PREDICTING VULNERABILITY OF FISHES
by
Stacey Lee O’Malley
A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Ecology and Evolutionary Biology University of Toronto
© Copyright by Stacey O’Malley 2010
Predicting Vulnerability of Fishes
Stacey Lee O’Malley
Master of Science
Department of Ecology and Evolutionary Biology University of Toronto
2010 Abstract
Conservation biology would benefit from methods that identify species at risk in a proactive manner, rather than through post-hoc conservation assessments. This study examines the utility of four potential indices for predicting vulnerability in fishes: total body length; trophic level; intrinsic vulnerability score; and, resilience. Statistical analysis was done to determine if correlations existed between any of these four indices and known levels of risk in marine and freshwater Canadian fishes. Results show the success of two of these indices to predict risk: fished species over 78.33 centimeters total length, or with intrinsic vulnerability scores over
57.41 are more highly vulnerable to becoming at risk. Over 20% of Canadian fished species of unknown conservation status are therefore currently vulnerable, and possibly at risk of extinction. This study shows vulnerability indices allow a rapid prioritization of fishes at risk of extinction, and can thus help achieve proactive conservation even in the absence of population decline data.
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Acknowledgements
Firstly, I would like to thank my supervisor Prof. Mart Gross for his support, guidance and assistance throughout the duration of my work at the University of Toronto, and for inspiring me to pursue graduate studies in conservation biology. Mart, more than anyone else you have expanded my thinking about conservation issues, and you have helped me to realize my potential to make a difference as a conservation biologist. Thank you.
I thank the David Suzuki Foundation (DSF) for their partnership through the NSERC Industrial Postgraduate Scholarship program. I would like to thank Dr. Scott Wallace, Sustainable Fisheries Analyst at DSF for his supervisory support, as well as the Marine Conservation Team and the rest of the DSF staff for welcoming me into their office and community.
My lab members have provided me the opportunity to discuss the work presented here, and I thank Blake Turner, Eric Davies, Dan Clarke, Sandra Neill, Kristen Hahn and Sarah Hasnain.
Additionally, I thank my supervisory committee members Dr. Lisa Manne and Douglas Macdonald, PhD. who have assisted in focusing my research. I thank as well Dr. Don Jackson, Dr. Brian Shuter and Dr. Joe Repka for discussions on statistics and mathematics, as well as graduate students Brie Edwards, Monica Granados and Bronwyn Rayfield.
Finally, I would like to extend appreciation to my friends and family for their support and encouragement. I particularly thank Amy Stevens Yee and Duane Petts for their editing assistance.
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Table of Contents
List of Tables ...... vi
List of Figures...... vii
List of Appendices ...... viii
Predicting Vulnerability of Fishes ...... 1
1 Introduction...... 1
2 Materials and Methods...... 3
2.1 Overall analytical design...... 3
2.2 Data sources...... 4
2.3 Vulnerability indices and categories...... 5
Body size (SIZE)...... 5
Trophic level (TROP) ...... 5
Intrinsic vulnerability score (IVUL) ...... 6
Resilience (RESL) ...... 6
2.4 Statistical analyses and comparisons ...... 7
3 Results...... 9
3.1 Comparative overview...... 9
3.2 Predictive capacity of indices ...... 10
Predictive success ...... 10
3.3 Predictive capacity by cause of endangerment ...... 11
Predictive success ...... 12
3.4 Misclassifications...... 12
3.5 Predicting vulnerability of Canada’s fishes ...... 13
4 Discussion ...... 13
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4.1 What makes an index successful?...... 14
4.2 Why do indices work for some species and not others? ...... 17
4.3 Uncertainty and misclassification...... 18
False positives...... 18
False negatives...... 19
4.4 Data considerations...... 20
4.5 Predicting Vulnerability in Canada’s Fish Fauna ...... 21
4.6 Conclusions...... 22
References...... 24
Tables...... 29
Figures...... 39
Appendices...... 46
v
List of Tables
• Table 1. Vulnerability index values for Canadian fish species at risk and not at risk.
• Table 2. Spearman correlations between vulnerability index values for assessed Canadian fish species (at risk and not at risk), n=166.
• Table 3. Analysis of variance (ANOVA) examining effects of external variables on capacity for vulnerability indices to predict risk.
• Table 4. Capacity for vulnerability indices to predict Canadian fish species at risk: cross- validated logistic regression output.
• Table 5. Threshold values of body size (maximum length) and intrinsic vulnerability score in predicting Canadian fish species at risk and not at risk, as determined by cross-validated logistic analysis.
• Table 6. Extrinsic threats to Canadian fishes.
• Table 7. Capacity for vulnerability indices to predict Canadian fish species at risk under particular extrinsic threats.
• Table 8. Cross-validated threshold values for species’ vulnerability to fishing related threats.
• Table 9. Cross-validated threshold values for species’ vulnerability to overfishing and bycatch threats.
• Table 10. Species misclassified by both body size and intrinsic vulnerability indices at probability cutoff point 0.5.
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List of Figures
• Figure 1. Conservation-assessed Canadian fish species (n=166) that are at risk or not-at- risk of extinction, classified by fishing and environment type.
• Figure 2. Classification success of vulnerability indices to predict risk, using cross- validated models in receiver-operating characteristic (ROC) space.
• Figure 3. Classification success of vulnerability indices to predict under the threat of fishing in Canadian fish species, using cross-validated models in receiver-operating characteristic (ROC) space.
• Figure 4. Frequency distribution and vulnerability of fished Canadian species of unknown risk status.
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List of Appendices
• Appendix 1. Vulnerability index values for assessed Canadian species.
• Appendix 2. Vulnerable Canadian species of unknown conservation status.
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Predicting Vulnerability of Fishes
1 Introduction
Only about 11% of the world’s 31 000 fish species have been assessed for their conservation status (IUCN 2008), in contrast to other taxonomic groups such as mammals and birds, for which 100% of species have known conservation status. Most of the remaining 89% of unassessed fish species lack reliable quantitative information on long-term population trends, even for species captured in fisheries. The absence of this information limits the application of quantitative criteria commonly used in assessment and assignment of conservation status. It also limits the recognition of risk until the species has collapsed, is endangered, and has declined too far to be recoverable (Hutchings and Reynolds 2004). This limitation also suggests the need for an approach to the assessment of fishes that will allow managers and conservation biologists, in the absence of quantitative trend data, to predict vulnerable fish species before their populations have collapsed (e.g., CBD 2006, Sala and Knowlton 2006, Worm et al. 2006).
