ENDANGERED SPECIES RESEARCHEndang Species Res

Vol. 31: 243–258, 2016doi: 10.3354/esr00766

Published November 28

INTRODUCTION

The standard for justifying the use of vertebrateanimals for monitoring and study is being increas-ingly raised for researchers requiring increased rigorin designing studies. Because ecosystems may con-tain large numbers of species, and studying everyspecies is not possible, the selection and prioritiza-tion of study species or target species for monitoringis critically important. Ideally, in selecting species foreither of these purposes, the goal is to select the bestumbrella species, that is, species that are representa-tive of the largest number of taxa. The umbrella con-cept has been widely used in conservation planning

and moni toring, for instance, where study or monitor-ing of a small number of broadly representative spe-cies makes multi-species protection and assessmenttractable (Fleishman et al. 2000, Hierl et al. 2008,Regan et al. 2008, Tucker et al. 2012). The idea hereis that through using a combination of presumed sen-sitivity of species to disturbance and species occur-rence and abundance records, conservation effortsapplied to one or a small number of species will alsoprovide the largest protective umbrella for conserva-tion of non-target species (Roberge & Angelstam2004). The umbrella species concept may also be auseful construct for understanding the species-leveleffects of ecosystem alteration; that is, the umbrella

© UT-Battelle 2016. Open Access under Creative Commons byAttribution Licence. Use, distribution and reproduction are un -restricted. Authors and original publication must be credited.

Publisher: Inter-Research · www.int-res.com

*Corresponding author: pracheilbm@ornl.gov**Retired

A traits-based approach for prioritizing species formonitoring and surrogacy selection

Brenda M. Pracheil1,*, Ryan A. McManamay1, Mark S. Bevelhimer1, Chris R. DeRolph1, Glenn F. >ada1,**

1Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

ABSTRACT: The bar for justifying the use of vertebrate animals for study is being increasinglyraised, thus requiring increased rigor for species selection and study design. Although we havepower analyses to provide quantitative backing for the numbers of organisms used, quantitativebacking for selection of study species is not frequently employed. This can be especially importantwhen measuring the impacts of ecosystem alteration, when study species must be chosen that areboth sensitive to the alteration and of sufficient abundance for study. Just as important is provid-ing justification for designation of surrogate species for study, especially when the species of inter-est is rare or of conservation concern and selection of an appropriate surrogate can have legalimplications. In this study, we use a combination of GIS, a fish traits database and multivariate sta-tistical analyses to quantitatively prioritize species for study and to determine potential study sur-rogate species. We provide two case studies to illustrate our quantitative, traits-based approachfor designating study species and surrogate species. In the first case study, we select broadly rep-resentative fish species to understand the effects of turbine passage on adult fishes based on traitsthat suggest sensitivity to turbine passage. In our second case study, we present a framework forselecting a surrogate species for an endangered species. We suggest that our traits-based frame-work can provide quantitative backing and added justification to selection of study species whileexpanding the inference space of study results.

KEY WORDS: Conservation · Life history traits · Monitoring · Threatened fish

OPENPEN ACCESSCCESS

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species concept can be extended to select one or atractably small subset of species for study that allowsfor an understanding of ecological effects of pertur-bations, stressors or management actions. Althoughthere is some debate as to whether the umbrella spe-cies construct is a useful or even a possible one(Andel man & Fagan 2000, Rubinoff 2001, Caro 2003),some studies have suggested that the umbrella con-cept can aid in conservation planning (Bell et al.2015). A variety of species have been shown to serveas useful umbrella species, such as coho salmonOnco rhyncus kisutch, which serves as an umbrella forprotection of aquatic invertebrate species due to simi-larities in habitat requirements (Branton & Richardson2014), and sage grouse Centrocerus uro pha sianus,which serves as an umbrella for migratory mule deerOdocoileus hemionus due to a similar reliance onsagebrush Artemesia spp. (Copeland et al. 2014).

Determining the best ways to incorporate multipledata types related to species abundance, distribution,and physical and behavioral traits that can facilitatethe selection of umbrella species or species that willserve as a surrogate for another species (hereafter,surrogate species) for detailed study is challenging.The dawn of the big data era has increased the publicavailability of species informatics databases such asthe Global Invasive Species Database (GISD), Biodi-versity Informatics Serving Our Nation Database(BISON), International Union for Conservation ofNature Red List Database (IUCN) and NatureServe,among many others. These databases often containlarge amounts of information ranging from georefer-enced species occurrence and abundance data tospecies trait data that can be used to inform selectionof umbrella and surrogate species. Selection of ap -pro priate species can be especially important whenmeasuring the impacts of ecosystem alteration, whenumbrella species must be chosen that are sensitive tothe alteration, of sufficient abundance for study andrepresentative of many other species in their re -sponses (e.g. surrogate species; Sattler et al. 2014).Just as important is providing justification for desig-nation of these species for study, especially when thespecies of interest is rare or of conservation concern.The designated umbrella or surrogate species inthese cases will be used to guide regulation and con-servation actions taken for the species of concern,making defensible justification for using a particularspecies of the utmost importance.

Although power analyses are available to providequantitative justification for the numbers of organ-isms used in an experiment, such techniques are notas frequently employed for the selection of umbrella

and surrogate species. There has been a variety ofpower analyses with different objectives reported inthe literature (e.g. genetics: Cornuet & Luikart 1996;fisheries biology: Mathur et al. 1996; and social, be -havioral and biomedical sciences: Faul et al. 2007),but few studies have focused on quantitative methodsof species selection using species traits. Sattler et al.(2014) used a combination of species traits and bio -logical diversity to quantitatively select umbrella spe-cies for ecosystem conservation and monitoring; how-ever, their method requires species abundance data,which may not make it appropriate for all species- monitoring and surrogacy-selection study designs.

