Canadian Journal of Zoology

Evaluating policy-relevant surrogate taxa for conservation: a case study from British Columbia

Journal: Canadian Journal of Zoology

Manuscript ID cjz-2019-0178.R1

Manuscript Type: Note

Date Submitted by the 24-Sep-2019 Author:

Complete List of Authors: Falconer, Sarah; University of British Columbia Okanagan, Biology Ford, Adam; University of British Columbia Okanagan, Biology

Is your manuscript invited for consideration in a Special Not applicableDraft (regular submission) Issue?:

WILDLIFE MANAGEMENT < Discipline, conservation, game species, Keyword: < Habitat, indicator species

https://mc06.manuscriptcentral.com/cjz-pubs Page 1 of 37 Canadian Journal of Zoology

Evaluating policy-relevant surrogate taxa for biodiversity conservation: a case study from

British Columbia

Sarah Falconer a, Adam T. Ford a,b

a Department of Biology, The University of British Columbia - Okanagan Campus, 1177

Research Road, Kelowna, British Columbia, Canada V1V 1V7

b Corresponding author:

[email protected] Draft

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 2 of 37

2

Abstract

Conservation efforts often lead to a small proportion of species receiving a disproportionate amount of attention. This bias in finding may help or hinder broader goals of biodiversity conservation depending on the surrogacy value of the well-funded species. Surrogate species are selected to represent other taxa in a shared environment when it would be costly or impractical to obtain information on individual taxa. We compared the surrogacy value of common groups of taxa implicated in conservation – game species, , non-game species, and other species. Using a publicly available dataset of species-habitat associations, we compared the surrogacy value for 1012 species and 64 habitat types in British Columbia. We used a conditional entropy metric to quantify pairwise associations betweenDraft species via their occurrence in different habitat types. Our analysis reveals that game and non-game species surrogacy groups do not significantly differ in either the frequency of captured pairwise associations or their coverage of species. These results suggest that funding game species conservation is likely conferring some benefits to non-game species, but optimal habitat-based conservation outcomes will come from a combination of taxa. This analysis provides an important step in influencing management decisions for the preservation of biodiversity in British Columbia.

Keywords: conservation, game species, habitat, indicator species, wildlife management

https://mc06.manuscriptcentral.com/cjz-pubs Page 3 of 37 Canadian Journal of Zoology

3

Introduction

There is widespread, human-induced losses of biodiversity and wildlife population

declines driven by , climate change, diseases, and (Dirzo

et al. 2014). Habitat loss however, is considered the most important contemporary driver

of species declines (Tilman et al. 1994; Hanski 2011; Newbold et al. 2015). The human

footprint is spreading (Venter et al. 2016), giving rise to range contractions (Shackelford

et al. 2018), changing animal behavior (Gaynor et al. 2018; Tucker et al. 2018), and

altered evolutionary trajectories for wildlife (Otto 2018). To counter these declines,

global efforts are being made to prioritize both species and for protection (Martin

et al. 2018).

Implementing habitat protectionDraft often involves trade-offs among target species

and other values derived from land (Bottrill et al. 2008). Habitat protections typically

reduce opportunity to use land for urbanization, agriculture, or extractive resources,

which means that social, economic, and political pressures minimize the amount of land

allocated for protection. Moreover, the diverse life-history needs of different taxa means

that protections targeted at one species will not necessarily confer benefits to other

species (Hermoso et al. 2013; Barnes et al. 2018; Heim et al. 2019). In addition,

relationships between habitat and fitness are unknown for most species, making

protection of habitats that enhance population viability elusive for managers (Fahrig

2001). For these reasons, it will be very challenging to implement habitat protection for

all species – prioritization may be needed to maximize the benefits of limited

conservation resources (Martin et al. 2018).

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 4 of 37

4

The use of surrogate species in conservation is a common approach to prioritize species, and therefore maximize conservation outcomes with limited funds. A surrogate species is any species that is selected to represent another, or several others, in a shared environment (Caro 2010). Surrogate species are usually perceived to share characteristics that are unknown in other target species, and it would be costly or impractical to obtain that information (Landres et al. 1988; Caro et al. 1999; Rodrigues and Brooks 2007; Caro

2010; Nekaris et al. 2015). Conceptually, this definition of conservation surrogates includes umbrella species, which are used to indicate the extent or type of habitat to be protected (Nekaris et al. 2015), , which are selected because they attract public attention (Jepson and Barua 2015; Nekaris et al. 2015), and indicator species, which are species that have characteristicsDraft that can serve as a proxy for another species that might be too difficult to measure directly (Landres et al. 1988). Surrogate species are also used in natural resources and environmental monitoring to estimate or track changes to (Muir et al. 2011; Campbell et al. 2019).

Though the use of surrogate species is widespread in conservation and management, the manner in which this concept is commonly applied has important limitations. First, it is often not clear how representative a surrogate species is for broader ecological conditions, as relationships or co-occurrence patterns are often assumed

(Andelman and Fagan 2000; Caro 2010; Hermoso et al. 2013; Campbell et al. 2019;

Henry et al. 2019). Second, species identified or presumed to have high surrogacy value may not align with the knowledge and infrastructure of existing conservation funding mechanisms or established ecological knowledge (Schweizer et al. 2014). For example, an obscure species may accurately predict the occurrence of other species (i.e., it is a

https://mc06.manuscriptcentral.com/cjz-pubs Page 5 of 37 Canadian Journal of Zoology

5

strong indicator), but there may be limited information on the life history of the obscure

species that would help inform management. Conversely, species with established

funding streams may be common, and so underappreciated for their potential role as

conservation surrogates (Neeson et al. 2018). Third, many existing tests of surrogate

species focus on a subset of taxa, such as fishes and mollusks (Stewart et al. 2018), birds

(Morelli et al. 2017), and vertebrates (Meurant et al. 2018), with limited inferences

between plants, animals, and other higher-order taxonomic groups. These taxonomic foci

may not conform to existing conservation infrastructure, such as engagement campaigns

or agency expertise.

Funding and knowledge for wildlife management in North America typically

focuses on game species (i.e., those Draftharvested for recreation and subsistence hunting),

which have a high profile but whose populations are not usually at risk (Jacobson et al.