The possibility of guiding assessment using “vulnerability indices” (sometimes called “extinction promoting traits”) has attracted considerable interest (McKinney 1997, Russell et al. 1998, Purvis et al. 2000, Fisher and Owens 2004). Vulnerability is defined here as the intrinsic components of a species’ biology, such as life history or ecological variables, which increase sensitivity to or inhibit recovery from a particular external threat. For instance, late age of maturity may increase sensitivity and inhibit recovery to fishing pressure because individuals are more likely to be captured before spawning, and populations take longer to rebuild when the threat is removed (Stearns 1992). From an ecological perspective, predators may have high vulnerability to fishing if energy flow limits the number of possible individuals in predator populations (Jennings and Reynolds 2007). Vulnerability, as defined here, is distinct from extinction risk: species are not necessarily currently at risk, but due to the combination of biological and extrinsic factors, may be at increased probability of becoming at risk in the future.
The search for the most useful traits in predicting vulnerability has spanned marine (Jennings et al. 1998, Reynolds et al. 2005a, Cheung et al. 2005) and freshwater fishes (Angermeier 1995, Parent and Schriml 1995, Duncan and Lockwood 2001, Reynolds et al. 2005b, Olden et al. 2008). 1
Worldwide, major threats are overfishing for marine fishes and habitat loss for freshwater fishes (Reynolds et al. 2002, Reynolds 2005a, Musick et al. 2000, Jelks et al. 2008, IUCN 2008). This pattern of threat has also been documented in Canada (Venter et al. 2006). However, despite vulnerability having both intrinsic (life history and ecological causation) and extrinsic components (characteristics of the threat), few authors have related internal biological factors to the external causes of endangerment (Olden et al. 2008). It is thought that morphological characters, life history and ecological traits may predict vulnerability in marine fishes (Dulvy et al. 2003, Reynolds et al. 2005a), but have less predictive capacity for freshwater fishes (Angermeier 1995, Duncan and Lockwood 2001). It is not known whether this difference is due to the environmental context (marine versus freshwater) or to the nature of the threat (fishing versus habitat impacts).
This study examined the utility of four vulnerability indices for predicting extinction risk in fishes: body size, trophic level, intrinsic vulnerability, and resilience. These variables are quantified on the FishBase database (Froese and Pauly 2009). Body size is an important life history variable for fishes due to its association with growth rate, natural mortality, longevity, age at maturity and reproductive output (e.g., Hildrew et al. 2007); it is also linked to energy requirements and patterns of predation (Jennings et al. 2001). Species of larger body size are believed to be more vulnerable than smaller fishes (McKinney 1997, Reynolds et al. 2005a, Jennings and Reynolds 2007). Trophic level is an ecological variable reflecting complex interactions within the biotic and abiotic community. Species with high trophic levels are believed to be more vulnerable than species with low trophic levels (Pauly et al. 1998, Jennings et al. 2001, Sibert et al. 2006). Intrinsic vulnerability is a composite score developed for fishes by Cheung et al. (2005) that incorporates both life history characteristics and ecological variables. These include maximum length, age at maturity, growth, mortality, maximum age, geographic range, low fecundity, and aggregating behaviour (Cheung et al. 2005). High intrinsic vulnerability correlates with population declines (Cheung and Pitcher 2008) and increased risk of extinction (Morato et al. 2006, Cheung et al. 2007). Finally, resilience, as developed by Musick (1998), is used for interpreting vulnerability by the American Fisheries Society (Musick et al. 2000). Resilience is considered the ‘inverse’ of vulnerability (Cheung et al. 2005), and includes growth, fecundity, age at maturity, and maximum age (Froese and Pauly 2009).
The purpose of this study is to determine if any of the four indices can predict vulnerability in Canadian fish species. The four indices were tested for their capacity to predict conservation status of these species, independent of population decline data. The question was posed: for a given threat,
2 is it possible to use these intrinsic characteristics of a fish species to predict its vulnerability? If relationships do exist between vulnerability indices and their risk status, there is a potential for application in conservation biology: both for assessment and prioritization of species for protection.
Canadian fishes provide an excellent model as they occupy three oceans (Arctic, Atlantic, Pacific) and thousands of freshwater lakes and rivers (Minns et al. 2008). They are comparatively well studied and have a long history of fishing and habitat impacts (Hart 1980, Scott and Crossman 1985, Scott and Scott 1988). These fishes are also assessed for risk by an independent body of conservation scientists, the Committee on the Status of Endangered Wildlife in Canada (COSEWIC, 2009) and the international World Conservation Union (IUCN 2009), and through these bodies their primary threats are identified and quantified. The predictive capacity of vulnerability indices was assessed in the context of the environment occupied by fish (freshwater, diadromous and marine) and the exploitation status of the species (fished or unfished). The relationships between vulnerability indices and known threats, including overfishing and habitat loss, were also examined. The findings provide a greater understanding of the internal and external factors under which the risk of extinction of fishes can be predicted in the absence of population trend data.
2 Materials and Methods 2.1 Overall analytical design
To examine the question of whether vulnerability of fishes could be predicted through use of indices, Canadian fishes were used as a model. From the initial data set, each species was assigned as freshwater , diadromous or marine. They were also assigned as fished or unfished , to take into account external factors with putative differences in predictive capacity. Conservation assessments from COSEWIC and IUCN were then examined for their assignment of species at risk or not at risk; these assessments provided the main variable of risk status examined in this study. Primary causes of endangerment (e.g., overfishing, habitat loss) were also identified in the conservation assessments for each species. Vulnerability index values were collected or calculated for all species from information on FishBase (Froese and Pauly 2009). Statistical analyses were undertaken to determine whether or not vulnerability indices provided a means of
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distinguishing species at known risk from those not at risk, and thresholds values for indices were determined and cross-validated. SPSS (ver. 17.0) was utilized for all statistical analyses.