In this study, we use a combination of geographicinformation system (GIS)-based species distributiondata, species traits and multivariate statistical analy-ses to step through 2 examples of a new, quantitative,traits-based approach for designating study speciesand surrogate species. As an example of the use ofspecies informatics databases for community or eco-system assessment and conservation, we offer a casestudy that focuses on incorporating fish species traitsthat indicate sensitivity to hydropower entrainment,that is, the likelihood of downstream movement offish through hydroelectric turbines, and the suscepti-bility of these fish to turbine passage stressors. Artifi-cial upstream and downstream fish passage meas-ures are often employed to circumvent the dams andto ameliorate the impacts on fish populations of eco-system fragmentation. Nonetheless, fish frequentlypass through hydropower turbines, where they mayencounter a variety of stressors such as extreme tur-bulence and shear forces, rapid pressure drops, andmechanical strike from turbine blades and otherstructures (>ada 2001). These stressors cause injuriesand mortalities that can exceed 30% for some species(Pracheil et al. 2016). Incorporation of fish trait infor-mation into hydropower design and schedulingrequires the understanding dose−response relation-ships between turbine stressors and fish injuries,which must be experimentally determined. Conduct-ing experiments is labor- and resource-intensive,making it impossible to determine quantitative dose−response relationships for all species. As a result, ourgoal is to choose a few species that are broadly rep-resentative of many species − a selection of umbrellaspecies for experimental studies − and whose traitsare similar to many other species such that we canmaximize our inferences about the effects of new tur-bine designs and operational strategies. Our casestudy stems from a study initiated by the US Depart-ment of Energy Hydropower Program, which seeksto incorporate biological information about fish into

Pracheil et al.: Traits-based species selection 245

the design of new hydropower turbines and thedevelopment of hydropower operating schemes withlower environmental impacts.

Moreover, there are situations where some speciesof interest are unavailable for study for logistical,permitting or other reasons (e.g. rare or protectedspecies). In those cases, a quantitative approach toselecting a surrogate species for study is needed. Inour second case study, we build on our first casestudy and present a framework for selecting a surro-gate species for an endangered species. In particular,this case study uses a traits-based approach to iden-tify a surrogate species for studying the effects ofdownstream passage in lieu of monitoring a pro-tected species, the US federally endangered pallidsturgeon Scaphirhynchus albus. We selected pallidsturgeon for this analysis because there has beenextensive documentation of surrogate study speciesfor monitoring and experimentation of pallid stur-geon that allows us to evaluate how well ourapproach compares with existing recommendationsof surrogate species (e.g. Quist et al. 2004) that arenot based on explicitly quantitative, traits-basedapproaches. We suggest that our traits-based frame-work can provide quantitative backing and addedjustification to selection of study species whileexpanding the inference space of study results.

MATERIALS AND METHODS

Both our species prioritization and species surrogacyselection methods rely on similar approaches and in-corporate the traits-based analysis framework pre-sented by Laliberté & Legendre (2010) for a distance-based measure of trait functional diversity. Ourspecific case studies are based on the traits-basedframework outlined by >ada & Schweizer (2012) fordetermining fish species’ sensitivity to turbine pas-sage. In brief, the methods we present here for moni-toring species prioritization and surrogate species se-lection use adult fish life-history traits that indicatesensitivity or resilience to a particular perturbation, inthis case to turbine passage. We use downstream hydropower passage stressors as inputs and then sta-tistically group species based on these traits (Fig. 1).

Both case study analyses were broadly interested inadult North American freshwater fish species and in-cluded all freshwater, anadromous and catadromousfish species from the NatureServe Explorer Fresh-water Fish of North America database (NatureServe2015). We then matched the fish distribution data withfish life history, morphological and trophic trait infor-

mation for adult fishes from across the USA using 2fish traits databases (Frimpong & Angermeier 2009,McManamay & Frimpong 2015) and identified traitsindicating: (1) whether a species was vulnerable toentrainment (e.g. entrainment vulnerability; Table 1and see Table S1 in Supplement 1 at www. int-res.com/ articles/ suppl/ n031 p243 _ supp1. pdf); (2) whether

Populate trait matrix

Calculate trait dissimilarity matrix

Use dissimilaritybetween species to determine suitability

Surrogate suitability

Cluster analysis

Score

Sum scores to determine priority

Species prioritization

Fig. 1. Workflow for traits-based species prioritization and surrogate suitability analyses

Entrainment Trait descriptionrisk ranking

1 Benthic, river or lake, non-migratory,moderate-fast current

2 Benthic, river, non-migratory, slow-moderate current

3 Pelagic, river and lake, migratory, slow-moderate current

4 Big, benthic, river migrants

5 benthic, river, moderate-fast current

6 Benthic, river, fast current

7 Benthic, river or lake, non-migratory,slow current

8 Pelagic, river and lake habitat, migra-tory, moderate current

Table 1. Hydropower turbine entrainment vulnerabilityfrom k-means clustering (k = 8). Entrainment risk rankingranks groups from least vulnerable to turbine entrainment(1) to most vulnerable to turbine entrainment (8) based onexpert opinion and published entrainment studies. Trait de-scription provides the general description of the types of life-history traits included in groups identified from the clusteranalysis. This dissimilarity matrix was constructed using fishtraits and included water-column position (pelagic or not),habitat (river, lake, river and lake), mobility (migratory ornot) and current velocity of habitat (slow, moderate or fast)

Endang Species Res 31: 243–258, 2016

a species was vulnerable to injury during turbine pas-sage (e.g. entrainment injury vulnerability; Table 2and Table S2 in Supplement 1); and (3) the relativeexposure of a species to turbines across its geographicrange (e.g. turbine exposure). Relative exposure of aspecies to hydroelectric turbines across its range wasdetermined by intersecting the National HydropowerAsset Assessment Program database — a spatial data-base containing location and plant attribute informa-tion such as turbine type and plant nameplatecapacity for hydropower facilities in the USA (Hadje-rioua et al. 2011) — and determining the number ofhydropower turbines of all types across a speciesrange. Our analyses included 450 extant North Amer-ican freshwater fish species for which we could obtainboth current distributional and traits information.

Our first case study, the species prioritization ana -lysis, additionally incorporated species conservationstatus (e.g. secure, threatened, endangered; Table 3,see Table S3 in Supplement 1) using range reductioninformation from the Fish Traits Database and con-servation status information from NatureServe. Wedid not include this information in the surrogacyanalysis because the need for selection of surrogatespecies may be due to the fact a species of interest istoo rare to study either in the field or in the laboratory.By incorporating population status into the analyses,

the method would be biased toward selecting specieswith similar population status. In other words, speciesof conservation concern may be shown as similar toother difficult-to-study species that may themselvesbe too rare to serve as a useful surrogate.