2007; Dalrymple et al. 2012). For example, in the USA, state wildlife agencies dedicate

only about 2 percent of their revenue to non-game species (Scheffer 1973), though

managed by or in conjunction with game management agencies may

affect this estimate (Restani and Marzluff 2002; Jacobson et al. 2010). Funding for game

species may contribute towards general wildlife management budgets and have spill-over

benefits for non-hunted species (Jacobson et al. 2007). However, in other cases, funding

for conservation of game species diverts funding for direct conservation of non-game

species (Darimont et al. 2018). In spite of the criticisms to this funding model (Artelle et

al. 2018a; 2018b) it is not clear if these funding biases are actually problematic from the

perspective of restoring and conserving biodiversity.

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 6 of 37

6

In addition to funding models associated with consumptive users, non- government conservation organizations often focus on a subset of biodiversity to advance their campaigns. Large mammals in general, and carnivores specifically, feature prominently on the covers of conservation magazines (Clucas et al. 2008), even if their ecological roles are frequently overstated (Allen et al. 2017). Quantifying the surrogacy value among game, non-game, and other species is an important step towards maximizing conservation outcomes from existing wildlife management and conservation infrastructure.

Using a matrix of categorical species-habitat associations, we derived a surrogacy metric by examining the degree to which large game species, large carnivores, and other species overlap in their habitat associationsDraft with terrestrial non-game species. In addition to game and guilds, we also evaluated the surrogacy value of non-game species, iterating the number of species in the surrogacy group to optimize the strength and coverage of species-habitat associations. We suggest that greater overlap in the habitat associations of species confers a stronger surrogacy value than weaker overlap.

Our goal was to determine which surrogate groups perform best at representing other taxa.

We focused our analysis on British Columbia, Canada’s most species-rich province (Canadian Endangered Species Conservation Council 2001). Currently, British

Columbia is re-examining policy on wildlife habitat management and seeking input on ways to fund wildlife management (Artelle et al. 2018a). As in many areas of North

America, there is a tension in British Columbia to increase opportunity for consumptive uses while also conserving non-game biodiversity (Butler et al. 2003; Artelle et al.

https://mc06.manuscriptcentral.com/cjz-pubs Page 7 of 37 Canadian Journal of Zoology

7

2018a). As such, there is a need to understand the extent to which current expertise and

funding streams of wildlife management agencies can be leveraged to maximize multiple

conservation outcomes. We shaped our analysis on the spatial planning units used by

wildlife and land managers in British Columbia (i.e., ‘wildlife management units’), each

of which may have local priorities, species representation, and budget constraints.

Methods

We obtained data on species-habitat associations from the Conservation Data Centre

(CDC), an agency of the Provincial Government of British Columbia provincial

government via (Retrieved July 7th 2017

https://www2.gov.bc.ca/gov/content/environment/plants-animals-Draft

ecosystems/conservation-data-centre). These data included 8684 species, of which 7830

were terrestrial and the rest aquatic or marine. Of these terrestrial species, 5856 had no

listed area of occurrence or habitat association, so were excluded from our analysis,

leaving a 1012 species in our data set for analysis (Tables S1, S2, S3 and Fig. S1). There

is an estimated 1140 vertebrate species present in British Columbia, which means that the

vertebrate species included in our analysis represent approximately 21% of those in B.C.

(https://www2.gov.bc.ca/gov/content/environment/plants-animals-

ecosystems/biodiversity). The remaining species in the data set mostly comprised of

vascular and non vascular plants (~40% and ~16% respectively). There were no

invertebrate animals in the dataset. In this case, the area of occurrence was one of nine

designated Wildlife Management Units (WMUs), which are administrative regions over

which hunting regulations, habitat restoration priorities, and funding for wildlife

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 8 of 37

8 conservation are allocated (Figure 1). The majority of the 5856 terrestrial species with no

WMU information were plants (n = 2612) or invertebrates (n =1879) (Table S2).

Habitat types were created and assigned to wildlife species by the CDC. Habitat types were categorical, and reflect the classifications used by IUCN species status assessments (IUCN/SSC 2017). To assign species to these habitat types, the CDC integrates information from a variety of sources including museum collections, published literature, unpublished reports, mapping, citizen science, and natural history groups (BC Conservation Data Centre 2018). Information about conservation or taxonomy is updated annually (BC Conservation Data Centre 2018). The associations between species and habitat types were binary. We recognize that a more realistic approach would be informed by the Draftstrength of species-habitat association, life history phase, or seasonality. Though our data are limited in this manner, these data are also relatively unique: they contain the associations of diverse taxa from plants, mammals, birds, and insects from a single jurisdiction using a relatively standardized approach across all species.

Data analysis

We used a conditional entropy statistic to measure the strength of pairwise associations in a species occurrence - habitat matrix. We used the netassoc package (Blonder and

Morueta-Holme 2017) in R (Version 1.1.383) to calculate an association score between each pairwise combination of species via their overlapping dependency on habitat categories (code available upon request from the authors).

https://mc06.manuscriptcentral.com/cjz-pubs Page 9 of 37 Canadian Journal of Zoology

9

We have no a priori information about the link between a given association score

and covariation in fitness or population density between two species. Our approach

assumes that the highest association scores represent a perfect match between habitats for

two species and the lowest scores represent an absence of surrogacy. A field-based

validation of these links were outside the scope of our study, given that this would

involve population performances of multiple taxes (plants, mammals, birds). Thus, we

assessed the percentile association scores to measure habitat-mediated surrogacy.