2.2 Data sources
A complete list of Canadian marine, freshwater, and diadromous fishes was obtained from Fisheries and Oceans Canada (n=1389; January 2009, Fisheries and Oceans Canada, St. Andrew’s Biological Station). This government database serves as the authoritative list of fishes in Canada for the Wild Species 2005: General Status of Species in Canada report. In total, 166 of these species have previously been assessed for risk of extinction and their conservation status and threats were obtained from COSEWIC (2009) and the IUCN Red List (2008). These 166 species were divided into those “at risk” of extinction (103 species, 62%) and “not at risk” (63 species, 38%). Data on body size, trophic level, intrinsic vulnerability and resilience were then extracted for each species from FishBase (Appendix 1).
COSEWIC assessments are either at the species level (30%) or the population level, called a ‘Designatable Unit’ (DU, 70%; COSEWIC 2008). Where more than one population was listed by COSEWIC, the highest risk status was used for the species (e.g., winter skate has 4 DU listings of endangered, threatened, special concern and data deficient; endangered was used for this analysis). Therefore, one assessment was used per species. Where both IUCN and COSEWIC assessments were available for a single species, COSEWIC’s conclusion was used. IUCN and COSEWIC differed in status assignment for 32% of species in common (n = 8/25). For statistical analyses, threat status categories were combined under the headings of ‘risk’ (RISK) and ‘not at risk’ (NAR). RISK included COSEWIC categories of Special Concern, Threatened, and Endangered; and IUCN categories of Near Threatened, Vulnerable, Endangered, and Critically Endangered.
Risk was assessed in relation to the specific external threats to a species. Specific threat information was available for most of the species at risk (85.4%), and threats were characterized into two main categories: fishing and habitat, and five subcategories: overfishing, bycatch, habitat loss, pollution or degradation, and ‘other’ (Appendix 1). Some species have membership in more than one group, as many faced more than one threatening factor.
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2.3 Vulnerability indices and categories
Vulnerability indices examined in this study were selected for a number of reasons. First, each has been used as a measure of estimating vulnerability in the literature (e.g., Lloret et al. 2008) or in practice (e.g., Musick et al. 2000), but not all indices have undergone stringent testing with multiple data sets to ascertain their utility for this purpose. Secondly, it was important here to choose indices that have values widely available for the most number of species, to ensure utility even for species for which few data are available on population status. Lastly, indices were selected due for their range of biological rationales for being linked to vulnerability: ecology (trophic level), life history (resilience), morphology (body size), and a combination of these and behavioural traits (intrinsic vulnerability). FishBase (Froese and Pauly 2009) was used to find vulnerability index values for all species.
Body size (SIZE) : Many fish species have measures of total length (greatest length of body taken in straight line from most anterior to most posterior point). The maximum total body length value was used for body size, as it was the most common variable available for size on FishBase (Froese and Pauly 2009) for this data set. For species with only standard length (tip of snout to end of caudal peduncle) and fork length measures (tip of snout to base of forked tail), length-length conversion factors available on FishBase were used to convert to total length. Length measures for nine species without conversion factors (e.g. width of disc measurements for rays) were included in the analysis, as only this length measurement was available. Microsoft Excel (ver. 2.1) was used to log-10 transform the total length data for statistical analyses.
Trophic level (TROP) : In fishes, trophic levels range from 2.0 (primary consumers, e.g., tilapias) to 4.5 (tertiary consumers, e.g., billfishes; Pauly and Christensen 1995). The measures for trophic level for each species in this study were collected from FishBase (Froese and Pauly 2009). This measure of TROP is an estimate, based on a randomized re-sampling of prey items known from a species’ diet (Froese and Pauly 2009). This estimate may differ from those estimates based on diet composition or isotopic analyses; however, this estimate from food items is the most commonly available TROP measure. In the data, where a trophic level ‘from food items’ estimate was absent and a value ‘from diet composition’ was present, the latter was included in the analysis (n=3). Missing trophic levels for 8 species were estimated by averaging the TROP estimates available for congeneric species (1-4 species, as available).
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Intrinsic vulnerability score (IVUL) : Intrinsic vulnerability is a composite score recently developed for fishes by UBC Fisheries Centre (Cheung et al. 2005) that incorporates life history characteristics, behavioural attributes and ecological variables: maximum length, age at maturity, growth, mortality, maximum age, geographic range, low fecundity, and aggregating behaviour. For this measure, the minimum input is body size, but all data available for each species is used (i.e., species for which all information is known would have values based on all input variables). An output score from a fuzzy logic system ranges from 1 to 100; where 100 is most highly vulnerable (Cheung et al. 2005). IVUL values were available as per FishBase (Froese and Pauly) for all species in this analysis.
Resilience (RESL) : Resilience as developed by Musick (1998) is used as a component of larger conservation assessment for the American Fisheries Society (Musick et al. 2000), which also include population decline criteria. As reported on FishBase (Froese and Pauly 2009), RESL includes a specific set of life history traits: growth, fecundity, age at maturity and maximum age: however, only one of these measures is necessary for a resilience score within the FishBase database. RESL is a categorical variable with four categories: high, medium, low and very low vulnerability; species with high RESL being most highly resilient, and those with very low RESL being most vulnerable. As with IVUL, RESL values were available on FishBase for each species in this analysis.
Species were grouped in categories of freshwater, diadromous, and marine species in order to examine differences in vulnerability with environment (Angermeier 1995, Duncan and Lockwood 2001, Reynolds et al. 2005b). Species listed on FishBase as occurring in freshwater, marine and brackish environments were classified as diadromous. To determine whether species targeted for fishing differed in their inherent vulnerability from those that were not fished, species were grouped as “Fished” (59%) and “Unfished” (41%). Species were classified as “Fished” when noted in FishBase as being ‘ minor commercial’ ‘commercial,’ ‘highly commercial,’ ‘ subsistence fisheries ’ or ‘game fish .’ Fisheries ‘ of potential interest,’ ‘ of no interest,’ and species with no information were classified as “Unfished.”