Case study 1: study species prioritization

We calculated functional trait diversity for each cat-egory of stressor using Gower dissimilarities (Gower1971) for all species pairs based on the fish traits in-cluded in our analysis (see Supplement 1) using the Rpackage Cluster (Maechler et al. 2014). Gower dis-similarities incorporate a mix of binary, continuousand discrete data in calculating the dissimilarityscores. We then used a Ward’s agglomerative cluster-

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Injury risk Taxa includedranking

1 Scaphirhynchus spp. and shortnosesturgeon

2 Small gar (e.g. shortnose, spotted)

3 Small catfishes (i.e. bullhead, madtom),lamprey

4 Cyprinids, catostomids, esocids,salmonids

5 All other sturgeons

6 Percids, centrarchids, mosquitofish

7 Giant gar, e.g. alligator and longnose

8 Big scaleless fish including paddlefish,blue catfish and sea lamprey

Table 2. Hydropower turbine injury vulnerability from k-means clustering (k = 8). The injury risk ranking ranks taxafrom least vulnerable to turbine injury (1) to most vulnerableto turbine injury (8) based on expert opinion and publishedturbine mortality rates. ‘Taxa included’ provides a generaldescription of the types of fishes included in groups identi-fied from the cluster analysis, but is not an all-encompassingdescription. This dissimilarity matrix was constructed usingfish morphological traits and included maximum total length,scale type (scaleless, cycloid, ctenoid, ganoid, scutes) and

swim bladder type (physoclistous or physostomous)

Population Descriptionrisk

1 Mix of game and non-game species; fewspecies of concern. Opportunistic andperiodic reproductive strategists

2 Mostly non-game fish, except catfish.Equilibrium reproductive strategists

3 Species of concern including alligator gar,blue sucker, paddlefish and green stur-geon. Periodic reproductive strategists

4 Desert sucker. Equilibrium reproductivestrategist

5 Cyprinids, darters and shads, some ofconservation concern. Opportunisticreproductive strategists.

6 Clupeids, cyprinids, lamprey of conserva-tion concern, many non-game fish not ofconservation concern. Equilibriumreproductive strategists.

7 Cyprinids and darters of conservationconcern. Opportunistic reproductivestrategists

8 Endangered sucker, sturgeon and chub.Periodic reproductive strategists

Table 3. Population vulnerability from k-means clustering (k =8). The threat score is a ranking of species groups from leastvulnerable to ecosystem alteration (1) to most vulnerable toecosystem alteration (8). Trait description provides the gen-eral description of the types of species included in groupsidentified from the cluster analysis. This dissimilarity matrixwas constructed using fish traits and species conservation statuses, and included habitat range reduction (minimal orsignificant), overexploitation (minimal or significant), anthro-pogenic threats (minimal or significant), range size (wide -spread or endemic), NatureServe conservation status, US En-dangered Species Act status and International Union for the

Conservation of Nature conservation status

Pracheil et al.: Traits-based species selection 247

ing method (R base 3.0.2, ‘Frisbee Sailing’; R Devel-opment Core Team 2014) to cluster species togetherbased on Gower dissimilarities for each stressor cate-gory. Each cluster was defined as species with ≤20%dissimilarity to each other, which resulted in 8clusters for entrainment vulnerability, entrainmentinjury and population vulnerability stressor cate-gories. Each stressor category in our analysis was de-fined by 8 clusters (k-means clustering; k = 8), al-though this was a matter of chance based ondissimilarities among species rather than design.

After grouping species into clusters, ranks wereassigned to each cluster by each of 4 of the authors(B.M.P., R.A.M., M.S.B., G.F.C.; hereafter labeled asA−D, in no particular order) based on relative vulner-ability, where clusters were scored from 1 to 8 inorder of increasing vulnerability. Ranks were deter-mined based on turbine mortality rates presented inPracheil et al. (2016) in addition to morphological,habitat and life history information associated withfish in each of the clusters. For example, the clustercontaining small gar (i.e. shorter maximum totallength [TL] as adults, specifically, longnose, short-nose and spotted gar) and sturgeons (i.e.Scaphirhynchus spp.) were ranked as resistant todownstream turbine passage because their heavyscales and integument presumably protect themfrom many injuries suffered during passage. In con-trast, the cluster containing centrarchids (sunfishesand black bass) was ranked as vulnerable to injurybecause of high mortality rates in past entrainmentstudies (e.g. Pracheil et al. 2016). Ranks were thenaveraged across authors and each rank was assignedthe nearest integer value, allowing for ties. Weassigned all fish species within a cluster the rank ofthat cluster.

We then calculated cumulative prioritization scorefor each species by summing the rank values fromeach stressor category with a standardized turbinepassage exposure score TE, which was calculated as:

TE = TR/Tmax × C (1)

where TR is the number of turbines within a species’range, Tmax is the maximum number of turbines re -ported within the range of any species in this study,and C is the standardizing coefficient (in this case,C = 8), because we wanted the weight of the turbinescore to be equal to that of the other individual stres-sors, which were ranked from 1 to 8. Finally, weranked species using these cumulative prioritizationscores from highest to lowest, where species with thehighest cumulative scores were determined to be thehighest priority for study.

Although our method for using a traits-basedapproach for selecting study species provides quanti-tative backing for species selection, this prioritizationapproach has several components that rely on expertopinion, which can be somewhat subjective. Resultsof the prioritization exercise will, no doubt, be sensi-tive to these subjectivities. We provide some exam-ples of how the subjective portions of our analysescan influence prioritization results on the upper 5%of species. The upper 5% of species were designatedas those with the highest species scores, where thespecies scores were rounded down to the nearestwhole integer. In cases where the 5% cut-off fell onspecies that had tied scores, all species with thatscore were included in the upper 5%.

We explicitly examined where and how variation inexpert opinion influenced results in 4 ways. (1) Weexamined the sensitivity of species prioritization toexpert opinion by comparing species lists ob tained bythe averaged expert opinion method we describedabove with species lists obtained using cluster rank-ings designated by each expert. We quantitativelycompared differences in species lists using Jaccard’scoefficient of community similarity — a metric thatprovides similarity in species composition for the up-per 5% of species irrespective of ranking, where a co-efficient of 1 indicates identical species in lists and acoefficient of 0 indicates no species in common be-tween lists — for each pair of experts. (2) We exam-ined sensitivity of the species lists to stressor cate-gories chosen by conducting simple linear regressionsbetween scores for each stressor category and speciesrank using the expert opinion averaged results. (3)We examined how sensitive overall species rank wasto individual expert rankings and averaged expertrankings using a Pearson's product-moment correla-tion coefficient. These analyses correlated speciesscores from the entrainment vulnerability stressorcategory with averaged species rank calculated fromall stressor categories. (4) We also used Pearson’sproduct-moment correlation co efficients to test thesensitivity of the results to using raw species scoresand ranks by correlating raw scores and ranks withaveraged scores and ranks, respectively.