We created eight types of surrogate species groupings: (1) game species, (2)

ungulates, (3) large carnivores, (4) non-game species, and (5) four groups of aggregated

taxa. A given species may belong to more than one of these groupings (e.g., a species can

be both a game species and an ungulate).Draft Here, game species refer to large mammals and

include: bighorn sheep (Ovis Canadensis Shaw, 1804), cougar (Puma concolor Linnaeus,

1771), grizzly bear (Ursus arctos Linnaeus, 1758), American black bear (Ursus

americanus Pallas, 1780), grey wolf (Canis lupus Linnaeus, 1758), mountain goat

(Oreamnos americanus Blainville, 1816), mule deer (Odocoileus hemionus Rafinesque,

1817), white-tailed deer (Odocoileus virginianus Zimmermann, 1780), elk (Cervus

elaphus Erxleben, 1777), and moose (Alces alces Linnaeus, 1758 ). Ungulates consist of:

bighorn sheep, mountain goat, mule deer, moose, elk, and white-tailed deer. Carnivores

include: cougar, grizzly bear, grey wolf and the American black bear. For each species,

we calculated its mean pairwise association score with all other species. We then selected

the top 11 (i.e., equal in number to the number of game species) non-game species for

each region (see Table S3). The aggregated surrogate groups derived from the mean

pairwise association scores by selecting the top nth percentile species, regardless of their

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 10 of 37

10

game status (see Table S3). We used 80th, 90th, 95th and 99th percentile scores for this

ranking procedure. Lower percentile surrogate groups contain more species, but the

strength of the mean species association value is lower.

For each species within a surrogate group, we calculated its pairwise association

with all other species. We then determined how many pairwise associations exceeded

different thresholds of association. We used percentiles of all pairwise associations to

represent these thresholds: 1st, 50th, 80th, 90th, 95th, and 99th percentiles. If a pairwise

association exceeded the threshold value, the target species was considered ‘captured’ by

the surrogate group (Supplemental Information Figure S1). The lower percentiles are

more comprehensive in the number of species represented by surrogate group than higher

percentiles. The higher percentiles includedDraft fewer species, but the average strength of

association was higher.

We calculated the frequency of pairwise associations captured by each of the

eight surrogate groups (i.e., the 80th, 90th, 95th, and 99th aggregate, game, non-game, carnivores, and ungulates). This analysis measures the frequency of coverage for all unique pairwise associations per se (i.e., 95344 ± 47391 [mean± standard deviation] per region). In other words, we counted how many pairwise associations were captured by a surrogate group, out of all possible pairwise associations. Some of these pairwise associations are redundant, because more than one species in a surrogacy group captures the same non-surrogate species (Supplemental Information Figure S1). We therefore calculated the frequency of species (i.e., 300 ± 81 per region), rather than the frequency of pairwise association, captured by the surrogate groups. In this analysis, a non-

https://mc06.manuscriptcentral.com/cjz-pubs Page 11 of 37 Canadian Journal of Zoology

11

surrogate species is considered captured when >= 1 species in the surrogate group

exceeded the threshold value for the association score.

We summarized the frequency of pairwise interactions captured by a given

surrogate group and strength of association. For the 95th and 99th thresholds for

association strength, we performed an ANOVA with frequency of included pairwise

associations as the response variable, surrogate group as the predictor, replicated across

WMUs. We then used a Tukey’s Honest Significant Difference test to determine which

surrogate groups were significantly different from one another.

Results

Each species had 5.24 ± 4.83 (meanDraft ± standard deviation) habitat types associated with

them. Mule deer appeared in 33 different habitat types, which represented the widest

habitat breadth in the dataset. Among WMUs, there was a mean of 27-42 species per

habitat type, with ‘meadow’ having the largest number of species (79-167 species per

management unit).

Six of the eleven species identified in the 99th percentile surrogate group are game

species (Table 1), including: American black bear, elk (Rocky Mountain and Roosevelt

subspecies), grey wolf, grizzly bear, and mule deer. Of the species in the 99th percentile

surrogate group, only the American black bear and the rufous hummingbird (Selasphorus

rufus Gmelin, 1788) occurs in every WMU (Table 1). All of the species included in the

top 99th percentile surrogate group appeared in over 20 different habitat types (Table 2).

Grizzly bears appeared in the fewest habitat types of any species included in the top 99th

percentile surrogate group, with occurrences in 25 habitat types. Species included in the

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 12 of 37

12

top 99th percentile surrogate group appeared in 28 different habitat types. In the non-

game surrogate group, 75% of species were birds (Table 2). There were more species that

were found in all WMUs in the non-game species surrogate group, all of which were

birds: American kestrel (Falco spaverius Linnaeus, 1758), barn swallow (Hirundo rustica

Linnaeus, 1758), Barrow’s goldeneye (Bucephala islandica Gmelin, 1789), common

nighthawk (Chordeiles minor J. R. Forster, 1771), killdeer (Charadrius vociferus

Linnaeus, 1758), rufous hummingbird, and the tree swallow (Tachycineta bicolor

Vieillot, 1808).

At weaker thresholds of association (i.e., below the 50th percentile), all surrogate

groups captured all possible pairwise associations in the province (Figure 2). As the

strength of association threshold exceedsDraft 80th percentile, the surrogate groups diversify

in their performance. The top 80th percentile surrogate group captures the lowest

frequency of pairwise associations (> 25%), while the top 99th percentile has the overall best coverage of pairwise associations (~50%) (Figure 2). A post-hoc Tukey test indicated that game and non-game species surrogate groups do not significantly differ in the frequency of captured pairwise associations (P = 0.982). The game and non-game surrogate groups both cover > 25% of potential pairwise associations. The carnivore surrogate group performed significantly better than the ungulate group at high levels of association (P = 0.004), with carnivores covering approximately 25% of pairwise associations (Figure 2). This is consistent with other papers that suggest the importance of carnivores as biological indicators (Ripple et al. 2014; Allen et al. 2017).

Considering the proportion of species covered per se (rather than the proportion of pairwise associations) by surrogate groups, ungulates covered the fewest species

https://mc06.manuscriptcentral.com/cjz-pubs Page 13 of 37 Canadian Journal of Zoology

13

(~40% of species), while the 95th percentile surrogate group covered the most (~80% of

species) (Figure 3). The game and non-game surrogate groups did not significantly differ

in their coverage of species (P = 0.999), with both groups covering > 75% of species

(Figure 3). However, the carnivore surrogate group did significantly better than the

ungulate group (P = 0.035), covering greater than 50% of species, while the ungulate

group covered ~ 40% (Figure 3).

Discussion

Based on the association scores of 1012 species, mediated by 64 habitat types, the

best surrogate grouping to direct conservation funding are aggregate taxa composed of

both game and non-game species. OurDraft results reveal that the game species surrogate

group did not perform significantly better than the non-game surrogate group (Figure 3).