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2.4 Statistical analyses and comparisons
Statistical analyses on assessed Canadian fish species were conducted in three distinct steps, followed by a final application back to species of unknown risk status. The first was to understand the relationships among the vulnerability indices, and which indices provided significant ability to distinguish between species known to be at risk and not at risk. The second step examined the predictive power of the vulnerability indices independently, and their classification success. The third step took into account specific threats faced by species, and again examined predictive power and classification success for these indices. Finally, threshold values determined by analyses above were applied to the full data set of Canadian fishes of unknown conservation status to examine vulnerability of species without previous conservation assessment.
The first analytical step was to understand and elucidate relationships between indices, as well as between indices and other external factors, such as environment and risk status. A correlation analysis was performed between all vulnerability indices to see the overlap in their explanatory power. Following this correlation analysis, a three-way analysis of variance (ANOVA) was conducted. Here, three categorical variables of risk status (RISK/NAR), environment (freshwater, diadromous or marine) and fishing status (fished/unfished) were compared against vulnerability index values for species. This provided for statistical control for each categorical variable, as well as accounted for any interaction effects (e.g., between habitat and fishing).
In the second step to validate the predictive capacity of the vulnerability indices, logistic regressions were performed on the full data set (n=166) of assessed species, as well as five randomized subsets of this data to allow for cross-validation. Logistic regression provides a prediction of binary variables (RISK/NAR), by continuous variables (vulnerability indices). This analysis was performed separately for each vulnerability index. Chi-square statistics were examined for significance at the p=0.05 level, and Nagelkerke pseudo-R squared values were employed to understand differences the strength of the relationships between the indices and determination of RISK/NAR. The general logistic equation is
P log = b + b x + b x +⋅⋅⋅+ b x 1− P 0 1 1 2 2 n n
7 where b is a constant, P is the probability value of group membership, and x is an index value. In this analysis, b was calculated through the logistic regression, P an assigned cutoff point for membership to the RISK group, and x a vulnerability index value for a species.
Maximized threshold values ( x) for vulnerability indices were determined for the purposes of misclassification analysis. Maximized probability values ( Pmax ) were determined by performing iterations of the same logistic regression tests, to examine classification success at probabilities between 0.1 and 1.0, at intervals of 0.1. The maximized P was determined at the point where both ‘at risk’ and ‘not at risk’ species had highest overall classification success (‘accuracy’). The final maximized threshold x was determined by solving for the above equation at the maximized probability value.
As noted above, analysis was cross-validated to ensure statistical rigor. Species were randomly assigned into to five groups (A, B, C, D, and E), with proportionally balanced RISK to NAR species within each group. Logistic regressions were conducted, and maximized thresholds determined, for a subset comprising of four of the five groups (e.g., ABCD). Classification success was then determined by applying this threshold to the group excluded from the model (e.g., E). This process was carried out five times, with logistic regressions performed on each of subsets ABCD, ABCE, ABDE, ACDE, BCDE; and predictive capacity examined against the excluded group.
The third step of statistical analysis involved an examination of explanatory power and classification success for indices when extrinsic threats were considered. Statistical tests used for this stage of analysis were similar to those used above in the second step. However, these analyses incorporated specific threat factors for species, as outlined in conservation assessments from COSEWIC and
IUCN (RISK (threat) ). Rather than distinguishing between RISK/NAR as used in the second step, analyses in this step examined the capacity of vulnerability indices to predict RISK (threat)/NAR. Using these data, maximized threshold values were determined, misclassifications were examined, and tests were cross-validated as outlined above.
Finally, threshold values determined by the above analyses were applied to Canadian fishes of unknown conservation status, categorizing species above the thresholds as vulnerable. This analysis allowed for the examination of species’ vulnerability without population decline data or previous conservation assessment.
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3 Results 3.1 Comparative overview
A general overview of the vulnerability indices for assessed Canadian fishes is provided in Table 1. Body size of fishes ranged from 4.4 cm (Least darter, Etheostoma microperca ) to 910.0 cm (Giant manta, Manta birostris ), with a mean of 129.7 cm (n=166 fishes; SE=13.40). Trophic levels ranged from 2.0 (e.g., Western silvery minnow, Hybognathus argyritis ) to 4.5 (e.g., longfin mako, Isurus paucus ), with a mean of 3.6 (SE=0.04). Intrinsic vulnerability scores ranged from 10 (minimum value; e.g., brook silverside, Labidesthes sicculus ) to 90 (maximum value, e.g., roughtail stingray, Dasyatis centroura ) with a mean of 51.3 (SE=1.78). Resilience ranged from very low (e.g., shortnose sturgeon, Acipenser brevirostrum ) to high (e.g., speckled dace, Rhinichthys osculus ), with a median value of ‘medium’ resilience (when high=1, medium=2, low=3 and very low=4: x=2.50, SE=0.77). Overall, these data demonstrate considerable variation in the four tested vulnerability indices. All indices were correlated at the p<0.05 level (Table 2). The most highly correlated indices were SIZE and IVUL, which had a Spearman correlation value of 0.90 and significance at the p<0.000 level.
A three way ANOVA was conducted for each of the four vulnerability indices to examine relationships between index values for species and their risk status, while taking into account external factors of habitat (freshwater, diadromous and marine) and whether species were fished or not (Figure 1, Table 3). There were no significant interaction effects (i.e., between habitat and risk, fishing and risk, or a combination of habitat, fishing and risk) for any vulnerability index, although results showed fished species were considerably larger in body size, higher in trophic level and in mean intrinsic vulnerability score, and had lower resilience values than unfished species. When habitat and fishing were corrected for, RISK was a significant factor for the indices of body size
(F 1,165=4.43, p=0.037) and intrinsic vulnerability score (F 1,165=4.46, p=0.036). However, this relationship was above the p=0.05 level for the indices of trophic level (F 1,165=0.869, p=0.353) and resilience (F 1,165=2.167 , p =0.143).
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3.2 Predictive capacity of indices
Cross-validated logistic regression analyses were carried out for body size and intrinsic vulnerability score, as these vulnerability indices significantly distinguished between risk status when habitat and fishing were statistically controlled. The indices of trophic level and resilience were excluded from this analysis, as their results were not significant in the previous ANOVA test.