Case study 2: surrogacy analysis

For the species surrogacy analysis, we incorpo-rated all traits from the entrainment vulnerability andentrainment injury vulnerability categories into a sin-gular matrix and calculated Gower dissimilaritiesbetween pallid sturgeon and each of the 170 fish spe-

Endang Species Res 31: 243–258, 2016

cies with ranges sympatric to that of the pallid stur-geon. Species with dissimilarity scores of 0 were per-fectly not dissimilar (i.e. functionally identical) basedon the traits matrix, indicating these species wouldbe the best possible surrogates. Species with dissim-ilarity scores of 1 were perfectly dissimilar based onthe traits matrix and would be the worst possible sur-rogates. We presented the lowest decile of species inaddition to using multidimensional scaling (MDS) todisplay how species were related to each other basedon the dissimilarity scores, where species pairs withlower dissimilarity scores were plotted closer to -gether and species with higher dissimilarity scoreswere plotted farther apart (cmdscale; R base).

Spatial distribution of hydropower risk

To better understand where the greatest risk ofhydropower turbines to fishes occur across the USA,according to our metrics, we calculated the turbinerisk score for each watershed, Rw, as:

Rw = ∑TE�Sw(NT) (2)

where TE is the standardized turbine passage expo-sure score calculated for a species (from Eq. 1) and Sw

is the number of species in a watershed w multipliedby the number of turbines in a watershed, NT. Wethen categorized watersheds into 1 of 7 equal per-centiles such that each category equaled approxi-mately 14.3% of the data, and mapped the water-sheds using ArcGIS 10.3.1.

RESULTS

Because clusters for generating turbine entrainmentinjury vulnerability, vulnerability to entrainment andpopulation vulnerability were informed using speciestraits; because traits such as habitat use, length andconservation status can vary within a fish taxon, clus-ters of fishes are not necessarily representative of phy-logeny. In fact, only the hydropower turbine injury vulnerability traits formed clusters that resembled phy-logeny, although only loosely so (Table 2). For instance,some of the primitive fishes, such as the Scaphi rynchusspp. sturgeons and the smallest of the gar species, clus-tered together in our analyses based on similarities incharacteristics of dermal armoring, scales and maxi-mum total length. The other cluster analyses for hy-dropower entrainment vulnerability (Table 1) and pop-ulation vulnerability (Table 3) were not generallystructured in concordance with phylo geny.

Case study 1: species prioritization and selection

Supplement 2 provides expert rankings assigned toclusters and species scores (Tables S4−S7 at www.int-res. com/ articles/ suppl/ n031 p243 _ supp2. xlsx) usedto inform prioritizations. The upper 5% of speciesidentified in our traits-based species selection forselecting broadly representative species sensitive tohy dropower system stressors had large speciesranges (Fig. 2). Most of these fishes have currentnative ranges in the eastern portion of the USA, inparticular, ranges that overlap with the high densi-ties of hydropower facilities in the northeast andsoutheast (Fig. 2). Nearly all the species in the upper5% list (Table 4) were not of any conservation con-cern, with the exception of the paddlefish Polyodonspathula, a species that was listed by the IUCN RedList as Vulnerable. None of the upper 5% specieswere listed as threatened or endangered by the USEndangered Species Act.Analyses of sensitivity of our prioritization methodsshowed that, while we did find that some risk cate-gories had higher correlations between average clus-ter score and species rank (e.g. some risk categorieshad r-values more than double those of other risk cat-egories; Fig. 3A), when looking at an individualstressor category, correlations between individualexpert assignments and final species prioritizationswere more homogeneous; further, in cases where 4experts were involved, very different rankings by 1expert did not strongly influence the averaged opin-ion of the group (Fig. 3B). We found that relation-ships between individual species scores as signed byexperts and composite species scores ob tained usingranks from all experts were similar across experts,but once expert and composite scores were placed inascending order and ranked, expert ranks and com-posite ranks were uncorrelated (Fig. 4). Concordancein top-20 species prioritizations varied betweenexperts (Jaccard’s coefficient of community similar-ity: 0.0566−0.2258; Table 5) and also between expertsand the averaged species prioritizations (Jaccard’scoefficient of community similarity: 0.0566−0.5200;Table 5).

Case study 2: designation of closely related surrogate species

Although the Gower dissimilarity matrix generatedwith our traits-based approach contains all pair-wisecombinations of all species used in this analysis, weonly present results for determining study surrogate

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Pracheil et al.: Traits-based species selection

species when examining the impacts of downstreamturbine passage for the federally endangered pallidsturgeon S. albus. Restricting this example to just onespecies allowed for ease of illustrating how to use ourapproach and interpret the results. The lower decileof dissimilarity scores (i.e. the species that are themost representative of pallid sturgeon) contained 17species: 9 cyprinids, 4 catostomids, 2 sturgeon, 1 cen-trarchid and 1 percid (Table 6). The blue sucker

Cycleptus elongatus — an obligate, large river cato -stomid — had the lowest dissimilarity score with pal-lid sturgeon, closely followed by the shovelnose stur-geon S. platorynchus, a congener of the pallidsturgeon. Although 2 additional sturgeon specieshave species ranges sympatric with the pallid stur-geon — lake sturgeon Acipenser fulvescens and At -lantic sturgeon A. oxyrinchus — only the Atlanticstur geon was listed in the lower decile of dissimilar-

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C) Pumpkinseed D) Yellow perch

E) Golden shiner F) Largemouth bass

A) Species richness B) Cumulative watershed turbine risk

1–1314–2324–3435–4647–5758–6970–8485–99100–119120–162 <0.001–0.026

0.026–0.0460.046–0.0660.066–0.1020.102–0.1730.173–0.3350.335–3.125

Fig. 2. Maps displaying fish (A) species richness (total number of fish species) in watersheds containing hydropower facilities (B)rarity-weighted turbine injury and mortality risk to fishes within a watershed containing hydropower turbines, and (C−F) distri-bution of selected fish species (shaded areas) from the top 5% priority species for assessing hydropower turbine-induced injury

and mortality by watershed and occurrence of hydropower turbines (black dots)

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ity scores. The lake sturgeon has amuch higher dissimilarity score of0.3812 (Fig. 5).