This finding is consistent with analyses conducted on similar data-sets for the Southern

California Coastal Sage Scrub Ecoregion, the Columbia Plateau Ecoregion, and the

continental United States, which found that large game species did not perform better

than a random selection of species from within those regions (Andelman and Fagan

2000).

Although the 11 species that comprised the non-game surrogate group did not

have higher average association scores than the game species surrogate group (Figures 2

and 3), our results suggest that there is not necessarily a need to restructure the existing

funding model to favour non-game species per se, nor to devalue the use of game species

as surrogate taxa. Considering the existing funding bias in British Columbia - large game

species currently receive a disproportionate amount of conservation funding (Dayer et al.

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 14 of 37

14

2016; Habitat Conservation Trust Foundation 2018-19 Approved Project List) - and our analysis suggests that this existing funding bias is likely adequate to conserve the habitats for a significant portion of species in British Columbia.

To maximize conservation outcomes in British Columbia, our analysis reveals that a mix of game and non-game species should be adopted as surrogates. The top 99th percentile surrogate group, which covered the most pairwise associations (Figure 2, bottom) contained both game and non-game species. Similarly, the Top 80th, 90th, 95th, and 99th surrogate groups also contained game and non-game species, and have a large

(>= 75%) proportion of captured species. These results suggest that conservation projects targeting non-game species will create benefits for game species, and vice-versa.

However, acting on these results in aDraft conservation management setting would require shifting conservation budgets and gaining stakeholder support to broaden definitions of

‘wildlife’. This transition may prove to be costly and time consuming, with unclear gains relative to the current emphasis on game species.

Our analysis also supports the use of carnivores as conservation surrogates, as this group captured significantly more pairwise associations and more species compared to the ungulate surrogate group (Figures 1 and 2, respectively). This finding is consistent with other research that has highlighted the use of large carnivores as umbrella species due to their expansive home ranges, and therefore increased species and habitat associations (Noss et al. 1996; Branton and Richardson 2011; Rozylowicz et al. 2011).

However, this approach has been criticized for failure to consider the connectivity of the respective habitat used by the carnivores, and the lack of a multi-taxonomic approach in these studies (Roberge and Angelstam 2004; Cushman and Landguth 2012). Therefore,

https://mc06.manuscriptcentral.com/cjz-pubs Page 15 of 37 Canadian Journal of Zoology

15

further studies that validate the use of carnivores of surrogates in general, and their

functional roles specifically, are needed to justify these taxa as a broad scale surrogate

group in British Columbia.

Our analysis was framed around the spatial planning or decision-making units

adopted by British Columbia’s resource management agencies (i.e., WMU - wildlife

management units). In British Columbia, each WMU is associated with some degree of

individualized hunting and trapping regulations, budgetary constraints, restoration

priorities, and stakeholder cultures. Our analyses match the scale and boundaries of these

decision-making units, thereby harmonizing these data to existing management

infrastructure. This scale of analyses differs from some common, ‘ecoregional’

approaches used in surrogate speciesDraft analyses, which may well match ecological process

and biogeographic patterns of diversity, but may not align with the decision making unit

of the agencies responsible for implementing conservation actions (Coristine et al. 2018;

2019; Rapacciuolo et al. 2018).

Our analysis addressed ecological and management redundancy through

examining the surrogacy of each species’ pairwise associations, wherein a single species

could be covered by more than one of the surrogate groups we selected. For example, the

long-tailed weasel (Mustela frenata Lichtenstein, 1831) appeared as a top-ranking

surrogate in both the non-game and 99th percentile surrogate groups (Tables 1 and 2).

Redundancy in ecological systems in general has been identified as a key factor in

increasing the stability of ecosystems and maintaining ecosystem functions (Naeem 1998;

Rosenfeld 2002; Goswami et al. 2017). Therefore, using surrogate groups that have

redundant associations with other species may help mitigate imprecision in species-

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 16 of 37

16 habitat data and provide a more robust assessment of covariation in fitness between surrogate and non-surrogate species.

Interestingly, the highest ranked surrogate species for each WMU, as well as the province itself, was the rufous hummingbird. The rufous hummingbird breeds farther north than any other hummingbird species, has the largest migration of any bird relative to body size (Moran et al. 2013), and is vulnerable to changes in habitat quality along it’s migratory route (Russell et al. 1994). Birds in general are considered good surrogates due to their broad and variable home ranges, short life spans, and generally accurate and abundant survey data (Järvinen and Väisänen 1979; Canterbury et al. 2000; Browder et al. 2002; Quinn et al. 2011). Our analysis supports these findings, as all of the species in the non-game surrogate group foundDraft to be high-ranking surrogates ubiquitously across each WMU were birds (Table 2). It is possible that the expansive home range of the rufous hummingbird contributed to its success as a surrogate species. Given the performance of birds as surrogate species, further studies linking bird fitness to other biodiversity indicators may be a more cost-effective approach to surveillance than quantifying trends in large mammals.

While these results have interesting implications for conservation and management, we were unable to validate this approach within the confines of this study.

A field-based validation in which species co-occurrences (or population trajectories) were recorded in respective habitat types would be ideal, however due to the large-scale nature of this study, it would be logistically impractical. Part of the challenge of validating a surrogate approach of this kind is the use of multi-taxa surrogate groupings. There is a lack of quality data surrounding spatially-distinct occurrences of multiple taxa, much of

https://mc06.manuscriptcentral.com/cjz-pubs Page 17 of 37 Canadian Journal of Zoology

17

which reflects taxonomic biases in conservation literature (Troudet et al. 2017). The use

of multiple surrogate species is gaining traction in conservation literature (Meurant et al.

2018) and our study represents a practical way to integrate this knowledge in decision -

making for multiple taxa approach in British Columbia.