Body size was a significant predictor of risk in Canadian fishes overall (Table 4a), despite a relatively low pseudo r-squared value. Randomly selected subsets of species each showed significance in predicting risk as well, each with Nagelkerke R values comparable to the full model. Similarly, intrinsic vulnerability score was a significant predictor for species at risk (Table 4b), also having low Nagelkerke R values.
Thresholds for the classification of RISK and NAR were determined for the full set, as well as for each random subset of species (Table 5) for the purposes of cross-validation. For body size, the maximized probability value ( Pmax ) was 0.40 for the full model and the overall maximized threshold was 14.41 cm (Table 5a). For the cross-validating subsets, Pmax values averaged to 0.42 with an average maximized threshold of 16.07 cm.
For intrinsic vulnerability score, Pmax was 0.5 and threshold equaled 31.65, close to the average maximized threshold for the cross-validating subsets at 31.61 (Table 5b).
Predictive success : Classification success was examined through the use of five cross- validating subsets of data (Figure 2). Each of these subsets included 80% of total data, with the 20% data excluded from the model to be used to test for classification success. Using this method, true positive, true negative, false positive and false negative rates were calculated to provide an understanding of the accuracy of each model. The results were plotted in receiver-operating characteristic (ROC) space for both body size (Figure 2a) and intrinsic vulnerability score (Figure 2b). ROC plots the true positive classification rate against the false positive classification rate, to depict predictive accuracy of models and provide for comparisons between them. Models closest to true positive rate of 1 and false positive rate of 0 are those with highest predictive capacity.
For both indices, results of cross-validated models showed that as true positive rates increased, false positive rates generally increased as well. This indicated that species were being classified as RISK
10 correctly, but NAR species were being incorrectly classified at risk as well. For body size, classification success rates ranged from 56.3% to 67.6%. For intrinsic vulnerability score, this success ranged from 62.5% to 70.6%.
Analyses above have focused on the biological indices without the explicit inclusion of the external component of vulnerability. The study next moved to the inclusion of information on specific external threats being faced by species.
3.3 Predictive capacity by cause of endangerment
Threats to Canadian fishes were quantified. The two major threats are habitat loss and fishing (Table 6; see also Venter et al. (2006)). Overfishing, including targeted fishing (74.0% of species) and bycatch (49.0%), is the primary threat to marine species, whereas habitat loss, including direct alteration (81.1%) and degradation or pollution (67.6%), are the primary threats to freshwater species. The majority (93.8%) of diadromous fishes are impacted by both fishing and habitat threats.
Logistic regression indicated that vulnerability indices were successful in predicting risk from fishing-related threats, but remained unsuccessful in predicting risk from habitat loss or non-fishing threats (Table 7). Within fishing-related threats, the indices were successful in discriminating species at risk from those not at risk under overfishing threats, and bycatch threats (Table 7). However, the indices could not discriminate species at risk due to habitat-related threats of habitat loss or pollution, from those not at risk.
As results of the logistic regression for fishing-related threats were significant, a closer analysis was carried out for species facing overfishing and/or bycatch as primary threat(s). As with analyses with the full data set above, data were broken into subsets for the purposes of cross-validation, and thresholds were determined for the full set and for each of five subsets (Table 8). Maximized thresholds for the cross-validated subsets averaged to 80.78 cm for body size at Pmax =0.52, and
57.44 for intrinsic vulnerability score at Pmax =0.60. These averages were comparable to the values for the full set, which were 78.33 cm (Pmax =0.50) and 57.41 ( Pmax =0.60), respectively.
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When examining each specific threat separately, threshold values were increased (Table 9). For overfishing threat, the body size threshold at P=0.5 was 100.00, and intrinsic vulnerability index threshold was 59.86. For bycatch threat, thresholds were higher still: 149.93 cm for body size, and 67.76 intrinsic vulnerability score.
Predictive success : As in the analysis above, thresholds determined for each 80% subset of data were cross-validated against the remaining 20% of data excluded from the model. Inclusion of information on threats improved classification success rates and accuracy of models (Figure 3). Average classification success rates for determining RISK from body size was 74.8%, whereas from intrinsic vulnerability score was 75.4%. Accuracy was improved over previous models not including extrinsic threats: false positive rates were generally lower and true positive rates improved (Figure 2). Models for body size had slightly higher true positive rates averaging 82.2%, versus those for intrinsic vulnerability score, averaging 77.6%; however false positive rates were also higher for body size cross-validated models, averaging 33.2% versus those for intrinsic vulnerability score at 27.1%.
3.4 Misclassifications
A case-by-case analysis of species was employed when risk was incorrectly classified by body size and intrinsic vulnerability. Classifications were made by applying maximized threshold values, determined for each random subset of data (e.g., maximized threshold for ABCD, Table 8), to the excluded group (e.g., E) for the purposes of cross-validation. Misclassifications were noted as either false positives (not at risk species classified as at risk by the model), or false negatives (species at risk classified as not at risk.)
Some groups of species were misclassified by both the body size and intrinsic vulnerability indices (Table 10). Species with high false positive classification rates commonly included species of the orders Rajiformes and Chimaeraformes. Those with high false negative classification rates included several species of the genus Coregonus .
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3.5 Predicting vulnerability of Canada’s fishes
Given that body size and intrinsic vulnerability score had some capacity for predicting risk for species facing fishing as at threat, threshold values as determined above were applied to Canada’s currently fished species, with unknown conservation status (n=389). Using the threshold value of 78.33 cm (body size, Figure 4a), 128 species (32.9%) were classified as being at potential risk. The threshold of 57.41 intrinsic vulnerability score (Figure 4b) indicated that 129 species (33.2%) were at potential risk. In total, of fished species with currently unknown conservation status (i.e., those unassessed by IUCN or COSEWIC; or with data deficient assessments), 174 species were classified above the thresholds, indicating an increased possibility of being at risk, with 81 species being above the threshold for both vulnerability indices (46.0% ‘overlap’ between the two indices). This indicated that 20.8% of Canada’s currently fished species with unknown conservation status could be at risk. These species are listed in Appendix 2.
