Man-agement 75:219–228.

Wang, X. H., C. White-Hull, S. D. Dyer, and Y. Yang. 2000.GIS-ROUT: A river model for watershed planning. Environ-ment and Planning B: Planning and Design 27:231–246.

Weigel, B. M., and J. J. Dimick. 2011. Development, validation,and application of a macroinvertebrate-based Index of BioticIntegrity for nonwadeable rivers of Wisconsin. Journal of theNorth American Benthological Society 30:665–679.

Weigel, B. M., and D. M. Robertson. 2007. Identifying biotic in-tegrity and water chemistry relations in nonwadeable rivers ofWisconsin: toward the development of nutrient criteria. Envi-ronmental Management 40:691–708.

Yuan, L. L., and S. B. Norton. 2003. Comparing responses ofmacroinvertebrate metrics to increasing stress. Journal of theNorth American Benthological Society 22:308–322.

1158 | Quantifying environmental predictor interactions A. Pilière et al.