There are at least three ways in which our study could be improved for future

analysis. First, there was a clear taxonomic bias in the species removed from our analysis

due to incomplete habitat association information, which could have influenced the

results. The majority of species with incomplete information are plants or invertebrates,

which is consistent with taxonomic biases towards more charismatic species apparent in

both conservation literature (Clark 2002; Ford et al. 2017), as well as general scientific

literature (Troudet et al. 2017). To expandDraft the results of this study and studies like it,

better data needs to be collected for these underrepresented species. Second, this study is

spatially implicit in the sense that associations were based off co-occurrence in habitat

types, not necessarily demographic rates, interactions, or locations. This meant that we

could not prioritize certain interactions, such as mutualistic relationships or trophic

cascades, that may be relevant for ecosystem level conservation (Ritchie and Johnson

2009; Harvey et al. 2017). Third, there is a need to develop surrogacy measures that

address the co-occurrence of species and the contributions of habitat types to species

diversity (Rapacciuolo et al. 2018). A logical next step in this field is to quantify

surrogacy of fitness or population density, rather than occurrence per se.

Our findings suggest that focusing conservation attention - specifically habitat

restoration - on game species is likely to confer benefits to many other species in British

Columbia. Our results also suggest that a non-game per se surrogate group would not be

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 18 of 37

18 significantly better at covering species associations, and therefore the current bias of conservation funding towards game species is likely adequate for providing benefits to broader biodiversity conservation objectives. However, based on our results, conservation efforts are maximized by focusing efforts on a mix of game and non-game species. This outcome could be achieved by facilitating cooperation among groups advocating for the consumptive and non-consumptive values of wildlife.

Acknowledgements

This research was supported by the Canada Foundation for Innovation, the National

Sciences and Engineering Research Council (Canada), and the Canada Research Chairs program. Comments from J. Pither helpedDraft shape the direction of this research. We thank

Drs. J.T. Fisher and R. Schuster for their thoughtful comments on an earlier version of this manuscript.

https://mc06.manuscriptcentral.com/cjz-pubs Page 19 of 37 Canadian Journal of Zoology

19

References

Allen, B.L., Allen, L.R., Andrén, H., Ballard, G., Boitani, L., Engeman, R.M., et al.

2017. Can we save large carnivores without losing large carnivore science? Food

Webs, 12: 64–75. doi:10.1016/j.fooweb.2017.02.008.

Andelman, S.J., and Fagan, W.F. 2000. Umbrellas and flagships: efficient conservation

surrogates or expensive mistakes? Proc. Natl. Acad. Sci. U.S.A. 97(11): 5954–9.

doi:10.1073/pnas.100126797.

Artelle, K.A., Moola, F.M., Paquet, P.C., and Darimont, C.T. 2018a. British Columbia’s

Wildlife Model Reform. Science, 361(6401): 459–460.

doi:10.1126/science.aau4222.

Artelle, K.A., Reynolds, J.D., Treves,Draft A., Walsh, J.C., Paquet, P.C., and Darimont, C.T.

2018b. Hallmarks of science missing from North American wildlife management.

Sci. Adv. 4(3): eaao0167. doi:10.1126/sciadv.aao0167.

Barnes, M.D., Glew, L., Wyborn, C., and Craigie, I.D. 2018. Prevent perverse outcomes

from global protected area policy. Nat. Ecol. Evol. 2(5): 759-762.

doi:10.1038/s41559-018-0501-y.

BC Conservation Data Centre. 2018. BC Species Ecosystems Explorer. B.C. Ministry of

Environment, Victoria B.C. Available:

https://www2.gov.bc.ca/gov/content/environment/plants-animals-

ecosystems/conservation-data-centre/explore-cdc-data/faq [Accessed 31 January

2018]

Blonder, B., and Morueta-Holme, N. 2017. Inference of Species Associations from Co-

Occurence Data. Available from https://cran.r-

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 20 of 37

20

project.org/web/packages/netassoc/netassoc.pdf.

Bottrill, M.C., Joseph, L.N., Carwardine, J., Bode, M., Cook, C., Game, E.T., et al. 2008.

Is conservation triage just smart decision making? Trends Ecol. Evol. 23(12): 649-

654. doi:10.1016

Branton, M., and Richardson, J.S. 2011. Assessing the Value of the Umbrella-Species

Concept for Conservation Planning with Meta-Analysis. Conserv. Biol. 25(1): 9–20.

doi:10.1111/j.1523-1739.2010.01606.x.

Browder, S.F., Johnson, D.H., and Ball, I.J. 2002. Assemblages of breeding birds as

indicators of grassland condition. Ecol. Indic. 2(3): 257–270. doi:10.1016/S1470-

160X(02)00060-2.

Butler, J.S., Shanahan, J., and Decker,Draft D.J. 2003. Public attitudes toward wildlife are

changing: a trend analysis of New York residents. Wildl. Soc. Bull. 31(4): 1027–

1036. doi:10.2307/3784448.

Campbell, M.A., Kopach, B., Komers, P.E., and Ford, A.T. 2019. Quantifying the

impacts of oil sands development on wildlife: perspectives from impact assessments.

Environ. Rev.doi:10.1139/er-2018-0118.

Canadian Endangered Species Conservation Council. 2001. Wild Species 2000: The

General Status of Species in Canada. Ottawa.

Canterbury, G.E., Martin, T.E., Petit, D.R., Petit, L.J., and Bradford, D.F. 2000. Bird

Communities and Habitat as Ecological Indicators of Forest Condition in Regional

Monitoring. Conserv. Biol. 14(2): 544–558. doi:10.1046/j.1523-1739.2000.98235.x.

Caro, T.M., O ’Doherty, G., and O ’Dohertyt, G. 1999. On the Use of Surrogate Species

in . Conserv. Biol. 13(4): 805–814.

https://mc06.manuscriptcentral.com/cjz-pubs Page 21 of 37 Canadian Journal of Zoology

21

Caro, T. 2010. Conservation by proxy: Indicator, Umbrella, Keystone, Flagship and other

surrogate species. Island Press, Washington, DC.

Clark, J.A. 2002. Taxonomic Bias in Conservation Research. Science, 297(5579): 191b –

192. doi:10.1126/science.297.5579.191b.

Clucas, B., McHugh, K., and Caro, T. 2008. Flagship species on covers of US

conservation and nature magazines. Biodivers. Conserv. 17(6): 1517–1528.

doi:10.1007/s10531-008-9361-0.