4 Discussion
The capacity to predict species at risk, or to identify vulnerable species that may become endangered, is a goal of conservation biology (CBD 2006, Balmford and Cowling 2006, Mace et al. 2008). This study demonstrates that life history and ecological characteristics can be used to predict risk in fishes. This prediction is successful when an index is combined with appropriate external threat factors. Body size and intrinsic vulnerability successfully predict risk to fishing-related threats—overfishing and bycatch—but cannot adequately predict the vulnerability of species that facing habitat threats.
In other areas of biology, studies of mammals and birds also suggest the important linkage of intrinsic biology and extrinsic threat (McKinney 1997, Russell et al. 1998, Cardillo et al. 2008, Isaac et al. 2009). Empirical evidence from at least these vertebrate groups suggests that conservation biologists can practice proactive forecasting of extinction risk with biological indices that are threat- appropriate.
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4.1 What makes an index successful?
Successful prediction of risk in fishes was due primarily to two characteristics of an index. First, the index measured life history and ecological traits that reflected biological sensitivity to the external threat. Second, the index was itself related to the intensity of the external threat (i.e., through its relationship with catchability). Catchability is the interaction between fish population status and human predation effort on the population, with high catchability commonly correlated with traits such as large body size (Arreguin-Sanchez 1996, Post et al. 2002). Given that the indices varied in their reflection of these two characteristics of internal biological sensitivity and external catchability, they differed in their predictive success. In this study, body size and intrinsic vulnerability score were the most successful predictors of risk in fishes.
Body size is an important indicator of vulnerability (see also Reynolds 2005a, b). This study showed that even when I controlled for fishing and habitat, larger species were more likely to be at risk. Large body size in fishes is related to characteristics which slow population growth, and therefore slow recovery under a threat (Jennings and Reynolds 2007). Larger species require more metabolic resources per individual than smaller species, limiting individual growth and population turnover rates (Hildrew et al. 2007). Body size is negatively correlated to a species’ intrinsic rate of natural increase (r max ), with populations of smaller bodied species generally having greater ability to ‘bounce back’ from high mortality events (Musick et al. 2000, Jennings and Reynolds 2007). Large species have evolved their size in the context of high adult longevity (T max ), and commonly have a late age of reproductive maturity (T m). Increased mortality from exposure to fishing is compounded, resulting in more individuals failing to breed.
Body size is explicitly connected to the intensity of the threat of fishing. In general, fisheries target larger species more often, and with disproportionately more effort, than species of smaller body size (Reynolds et al. 2002). This is likely due to higher catchability per unit effort, and higher energetic efficiency in capture and consumption (Iudicello et al. 1998); as also found in mammals and birds where large species are preferentially hunted (McKinney 1997, Cardillo et al. 2003). Body size acts as a surrogate for catchability. In addition to generally being more valuable and therefore more sought after, large species are efficiently captured by fishing gears and strategies currently in use (Arreguin-Sanchez 1996). Large-bodied species retain their value as populations decline and individuals of the species become rare (e.g., bluefin tuna, Reynolds et al. 2002; schooling species
14 such as cod, Hutchings and Myers 1994). Finally, even when not specifically targeted, larger species have a greater probability of becoming bycatch in fishing gears, such as trawls and longlines (Jennings and Reynolds 2007).
Intrinsic vulnerability score (Cheung et al. 2005) also predicted risk in this study. When the effects of fishing and habitat were controlled, those species with higher intrinsic vulnerability scores were more likely to be at risk. This index is made up of multiple variables, such as age at maturity, in addition to body size, with the expectation that multiple variables will improve predictive capacity (Cheung et al. 2005). Studies with tropical and seamount-aggregating fish have demonstrated intrinsic vulnerability index to be correlated to declines caused by fishing (e.g., Morato et al. 2006, Cheung et al. 2007). Within this study, intrinsic vulnerability successfully predicted risk of endangerment, but was unable to provide the expected improvement over the body size index. Instead, intrinsic vulnerability index had approximately similar predictive ability to body size alone, and misclassified as many species. Given that it utilizes seven variables in addition to body size, it is more cumbersome to calculate, with its calculation varying in completeness among species, as often not all variables have empirical data. Body size is the minimum input in determining an intrinsic vulnerability score (Cheung et al. 2005), and for a number of species, the index may only be body size, which is not disclosed on FishBase.
The intrinsic vulnerability index has not previously been applied to freshwater species, and the development of this index has centered on the vulnerabilities of marine fishes to fishing (e.g., Cheung et al. 2005, 2007). This study demonstrates that the intrinsic vulnerability index does not work for species facing threats unrelated to fishing. The inclusion of intrinsic vulnerability scores for some freshwater and unfished species on FishBase has the potential to be misleading, as vulnerability must be tailored to a specific threat. An intrinsic vulnerability index for freshwater fishes should reflect the fact that habitat loss, not fishing, is the primary threat. The life history and ecological traits included in the current intrinsic vulnerability fuzzy logic system may not be effective in estimating vulnerability to habitat-related threats.
Trophic level did not successfully predict risk as body size or intrinsic vulnerability. However, trophic level was included in this analysis, as species with high trophic levels may be more vulnerable than species with low trophic levels. According to the ‘fishing down the food web’ theory, higher trophic level species have been targeted and depleted over recent decades, reducing
15 mean community trophic levels in marine environments worldwide (Pauly et al. 1998). In addition to being targeted more often, like large-bodied species, those fish at high trophic levels require more energetic resources than smaller fishes; and this in turn is related to slower growth rates at individual and population levels (Jennings et al. 2001).
In data presented in this study, body size and trophic level were significantly correlated. However, the measure of trophic level is not as strong of an indicator of catchability as body size, which is specifically targeted in fishing. The utility of trophic level in assessing vulnerability may be limited to interpreting community declines and changes over time (e.g., Pauly et al. 1998), rather than the vulnerability of species one at a time or at a point in time; this capacity may be in assessment rather than in proactive prediction. The Marine Trophic Index (MTI), which measures overall mean trophic level and is applied to declines in fisheries catches over a given period of time, has been adopted by the Convention for Biological Diversity (CBD) as a metric of ocean biodiversity richness and abundance (CBD 2006, Pauly and Watson 2005) but may not be successful as a predictor of extinction risk.