The MDS analysis using theGower dissimilarity matrix depictsa graphical approximation of thedissimilarities of all fish speciesused in the analysis in relation topallid sturgeon (Figs. 3 & 4). Inter-estingly, other primitive fishessometimes used as surrogates forsturgeon species, such as the lakesturgeon and the closely relatedpaddlefish Polyodon spa thu la, hadrelatively high dissimilarity scoreswith the pallid sturgeon.

Spatial distribution ofhydropower risk

The Northeast, Great Lakes andSoutheast USA comprised the areasof highest turbine-weighted meanwatershed hydropower risk (Fig. 2B).There were also several watersheds

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Rank Common name Scientific name No. Cumulative turbines score

1 Brook trout Salvelinus fontinalis 2339 1122 Yellow perch Perca flavescens 2513 1043 Pumpkinseed Lepomis gibbosus 2857 844 White sucker Catostomus commersonii 3100 725 Golden shiner Notemigonus crysoleucas 3072 666 Largemouth bass Micropterus salmoides 2071 657 Common shiner Luxilus cornutus 2498 648 Walleye Sander vitreus 1560 609 Redbreast sunfish Lepomis auritus 1616 5610 Spottail shiner Notropis hudsonius 1755 5611 Channel catfish Ictalurus punctatus 1541 5412 Banded killifish Fundulus diaphanus 1803 5213 Bluegill Lepomis macrochirus 1802 5214 Black crappie Pomoxis nigromaculatus 1585 5215 Longnose sucker Catostomus catostomus 1572 5216 Rockbass Ambloplites rupestris 1608 4817 Fathead minnow Pimephales promelas 1703 4818 Lake sturgeon Acipenser fulvescens 1216 4819 Gizzard shad Dorosoma cepedianum 1810 4420 Chain pickerel Esox niger 1684 4421 Redfin pickerel Esox americanus 1568 44

Table 4. Upper 5% of umbrella species traits-based prioritization. These are theupper 5% of species that are most sensitive to turbine stressors while still beingbroadly representative of other fish species. Cumulative score shows the riskscore calculated as the sum of entrainment vulnerability rank, entrainment injury rank and turbine exposure score for each species. No. turbines: number

of turbines in each species’ range

r=–0.52***

r=–0.84***

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r=–0.49***

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Fig. 3. Relationship between species rank (top panel; rank 1 = highest priority) and average risk group score obtained from allexperts with Pearson’s product moment correlation coefficients for the relationship between species rank and average riskgroup score. In panel (A), color of line, dots and r-value correspond to risk group as shown in key. In panel (B), color of line,dots and r-value correspond to risk score assigned to each species by each expert (denoted as A−D) and averaged from all ex-perts (denoted as All) based on entrainment risk. In both panels, gray shaded regions surrounding linear best-fit lines show

95% confidence intervals. ***p < 0.001

Pracheil et al.: Traits-based species selection 251

in the Pacific Northwest with high riskscores. Again, map legend categorieswere broken into 7 equal percentiles forease of display, so although some cate-gories have a large spread of values,they represent the range in a particularrisk category.

DISCUSSION

Trait-based approaches have a longhistory rooted in community ecologyand have been shown to be powerfulfor improving our understanding of bi-ological communities as drivers of eco-system function, process and turnover(McGill et al. 2006). Our ap proach forusing a traits-based framework for de -fining broadly representative speciesfor study and monitoring capitalizes onthe power of quantitative traits-basedapproaches (Laliberté & Legendre2010), further extending their utility tospecies conservation and management.Publically available traits databasessuch as those shown in Table 7 are be-coming more common for many differ-ent types of species, making our ap-proach also useful for prioritizingspecies for monitoring or designatingappropriate surrogate species for non-fish taxa. Moreover, be cause our frame-work is flexible and non-specific to lifestage, taxa or ecosystem, it can accom-modate data from existing traits data-bases, new traits databases as they be-come available, or additional traitsmeasured by re searchers for any num-ber of taxa, in any geographic area, and

can even accommodate demographically specifictraits. We did not weight any of the traits in our analy-ses and, conveniently, each measure of hydro powervulnerability separated into 8 clusters, making alltraits within a hydro power vulnerability group (i.e. in-jury vul nerability, conservation status, etc.) equallyweighted.

Although the framework we present here is quiteflexible, it is important to note that our approach doeshave some limitations, and the specific case study re-sults presented here are applicable only to the fishspecies and life stages we included in our analysisand only in rivers altered by hydropower. That is, we

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0 5 10 15 20 25

10

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20

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10

15

20

25

10

15

20

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Expert A

Expert B

Expert C

Expert D

Expert Expert A

Expert Expert B

Expert Expert C

Expert Expert D

Expert score Expert rank

r = 0.41***

r = 0.82***

r = 0.50***

r = 0.85***

r = 0.10*r = 0.10*

Expert A

Expert B

Expert C

Expert D

r = 0.10*

Fig. 4. Left panels: relationships between species score (highest score = highestpriority) averaged from all experts and score based on each individual expert’sinput. Right panels: relationships between species rank (highest rank = highestpriority) derived from score for all experts and rank derived from scores basedon each individual expert’s input. Expert A–D denotes which expert’s inputwas used to derive the relationship. Pearson’s product moment correlation coef-ficients are shown where correlations are significant at the α = 0.05 level (***p <0.001). In all panels, gray shaded regions surrounding the linear best-fit line

show 95% confidence intervals

A B C D Average

A 1 0.1875 0.0556 0.0556 0.0556B 1 0.1515 0.2258 0.5200C 1 0.2258 0.1515D 1 0.2667

Average 1

Table 5. Jaccard’s coefficient of community similarity for 20fish species with the highest composite scores between eachpair of experts. A coefficient of 1 indicates 2 experts had all ofthe same species in their lists of 20 species, and a coefficientof 0 indicates 2 experts had no species in common between

their lists. Experts are designated A, B, C, D

Endang Species Res 31: 243–258, 2016

selected traits that would in-dicate susceptibility or resist-ance of a fish species todownstream passage throughhydropower turbines, andapplying the list of speciesand traits from our analysesto predict species sensitivityto a different set of environ-mental perturbations or in adifferent type of ecosystemwould not provide meaning-ful results. For example, if re-searchers were looking toprioritize which fish specieswould be the best indicatorsof climate change impacts,they would not want to con-duct this prioritization basedon the suite of traits we use inour analyses. Rather, the re-searchers would want to se-lect traits that may predictsensitivity to changing tem-perature and precipitation