Coristine, L.E., Jacob, A.L., Schuster, R., Otto, S.P., Baron, N.E., Bennett, et al. 2018.

Informing Canada’s commitment to biodiversity conservation: A science-based

framework to help guide protectedDraft areas designation through Target 1 and beyond.

FACETS, 3(1): 531–562. doi:10.1139/facets-2017-0102.

Coristine, L.E., Colla, S., Bennett, N., Carrlsson, A., Davy, C., Davies, et al. 2019.

National contributions to global ecosystem values. Conserv. Biol. 33: 1219-

1223.doi:10.1111/cobi.13284.

Cushman, S.A., and Landguth, E.L. 2012. Multi-taxa population connectivity in the

Northern Rocky Mountains. Ecol. Modell. 231: 101–112. Elsevier.

doi:10.1016/J.ECOLMODEL.2012.02.011.

Dalrymple, C.J., Peterson, M.N., Cobb, D.T., Sills, E.O., Bondell, H.D., and Dalrymple,

D.J. 2012. Estimating public willingness to fund nongame conservation through

state tax initiatives. Wildl. Soc. Bull. 36(3): 483–491. doi:10.1002/wsb.164.

Darimont, C.T., Paquet, P.C., Treves, A., Artelle, K.A., and Chapron, G. 2018. Political

populations of large carnivores. Conserv. Biol. 32: 747-749.

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 22 of 37

22

doi:10.1111/cobi.13065.

Dayer, A.A., Bright, A.D., Teel, T.L., and Manfredo, M.J. 2016. Application of a Stated

Choice Approach to Assessing Public Preferences for

Funding. Hum. Dimens. Wildl. 21(5): 379–390.

doi:10.1080/10871209.2016.1169565.

Dirzo, R., Young, H.S., Galetti, M., Ceballos, G., Isaac, N.J.B., and Collen, B. 2014.

Defaunation in the Anthropocene. Science, 345(6195): 401–406.

doi:10.1126/science.1251817.

Fahrig, L. 2001. How much habitat is enough? Biol. Conserv. 100(1): 65–74.

doi:10.1016/S0006-3207(00)00208-1.

Ford, A.T., Cooke, S.J., Goheen, J.R.,Draft and Young, T.P. 2017. Conserving megafauna or

sacrificing biodiversity? Bioscience, 67(3): 193-196. doi:10.1093/biosci/biw163.

Gaynor, K.M., Hojnowski, C.E., Carter, N.H., and Brashares, J.S. 2018. The influence of

human on wildlife . Science, 360(6394): 1232–1235.

doi:10.1126/science.aar7121.

Goswami, M., Bhattacharyya, P., Mukherjee, I., and Tribedi, P. 2017. Functional

Diversity: An Important Measure of Ecosystem Functioning. Adv. Microbiol. 7(01):

82–93. doi:10.4236/aim.2017.71007.

Habitat Conservation Trust Foundation 2017-18 Approved Project List. Victoria B.C.

Available: https://hctf.ca/wp-content/uploads/2019/04/HCTF-Project-List-2017-

18.pdf [Accessed April 19 2018]

Hanski, I. 2011. Habitat Loss, the Dynamics of Biodiversity, and a Perspective on

Conservation. Ambio, 40(3): 248–255. doi:10.1007/s13280-011-0147-3.

https://mc06.manuscriptcentral.com/cjz-pubs Page 23 of 37 Canadian Journal of Zoology

23

Harvey, E., Gounand, I., Ward, C.L., and Altermatt, F. 2017. Bridging and

conservation: from ecological networks to ecosystem function. J. Appl. Ecol. 54(2):

371–379. doi:10.1111/1365-2664.12769.

Heim, N., Fisher, J.T., Volpe, J., Clevenger, A.P., and Paczkowski, J. 2019. Carnivore

response to anthropogenic landscape change: species-specificity foils

generalizations. Landsc. Ecol. 34 (11): 2493–2507. doi:10.1007/s10980-019-00882-

z.

Henry, E., Brammer-Robbins, E., Aschehoug, E., and Haddad, N. 2019. Do substitute

species help or hinder endangered species management? Biol. Conserv. 232: 127–

130. Elsevier. doi:10.1016/J.BIOCON.2019.01.031.

Hermoso, V., Januchowski-Hartley,Draft S.R., and Pressey, R.L. 2013. When the suit does not

fit biodiversity: Loose surrogates compromise the achievement of conservation

goals. Biol. Conserv. 159: 197–205. doi:10.1016/j.biocon.2012.11.026.

IUCN/SSC. 2017. The IUCN Red List of Threatened Species.Version 2017-3. Available

from https://www.iucnredlist.org/resources/summary-statistics#Summary%20Tables

[Accessed 31 January 2018]

Jacobson, C.A., Decker, D.J., and Carpenter, L. 2007. Securing Alternative Funding for

Wildlife Management: Insights from Agency Leaders. J. Wildl. Manage. 71(6):

2106–2113.. doi:10.2193/2006-442.

Jacobson, C.A., Organ, J.F., Decker, D.J., Batcheller, G.R., and Carpenter, L. 2010. A

Conservation Institution for the 21st Century: Implications for State Wildlife

Agencies. J. Wildl. Manage. 74(2): 203–209. Wiley-Blackwell. doi:10.2193/2008-

485.

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 24 of 37

24

Järvinen, O., and Väisänen, R.A. 1979. Changes in bird populations as criteria of

environmental changes. Ecography, 2(2): 75–80. doi:10.1111/j.1600-

0587.1979.tb00684.x.

Jepson, P., and Barua, M. 2015. A Theory of Flagship Species Action. Conserv. Soc.

13(1): 95. doi:10.4103/0972-4923.161228.

Landres, P.B., Verner, J., Thomas, J.W. 1988. Society for Conservation Biology

Ecological Uses of Vertebrate Indicator Species : A Critique. Conserv. Biol. 2(4):

316–328. doi:10.1111/j.1523- 1739.1988.tb00195.x

Martin, T.G., Kehoe, L., Mantyka-Pringle, C., Chades, I., Wilson, S., Bloom, R.G., et al.

2018. Prioritizing recovery funding to maximize conservation of endangered

species. Conserv. Lett. 11(6): e12604.Draft doi:10.1111/conl.12604.