Resilience had the lowest success at identifying fish species at risk. This may be due in part to limitations in the calculation of the measure, as presented on FishBase. For some species, this index is based on one variable (e.g., fecundity), for others up to four have been considered (T m, T max , fecundity, and individual growth). In this study, over 30% of species in the data set, the resilience measure was based solely on estimates of fecundity, which has been widely criticized as a measure of vulnerability (e.g., Hutchings 2001, Sadovy 2001, Dulvy et al. 2005). When resilience estimates based solely on fecundity were removed, the utility of the resilience measure was improved. The better-performing intrinsic vulnerability score also includes maximum age, age at maturity and growth as parameters, but a key difference is that only low fecundity is included in the measure (Cheung et al. 2005). Therefore, high fecundity is not included as an index to reduce vulnerability, as it is in the resilience score. Further research could be done to determine how inclusion of fecundity changes the utility of these composite scores.
Currently, a form of this resilience measure is applied to conservation assessment by the American Fisheries Society (Musick et al. 2000), and has also been adopted by sustainable seafood awareness groups such as SeaChoice in Canada (SeaChoice 2009). The results of this study indicate that fecundity alone as an indicator of vulnerability may be problematic, and as such the resilience index
16 as presented on FishBase must be interpreted with caution. It is important that this measure incorporate values beyond fecundity to ensure the accuracy of predictions, and AFS and SeaChoice assessments need also to proceed with caution in their use of fecundity-based approaches.
4.2 Why do indices work for some species and not others?
Overall, the indices were not equally successful for all species, and success differed across threat types. In particular, there was limited predictive capacity for species with a primary threat factor of habitat loss, mainly freshwater and unfished species; and higher success for marine and fished species, facing threats of overfishing and bycatch. The results of this study indicate that despite differences in habitat, the threat of fishing impacts species with high catchability, and that catchability is related to body size, but less strongly related to other variables examined in this study.
While fishing is the primary threat to marine species, freshwater and diadromous species are impacted by many additional and different threats. These include habitat loss, degradation, invasive species, or others such as hybridization (Rose 2005). While fishing can be characterized as generally species-oriented, habitat loss impacts in a less discriminating manner (Duncan and Lockwood 2001). When external threats themselves are difficult to isolate, it is correspondingly more difficult to identify those internal life history and ecological traits that create vulnerability (e.g., Reynolds et al. 2005b). In general, habitat becomes a proxy for the types of threats faced by a freshwater fish species.
In addition, the nature of the fishing threat is variable across the different habitats, as human predation pressures differ in marine and freshwater realms. Canada has both commercial and recreational fisheries; the former primarily in marine environments and the latter primarily in freshwater, with notable exceptions such as the commercial fisheries on the Great Lakes and the recreational fisheries in near-shore marine waters (Post et al. 2002). Expenditures by recreational anglers have recently begun to generate more revenue in Canada than commercial fisheries (e.g., Post et al. 2002, Cooke and Cowx 2006). The lack of sustainability in Canadian marine commercial fisheries is studied and well known to the general public (e.g., Atlantic cod collapse, Hutchings and Myers 1994; Pacific salmon endangerment, Irvine et al. 2005, Rand 2008). Due to the high rate of capture per fisher, commercial fisheries are perceived as having a greater negative impact on
17 biodiversity than recreational fishing (‘high catchability and low effort,’ Pereira and Hansen 2003). Recreational fisheries operate in a reverse model from those of commercial fisheries, with a large number of individual fishers and low rate of capture per person (‘high effort and low catchability,’ Pereira and Hansen 2003). Unlike marine fisheries, in freshwater areas fish are largely confined to lakes, while fishers move from area to area to find highest catchability (Lester et al. 2003). The impacts of freshwater recreational fishing are less understood, and in the past have been assumed more benign than commercial fisheries (Cooke and Cowx 2006). However, Post et al. (2002) and Lester et al. (2003) demonstrated that Canadian recreational fisheries have significantly altered freshwater fish communities, with many commonly fished freshwater species such as rainbow trout in British Columbia, walleye and pike in Alberta, and lake trout in Ontario having little resemblance to their pre-fished state (i.e., in population abundance as well as morphology).
In sum, the nature of the fishing effort is different between recreational and commercial fisheries, and appears to be reflected in results of this study for freshwater and marine species in Canada. The impacts of recreational fishing in freshwater systems are less well-studied than those for commercial fishing. Combined with the additional habitat-related threats faced in freshwater systems, vulnerability cannot be as easily attributed to single variables as can be done for marine species threatened by commercial fishing. In this study, this appears to have influenced the capacity for indices to predict risk in freshwater fishes.
4.3 Uncertainty and misclassification
Similar types of species were misclassified at maximized probability thresholds for both body size and intrinsic vulnerability score. Many of the misclassified species had body size or intrinsic vulnerability values close to the determined thresholds. However, many of the misclassified species also fit within a larger framework of understanding of fishing as the primary driver of vulnerability. This includes non-targeted species misclassified as at risk, and species with multiple threats, including combinations of fishing and habitat related threats.