252

Rank Common name Scientific name Dissimilarity Range overlap (%)

1 Blue sucker Cycleptus elongatus 0.1121 882 Shovelnose sturgeon Scaphirhynchus platorynchus 0.1243 663 Blacktail redhorse Moxostoma poecilurum 0.1879 84 Sicklefin chub Macrhybopsis meeki 0.1986 605 Sturgeon chub Macrhybopsis gelida 0.2009 716 Shoal chub Macrhybopsis hyostoma 0.2013 327 Gravel chub Erimystax x-punctatus 0.2017 68 River redhorse Moxostoma carinatum 0.2083 69 Suckermouth minnow Phenacobius mirabilis 0.2095 3110 Silverjaw minnow Notropis buccatus 0.2112 511 Steelcolor shiner Cyprinella whipplei 0.2117 912 Bigeye chub Hybopsis amblops 0.2156 313 Highfin carpsucker Carpiodes velifer 0.2158 3214 Atlantic sturgeon Acipenser oxyrinchus 0.2497 415 Sand shiner Notropis stramineus 0.2522 5816 Shadow bass Ambloplites ariommus 0.2651 317 Stargazing darter Percina uranidea 0.2698 3

Table 6. Lower 10% of traits-based surrogate species analysis for the endangered pallidsturgeon Scaphirhynchus albus. Rankings are based on Gower dissimilarities of fish spe-cies traits for species with ranges that overlap with that of pallid sturgeon. Lower dissim-ilarity scores mean that fish have traits that are most like those of the pallid sturgeon. Therange overlap column shows the percentage of the pallid sturgeon range that is sym-

patric with each species, although it was not considered in our analysis

Atractosteus_spatula

Polyodon_spathula*Lepisosteus_osseus

Morone_saxatilis

Ictalurus_furcatus

Pylodictis_olivaris

Lota_lota

Esox_lucius

Ictalurus_punctatus

Salvelinus_namaycush

Ictiobus_cyprinellusIctiobus_niger

Ictiobus_bubalus

Lepisosteus_oculatusAmia_calva

Esox_niger

Oncorhynchus_clarkii

Micropteruss_almoides

Aplodinotus_grunniens

Sander_vitreus

Lepisosteus_platostomusMoxostoma_erythrurum

Thymallus_arcticus

Moxostoma_macrolepidotumMoxostoma_anisurum

Sander_canadensis

Prosopium_williamsoni

Micropterus_dolomieu

Ameiurus_melas

Carpiodes_cyprinus

Carpiodes_carpio

Catostomus_commersonii

Catostomus_catostomus

Micropterus_punctulatus

Dorosoma_cepedianum

Pomoxis_annularis

Hiodon_alosoides

Alosa_alabamae

Moxostoma_duquesnei

Alosa_chrysochloris

Perca_flavescensPomoxis_nigromaculatus

Hiodon_tergisus

Morone_mississippiensis

Morone_chrysops

Lepomis_microlophus

Ambloplites_rupestrisLepomis_macrochirus

Lepomis_gibbosus

Ichthyomyzon_unicuspis

Ichthyomyzon_castaneus

Esox_americanus

Platygobio_gracilis

Noturus_flavus

Lepomis_cyanellusLepomis_gulosus

Notemigonus_crysoleucas

Nocomis_biguttatusNocomis_leptocephalus

Catostomus_platyrhynchus

Lepomis_megalotis

Couesius_plumbeus

Macrhybopsis_storeriana Campostoma_anomalum

Dorosoma_petenense

Percopsis_omiscomaycus

Fundulus_catenatus

Ichthyomyzon_greeleyi

Cyprinella_venusta

Luxilus_cornutus

Lepomis_miniatus

Hybognathus_nuchalis

Percina_caprodes

Luxilus_chrysocephalus

Cottus_carolinaeEtheostoma_blennioides

Margariscus_margarita

Cyprinella_whipplei

Lepomis_humilis

Notropis_hudsonius

Noturus_exilis

Cottus_bairdii

Noturus_nocturnus

Menidia_beryllina

Noturus_phaeus

Aphredoderus_sayanusUmbra_limi

Noturus_stigmosus

Notropis_atherinoidesHybognathus_placitus

Noturus_gyrinus

Notropis_blennius

Labidesthes_sicculus

Percina_scieraNoturus_miurusNoturus_eleutherus

Hybognathus_argyritis

Phenacobius_mi rabilis

Lepomis_marginatus

Hybognathus_hayiPhoxinus_erythrogaster

Cyprinella_spilopteraPimephales_notatus

Percina_maculata

Erimystax_x−punctatuCyprinella_camura

Phoxinus_neogaeusPimephales_promelas

Macrhybopsis_meekiNotropis_shumardi

Fundulus_diaphanus

Luxilus_zonatusHybopsis_amblops

Etheostoma_crossopterum

Notropis_heterolepis

Notropis_buccatus

Hybognathus_hankinsoniFundulus_olivaceus

Percina_phoxocephala

Macrhybopsis_gelidaCyprinella_lutrensis

Lepomis_symmetricus

Fundulus_sciadicus

Notropis_boops

Culaea_inconstans

Lythrurus_umbratilis

Fundulus_chrysotus

Etheostoma_flabellare

Etheostoma_kennicotti

Notropis_st ramineus

Phoxinus_eos

Notropis_dorsalis

Fundulus_notatusFundulus_blairae

Percina_shumardi

Fundulus_dispar

Etheostoma_zonaleEtheostoma_caeruleum

Notropis_nubilus

Etheostoma_histrio

Notropis_topekaNotropis_maculatus

Macrhybopsis_aesti vHybopsis_winchelli

Etheostoma_nigrumEtheostoma_exile

Etheostoma_spectabile

Etheostoma_asprigene

Pimephales_vigilax

Lythrurus_fumeusHybopsis_amnis

Pimephales_tenellus

Notropis_chalybaeus

Notropis_longirostris

Notropis_buchanani

Etheostoma_gracileEtheostoma_chlorosoma

Percina_vigil

Etheostoma_fusiformeEtheostoma_microperca

Gambusia_affinis

Ambloplites_a riommusPercina_u ranidea

Machrybopsis meekiM. gelidaM. hyostomaErimystax x-punctatusPhenacobius mirabilisNotropis buccatusCyprinella whippeliHybopsis amblopsNotropis stramineus