Meurant, M., Gonzalez, A., Doxa, A., and Albert, C.H. 2018. Selecting surrogate species

for connectivity conservation. Biol. Conserv.227: 326 -334

Moran, J.A., Wassenaar, L.I., Finlay, J.C., Hutcheson, C., Isaac, L.A., and Wethington,

S.M. 2013. An exploration of migratory connectivity of the Rufous Hummingbird

(Selasphorus rufus), using feather deuterium. J. Ornithol. 154(2): 423–430.

doi:10.1007/s10336-012-0906-3.

Morelli, F., Møller, A.P., Nelson, E., Benedetti, Y., Liang, W., Šímová, P., Moretti, M.,

and Tryjanowski, P. 2017. The common cuckoo is an effective indicator of high bird

in Asia and Europe. Sci. Rep. 7: Article 4376 doi:10.1038/s41598-

017-04794-3.

Muir, J., Hawkes, V.C., Tuttle, K.N., and Mochizuk, T. 2011. Synthesis of Habitat

Models used in the Oil Sands Region. Report prepared for the Cumulative

https://mc06.manuscriptcentral.com/cjz-pubs Page 25 of 37 Canadian Journal of Zoology

25

Environmental Management Association (CEMA), Fort McMurray, Alberta by

LGL Limited Environmental Research Associates, Sidney, British Columbia.

LGL Report EA3259. 76 pp.

Naeem, S. 1998. Species Redundancy and Ecosystem Reliability. Conserv. Biol. 12(1):

39–45.

Neeson, T.M., Doran, P.J., Ferris, M.C., Fitzpatrick, K.B., Herbert, M., Khoury, M., et al.

2018. Conserving rare species can have high opportunity costs for common species.

Global Change Biol. 24(8): 3862–3872. doi:10.1111/gcb.14162.

Nekaris, K., Arnell, A.P., and Svensson, M.S. 2015. Selecting a conservation surrogate

species for small fragmented habitats using modelling. Animals,

5(1): 27–40. doi:10.3390/ani5010027.Draft

Newbold, T., Hudson, L.N., Hill, S.L.L., Contu, S., Lysenko, I., Senior, R.A., et al. 2015.

Habitat Loss and Extinction in the Hotspots of Biodiversity. Brazilian J. Biol.

104(4): 909–923. doi:10.1046/j.1523-1739.2002.00530.x.

Noss, R.F., Quigley, H.B., Hornocker, M.G., Merrill, T., and Paquet, P.C. 1996.

Conservation Biology and Carnivore Conservation in the Rocky Mountains.

Conserv. Biol. 10(4): 949–963.

Otto, S.P. 2018. Adaptation, speciation and extinction in the Anthropocene. Proc. R. Soc.

B. 285(1891): 20182047. doi:10.1098/rspb.2018.2047.

Quinn, J.E., Brandle, J.R., Johnson, R.J., and Tyre, A.J. 2011. Application of detectability

in the use of indicator species: A case study with birds. Ecol. Indic. 11: 1413–1418.

doi:10.1016/j.ecolind.2011.03.003.

Rapacciuolo, G., Graham, C.H., Marin, J., Behm, J.E., Costa, G.C., Hedges, S.B., et al.

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 26 of 37

26

2018. as a surrogate for conservation of phylogenetic and

functional diversity in terrestrial vertebrates across the Americas. Nat. Ecol. Evol. 3:

53-61. 1.doi:10.1038/s41559-018-0744-7.

Restani, M., and Marzluff, J.M. 2002. Funding Extinction? Biological Needs and

Political Realities in the Allocation of Resources to Endangered Species Recovery.

Bioscience, 52(2): 169-177. doi:10.1641/0006-

3568(2002)052[0169:FEBNAP]2.0.CO;2.

Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Ritchie, E.G., Hebblewhite, M.,

et al. 2014. Status and ecological effects of the world’s largest carnivores. Science,

343(6167): 151-163. doi:10.1126/science.1241484.

Ritchie, E.G., and Johnson, C.N. 2009.Draft Predator interactions, release and

biodiversity conservation. Ecol. Lett. 12(9): 982–998. doi:10.1111/j.1461-

0248.2009.01347.x.

Roberge, J.-M., and Angelstam, P. 2004. Usefulness of the umbrella species concept as a

conservation tool. Conserv. Biol. 18(1): 76-85. doi:10.1111/j.1523-

1739.2004.00450.x.

Rodrigues, A.S.L., and Brooks, T.M. 2007. Shortcuts for Biodiversity Conservation

Planning: The Effectiveness of Surrogates. Annu. Rev. Ecol. Evol. Syst. 38(1): 713–

737. doi:10.1146/annurev.ecolsys.38.091206.095737.

Rosenfeld, J.S. 2002. Functional Redundancy in Ecology and Conservation. Oikos, 98(1):

156–162.

Rozylowicz, L., Popescu, V.D., Pǎtroescu, M., and Chişamera, G. 2011. The potential of

large carnivores as conservation surrogates in the Romanian Carpathians. Biodivers.

https://mc06.manuscriptcentral.com/cjz-pubs Page 27 of 37 Canadian Journal of Zoology

27

Conserv. 20(3): 561–579. doi:10.1007/s10531-010-9967-x.

Russell, R.W., Carpenter, F.L., Hixon, M.A., and Paton, D.C. 1994. The impact of

variation in stopover habitat quality on migrant rufous hummingbirds. Conserv.

Biol. 8(2): 483–490. doi:10.1046/j.1523-1739.1994.08020483.x.

Scheffer, V.B. 1973. The Future of Wildlife Management. Wildl. Soc. Bull. 4(2): 51–54.

Schweizer, M., Ayé, R., Kashkarov, R., and Roth, T. 2014. Conservation action based on

threatened species capture taxonomic and phylogenetic richness in breeding and

wintering populations of Central Asian birds. PLoS One, 9(10): e110511.

doi:10.1371/journal.pone.0110511.

Shackelford, N., Standish, R.J., Ripple, W., and Starzomski, B.M. 2018. Threats to

biodiversity from cumulative humanDraft impacts in one of North America’s last wildlife

frontiers. Conserv. Biol. 32(3): 672–684. doi:10.1111/cobi.13036.