Scaphirhynchus_albus

Acipenser_fulvescens*

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

−0.4 −0.2 0.0 0.2 0.4Dimension 1

Dim

ensi

on 2

Scaphirhynchus_platoryncusCycleptus_elongatus

Moxostoma_carinatumMoxostoma_poecilurum

Carpiodes_velifer

Acipenser_oxyrinchus

Fig. 5. Multidimensional scaling plot for species included in traits-based surrogacy analyses shown in reference to pallidsturgeon Scaphirhynchus albus. Fish species in black italic font are those in the lowest decile of similarity to pallid sturgeon(e.g. the most appropriate surrogates based on hydropower vulnerability traits). Fish species in the inset box are contained inthe cluster of species just below the box. Fish species in blue followed by asterisks are closely related species that fall outside

of the lowest decile of similar species

Pracheil et al.: Traits-based species selection 253

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Endang Species Res 31: 243–258, 2016

patterns such as those associated with phenology,optimal growing conditions or use of habitat vulnera-ble to climate change impacts. Similarly, studies in-terested in prioritizing local- or regional-scalespecies should first re strict the species included inprioritization analyses to those present in thephysical inference space for the study, as we did forthe species surrogacy analysis. Also, while our ap-proach is not specific to a particular life stage, the re-sults of the specific analyses presented in this paperonly apply to adults. The data bases we used to popu-late our traits matrices (Frimpong & Angermeier2009, McManamay & Frimpong 2015, NatureServe2015) contained information on such life history traitsas length and habitat use for adult fishes. It is there-fore possible that using traits of very young or juve-nile fishes or a particular age class may yielddifferent results. Unfortunately, comparatively littleis known about earlier life stages for most species offish, and conducting prioritizations that incorporatethem on a broad scale would be difficult.

Our species prioritization example deliberately didnot prioritize threatened or endangered species.Alternatively, we selected traits for our analyses thatwould not only prioritize species at-risk to entrain-ment and injury but also those that are widely dis-tributed so as to be able to draw inferences across awide array of ecosystems. For example, our methoddid not contain any threatened or endangered sal mo -nid (e.g. salmon and trout) or anguillid (e.g. eel) spe-cies that are often the source of hydropower mitiga-tion requirements. This is due in no small part to ouranalysis ‘rewarding’ species that had large rangesand ‘penalizing’ species with more restricted rangesthrough incorporation of the number of turbines in aspecies’ range. However, electing to study a speciesof concern over another species also generally re -quires little justification and the prioritization analy-sis we present would not be necessary.

We initially chose these case studies — prioritizingstudy species for understanding the effects of hydro -power on fishes in the USA and choosing study sur-rogates for inferring effects of ecosystem alterationson a rare, endangered species — because of priorpublished efforts that used qualitative means to ac -complish these objectives. In this way, we can com-pare previous qualitative assessments with our quan-titative assessment. Our attempt to prioritize studyspecies for monitoring, for example, focused on iden-tifying fish species that are the most broadly repre-sentative of fish species impacted by hydropower inthe USA, potentially also fish species that would begood targets for evaluating low-impact turbines to be

installed across the USA. Peer-reviewed studies con-cur with the findings of our method, demonstratingthat the species our method elevates as the highestpriority for studying the effects of turbine injury andmortality (Table 4) are either very commonly studiedin turbine entrainment studies (Pracheil et al. 2016)or very commonly entrained through hydropowerturbines. For example, a systematic review of fish tur-bine entrainment by Pracheil et al. (2016) tallied mul-tiple studies reporting entrainment of percids (mostlyyellow perch Perca flavescens), paddlefish and aci -pen serids such as lake sturgeon Acipenser fulve -scens. Similarly, Travnichek et al. (1993) andMichaud & Taft (2000) reported sunfishes (such aspumpkinseed Lepomis gibbosus), black bass (such aslargemouth bass Micropterus salmoides) and log-perch Percina caproides as being among the mostcommon constituents of entrainment catches amonghydropower facilities in the southern and midwest-ern USA. Gizzard shad ranks 19 in our prioritizationresults, is frequently entrained and is also among theleast dissimilar species to American shad — a speciesthat has been substantially impacted by hydropower(Haro et al. 1998, Castro-Santos & Haro 2015).

Interestingly, our surrogacy analysis suggests thatphylogeny is not necessarily a good indicator of whatspecies would best serve as surrogates, and rein-forces the power of a traits-based approach. This wasinteresting, although unsurprising because hydro -power entrainment vulnerability would not havebeen a selecting factor during the evolution of fishes,and more directly because clustering was based ontraits such as habitat use that do not necessarily maponto phylogeny. In our example, many of the speciesmost closely related to pallid sturgeon, such as stur-geon, paddlefish and gar, are deemed less suitablesurrogates than blue sucker and some chubs. This isimportant because we may intuitively bias our selec-tion of surrogate species towards phylogeneticallyrelated species even though there may be more ap -pro priate species based on traits that are sensitive tothe perturbation of interest. Paddlefish, for instance,have been named as an indicator of pallid sturgeonspawning success and habitat use (Dryer & Sandvol1993). Our analysis suggests that they would not beequally sensitive to hydropower system stressors. Infact, neither paddlefish nor lake sturgeon would begood surrogates in this case, likely because of theirdifferent habitat uses. For example, pallid sturgeonare found only in rivers while paddlefish and lakesturgeon are both found in rivers and lakes (Fig. 6B).Also, both paddlefish and lake sturgeon were associ-ated with slow current velocities while the pallid

254

Pracheil et al.: Traits-based species selection 255

−0.2

0.0

0.2

Dim

ensi

on2

−0.4 −0.2 0.0 0.2 0.4

Dimension1−0.4 −0.2 0.0 0.2 0.4

−0.2

0.0

0.2

−0.2

0.0

0.2

A B

C D

E F

Fig. 6. Multidimensional scaling plots (see also Fig. 5) showing patterns of clustering for species characteristics used to popu-late the trait matrix. Characteristics shown are as follows: (A) pelagic (gray: yes, black: no), (B) river or lake habitats (gray:both; black: river), (C) migratory (gray: yes; black: no), (D) slow current (gray: yes; black: no), (E) swim bladder type (gray:physoclistous; black: physostomous), and (F) scale type (blue: scutes; gray: ganoid; white: cycloid; pink: ctenoid; black: none).

The large dot in the upper right of each panel indicates the position of pallid sturgeon

Endang Species Res 31: 243–258, 2016

sturgeon was associated with fast current velocities(Fig. 6D). Interestingly, our surrogacy analysis findsblue sucker as the species with the lowest dissimilar-ity values to pallid sturgeon, although shovelnosesturgeon — a congener of the pallid sturgeon — hasbeen generally thought of as the best and most ap -pro priate study surrogate (Dryer & Sandvol 1993).However, blue sucker has also been named as astudy surrogate species for pallid sturgeon becauseof their similarities in benthic habitat use, spawningcues and migratory behavior (Quist et al. 2004). Itmust be reiterated, however, that our methods of des-ignating surrogate species differ from those of Dryer& Sandvol (1993) and Quist et al. (2004). First, weused a quantitative approach for designating surro-gates, while the previous assessments used qualita-tive approaches, although they were keen to con-sider traits in their surrogate designations. Second,while we were using basic life history and morpho-logical traits in our analysis, we honed in on traitsthat would be indicative of susceptibility or resist-ance to entrainment, and the addition or subtractionof certain traits may enable a different solution.Third, it is possible that we did not arrive at shovel-nose sturgeon as the best surrogate for pallid stur-geon because our prioritization did not include traitsinformation about hydrodynamics of body shape andaspects of life history such as spawning periodicitythat may make shovelnose sturgeon a superior surro-gate. As a result, we presented the entire lower de -cile of dissimilarity scores. Fourth, we did not applyweights to any of the traits. It may be that some traitsare more important than others for determiningwhether organisms are similar. For instance, if wehad weighted some scale types over others becausescutes — the sharp, bony plates on the backs of stur-geons — make sturgeons much more resistant toscale loss and other injury than fish with other scaletypes (Amaral et al. 2015), or weighted scale type asbeing more important to entrainment injury predic-tion than other traits such as habitat, our surrogacyanalysis would have provided a different prioritiza-tion. The differences in our findings and the findingsof Dryer & Sandvol (1993) and Quist et al. (2004)highlight the importance of carefully choosing thetraits that are incorporated into the analysis becauseinclusion or omission of traits, or over-/ under-weighting of any trait, can inadvertently provide dif-fering results and contribute to misleading interpre-tations and poor decisions.

Selecting species for study, monitoring or manage-ment activities can be somewhat subjective in theabsence of a rigorous quantitative framework, some-

times leaving these choices as the focus of scrutinyduring litigation. Some portions of our method relyon expert opinion — deciding which traits to include,assigning ranking scores to clusters of species —which can bear heavily on the results and introducemeasures of subjectivity into the quantitative analy-sis. First of all, it is important to remember that spe-cies selection decisions are typically made solely onwhat are the subjective portions of our analyses with-out incorporating an unbiased, quantitative methodfor comparing species and tracking species choice inthe decision-making process. In contrast, it is alsoimportant to remember that our process does notreplace expert knowledge. Our framework is able toprovide this quantitative backing to the choice ofspecies and to serve as a proxy for biological impactto an ecosystem (in the case of selecting a species forecosystem monitoring) or to the relationship betweena surrogate species and a threatened or endangeredone. Our methodology capitalizes on advances indata availability, coverage and computing power toprovide quantitative justification for a traditionallyqualitative process that can result in more easily de -fensible monitoring and conservation plans for im -pacted ecosystems. Our method did not deliberatelyweight any risk categories or expert opinions againstone another; however, risk categories with highercorrelations between average cluster score and over-all species rank had higher levels of consensusamong experts, whereas risk categories with higherdisagreement had lower correlations between aver-age cluster score and overall species rank. We dourge caution, however, in strict interpretations ofranks issued from composite species risk scores. Oursensitivity analyses do suggest that while compositespecies scores are fairly good representations of val-ues assigned by individual experts, translating thesescores into ranks can distort relationships betweenoriginal expert opinions and the composite scores de -rived from combining all opinions (Fig. 4). Neverthe-less, other portions of our sensitivity analyses showthat there can be at least some overlap in the top spe-cies designated by all experts (Table 6). Prioritizingthese species in common in the upper percentiles forall or several experts may be one way to help con-serve expert opinion in final species selection.

Acknowledgements. We thank Alison Colotelo and GaryJohnson at Pacific Northwest National Laboratory, andClayton Ridenour at the US Army Corps of Engineers forcomments on this manuscript. This study was funded by theUS Department of Energy (DOE) Energy Efficiency andRenewable Energy Office, Wind and Water Power Tech-nologies Program through Oak Ridge National Laboratory,

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which is managed by UT-Battelle, LLC, for the DOE undercontract DE-AC05-00OR22725. Fish distribution informationis provided by NatureServe (www.natureserve.org) and itsnetwork of natural heritage member programs, a leadingsource of information about rare and endangered species,and threatened ecosystems. Opinions expressed are those ofthe authors and do not reflect those of their employers. Thismanuscript has been authored by UT-Battelle, LLC underContract No. DE-AC05-00OR22725 with the US Departmentof Energy. The United States Government retains and thepublisher, by accepting the article for publication, acknowl-edges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to pub-lish or reproduce the published form of this manuscript, orallow others to do so, for United States Government pur-poses. The Department of Energy will provide public accessto these results of federally sponsored re search in accor-dance with the DOE Public Access Plan (http:// energy. gov/downloads/doe-public-access-plan).

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Editorial responsibility: Steven Cooke, Ottawa, Ontario, Canada

Submitted: February 22, 2016; Accepted: August 19, 2016Proofs received from author(s): October 31, 2016, 2016

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