Stewart, D.R., Underwood, Z.E., Rahel, F.J., and Walters, A.W. 2018. The effectiveness

of surrogate taxa to conserve freshwater biodiversity. Conserv. Biol. 32: 183-

194.doi:10.1111/cobi.12967.

Tilman, D., May, R.M., Lehman, C.L., and Nowak, M.A. 1994. Habitat destruction and

the . Nature, 371(6492): 65–66. doi:10.1038/371065a0.

Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R., and Legendre, F. 2017.

Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. 7(1): 9132.

Nature Publishing Group. doi:10.1038/s41598-017-09084-6.

Tucker, M.A., Böhning-Gaese, K., Fagan, W.F., Fryxell, J.M., Van Moorter, B., Alberts,

S.C., et al. 2018. Moving in the Anthropocene: Global reductions in terrestrial

mammalian movements. Science, 359(6374): 466-469.

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 28 of 37

28

doi:10.1126/science.aam9712.

Venter, O., Sanderson, E.W., Magrach, A., Allan, J.R., Beher, J., Jones, K.R., et al. 2016.

Sixteen years of change in the global terrestrial human footprint and implications for

biodiversity conservation. Nat. Commun. 7(1): 12558. doi:10.1038/ncomms12558.

Draft

https://mc06.manuscriptcentral.com/cjz-pubs Page 29 of 37 Canadian Journal of Zoology

29

Tables

Table 1. Species selected (shown by the “•”) for the top 99th percentile surrogate group

for each wildlife management unit. Association score shown for the Province-wide

assessment only.

Wildlife management unit Association Province- score Species 1 2 3 4 5 6 7A 7B 8 wide (mean ±SD) American black bear • • • • • • • • • • 0.65 ± 0.04 (Ursus amricanus)

Barn swallow • 0.63 ± 0.07 (Hirundo rustica) Draft Coyote • • 0.64 ± 0.06 (Canis latrans) Rocky Mountain Elk • 0.64 ± 0.05 (Cervus elaphus) Grey Wolf • • • • • • • • 0.65 ± 0.05 (Canis lupus) Grizzly bear • 0.63 ± 0.04 (Ursus arctos) Long-tailed weasel • 0.64 ± 0.06 (Mustela frenata)

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 30 of 37

30

Mule deer • • • • • • 0.64 ± 0.06 (Odocoileus hemionus) Rufous hummingbird • • • • • • • • • • 0.66 ± 0.04 (Selasphorus rufus) Tree swallow • • 0.64 ± 0.05 (Tachycineta bicolor)

Draft

https://mc06.manuscriptcentral.com/cjz-pubs Page 31 of 37 Canadian Journal of Zoology

31

Table 2. Species selected (shown by the “•”) non-game game surrogate group for each

wildlife management unit.

Wildlife management unit

Species 1 2 3 4 5 6 7A 7B 8 Province-wide

American goldfinch

(Spinus tristis) • • • • • • •

American kestrel

(Falco spaverius) • • • • • • • • • •

Bald eagle Draft (Haliaeetus • • • leucocoephalus)

Barn swallow

(Hirundo rustica) • • • • • • • • • •

Barrow's goldeneye

(Bucephala islandica) • • • • • • • • •

Common nighthawk

(Chordeiles minor) • • • • • • • • • •

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 32 of 37

32

Common ringlet

(Coenonymoha tullia) • • • •

Coyote • • • • • • • • (Canis latrans)

Gyrfalcon

(Falco rusticolus) • • •

Killdeer (Charadrius • • • • • • • • • • vociferous) Draft

Long-tailed weasel

(Mustela frenata) • •

Peregrine falcon

(Falco peregrinus) • • • • • • • •

Rufous hummingbird

(Selasphorus rufus) • • • • • • • • • •

Tree swallow • • • • • • • • • • (Tachycineta bicolor)

https://mc06.manuscriptcentral.com/cjz-pubs Page 33 of 37 Canadian Journal of Zoology

33

Wolverine • • • • • • (Gulo gulo)

Figures: Draft

Figure 1. Province of British Columbia showing the 9 wildlife management units

(WMUs) that are used to guide wildlife management planning. Figure was created using

ArcGIS version 10.5 and assembled using data from British Columbia Wildlife

Management Units Data Catalogue (accessible online at

catalogue.data.gov.bc.ca/dataset/wildlife-management-units). Catalogue published by the

British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural

Development - Wildlife and Habitat and Licensed under "Open Government Licence -

British Columbia" (https://www2.gov.bc.ca/gov/content/data/open-data/open-

government-licence-bc) Base map from Esri "Light Gray Canvas Map" Scale Not Given.

"Canvas Base"

(https://services.arcgisonline.com/ArcGIS/rest/services/Canvas/World_Light_Gray_Base/

MapServer) Last Accessed: Dec. 6, 2019. Created: Sep. 26, 2011; Updated: Jan. 9, 2020.

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 34 of 37

34

Figure 2. Proportion of pairwise associations capture by the 8 surrogate groups we tested for (top) all levels of association strength and (bottom) for the 99th percentile strength of association. Error bars represent the standard error, observed over the 9 wildlife management units of British Columbia. Letters above the bars indicate significantly different groups (P < 0.05) from a Tukey’s Post-Hoc comparison.

Figure 3. Proportion of species captured by the 8 surrogate groups we tested for (top) all levels of association strength and (bottom) for the 99th percentile strength of association.

Error bars represent the standard error, observed over the 9 wildlife management units of

British Columbia. Letters above the Draftbars indicate significantly different groups (P < 0.05) from a Tukey’s Post-Hoc comparison.

https://mc06.manuscriptcentral.com/cjz-pubs Page 35 of 37 Canadian Journal of Zoology

Draft

190x254mm (300 x 300 DPI)

https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 36 of 37

Draft

190x254mm (300 x 300 DPI)

https://mc06.manuscriptcentral.com/cjz-pubs Page 37 of 37 Canadian Journal of Zoology

Draft

190x254mm (300 x 300 DPI)

https://mc06.manuscriptcentral.com/cjz-pubs