False positives – False positives in the data set followed several trends. First, some species misclassified as positives were those that did not encounter a threat (i.e., species that were not targeted for fishing, or lived in habitats too deep for fishing). Examples of these include the
18 chimaera species and Richardson’s ray ( Bathyraja richardsoni ), which commonly reside in depths below 1000 meters; below targeted depths of most commercial fisheries (IUCN 2009). However, with increasing effort in deep sea fishing (e.g., Devine et al. 2006), these species may become at risk. Second, some false positive species were those that no longer encountered a threat. In conservation assessments for these species, threats were ‘known and reversed’ down-listing their status to ‘not at risk’ under IUCN or COSEWIC guidelines despite significant population declines. These species include the big skate ( Raja binoculata ) and the wolf-eel ( Anarrhichthys ocellatus ). Although these are currently listed as not at risk, conservation status report authors suggest that both species were ‘vulnerable’, and that if a threat was present, they would be at risk (COSEWIC 2009): management efforts are in place to allow their persistence. Third, for the sea lamprey ( Petromyzon marinus ), total length was not an appropriate method of measurement for body size (e.g., despite a maximum length of 120 centimetres, maximum weight for the species is approximately 2 kg; Froese and Pauly 2009). For some groups, such as the Petromyzontiformes, body weight may be a more appropriate measure than total length. However, as weight data were limited for species in this study (only available for 24.6% of Canadian species), it was not possible to test the difference in classification success using length, versus weight, as a metric of body size. Lastly, species were ‘true’ false positives; like the Pacific electric ray ( Torpedo californica ). This species is targeted for fishing as well as for scientific research (molecular biology: acetylcholinesterase, e.g., Sussman et al. 1991) and has comparatively ‘slow’ life history characteristics, but populations remain stable. This species has a size similar to related elasmobranch species that are at risk (e.g., barndoor skate, Dipturus laevis ). Nevertheless, one noted difference is that this ray has comparatively high intrinsic rate of increase (rmax ) compared to most other elasmobranch species (Neer and Cailliet 2001). This rmax is similar to two other elasmobranch species that are not at risk, the Atlantic sharpnose shark (Rhizoprionodon terraenovae ) and the bonnethead shark ( Sphyrna tiburo ; Neer and Cailliet 2001). This suggests that species such as the Pacific electric ray, that are ‘true’ false positives, provide an important opportunity to further understand the factors that can contribute to resilience.
False negatives – A notable group of species misclassified as not-at-risk by both body size and intrinsic vulnerability indices was that of the genus Coregonus . This included the Atlantic whitefish (Coregonus huntsmani ) and four species of ciscos: the Bering cisco ( Coregonus laurettae ), Kiyi (Coregonus kiyi ), shortnose cisco ( Coregonus reighardi) and shortjaw cisco ( Coregonus zenithicus ).
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The latter three cisco species occur in the Great Lakes basin, where commercial fishing in the early part of the 20 th century contributed much of their decline (COSEWIC 2009). These species were often caught in multi-species gillnet fisheries, that did not need to report on a species-by-species basis (COSEWIC 2003), thus concealing population declines of individual species and increasing their vulnerability. This issue is mirrored in the current case of the Acadian redfish ( Sebastes fasciatus ), another species misclassified in this model. Due to dispute as to the taxonomic status distinguishing it from the deepwater redfish, Sebastes mentella , fisheries need only report by genus in redfish catch (DFO Atlantic 2009). Despite a moratorium on the fishing of redfish in of the three major management areas on the east coast of Canada, 28 500 metric tons are the combined total allowable catch for the other two management zones (DFO Atlantic 2009), of which an unidentified portion may be the COSEWIC-listed Acadian redfish.
All misclassified Coregonus species encountered multiple threat factors. For example, in addition to overfishing, competition with non-native alewife ( Alosa pseudoharengus ) and rainbow smelt (Osmerus mordax mordax ) are attributed to recovery failure in shortjaw cisco, whereas predation by sea lamprey negatively impacted the shortnose cisco (COSEWIC 2009). The Bering cisco and Atlantic whitefish are both diadromous and migratory species; and as shown in analyses above, diadromous fishes face multiple threat factors more often than freshwater or marine species. These species have both experienced overfishing as well as habitat loss through alteration of migratory pathways (COSEWIC 2009). These misclassifications of species with multiple and compounding threats highlight a potential limitation for vulnerability indices for species with complex threat regimes, rather than the single threat of overfishing.
4.4 Data considerations
Several factors in this study could be of potential concern, including the use of conservation assessment data thought to be inherently biased, comparisons of traits within large taxonomic groupings, and the use of index values as reported on FishBase. These concerns were considered as follows.
Assessments by IUCN and COSEWIC have been taken as ‘state of the art’ for the purposes of this study, as authoritative sources which have been peer reviewed and validated in the scientific
20 literature (e.g., Venter et al. 2006, Olden et al. 2007, Mace et al. 2008). However, criticisms of using data from conservation assessments such as these arise from the inherent biases in the listing processes, including under-representing certain taxonomic groups, and disproportionately more assessments completed for large, ‘charismatic’ species that are important to humans (Possingham et al. 2002, Mace et al. 2008). The variation in observational effort and data availability are difficult to control, as most species with necessary population data for assessment are those that are commercially fished (and therefore larger), potentially biasing the lists of at risk species to larger and more vulnerable species (Dulvy et al. 2003). Although the data may be biased when compared to a fully assessed area, such as Virginia (e.g., Angermeier 1995), the data presented here represent 50 families and 104 genera, which is much of the cross section of Canadian fishes (Mosquin et al. 1995). Half of the freshwater fish species in Canada have been assessed by COSEWIC (COSEWIC 2009). This study began with a large sample size and used assessments from both IUCN and COSEWIC, including both fished and unfished species, reducing bias in species. As population- level extinctions are indicative of global status (Dulvy et al. 2003), COSEWIC’s DU-level assessments were included, but corrected the issue of inflated species assessment numbers by using only one DU per species.
Some considerations were made in order to reduce comparisons of widely divergent phylogenetic groups within fishes (e.g., Reynolds et al. 2005b). When a matched-pairs analysis was undertaken, matching identified species at risk with identified not at risk species within the same genus, it reduced the sample size to only 29 pairs, 16 of which were from the family Cyprinidae. Due to the bias created, the analysis proceeded as shown here, breaking it down by species subsets to better compare within groups of species within similar habitats and facing similar external threats.
4.5 Predicting Vulnerability in Canada’s Fish Fauna
Canadian fishes are relatively well-studied: it was found that 14.6% have been assessed for conservation status by national and international authorities compared to a global average of 11% of fishes (IUCN 2008). However, 85.4% (n=1186) remain unassessed for their conservation status by either COSEWIC or IUCN. Application of the body size index to fished species of unknown conservation status shows that approximately 1/5 Canadian species that are currently fished are above threshold levels for body size and intrinsic vulnerability scores. This analysis would classify
21 these species as vulnerable, or at higher risk of endangerment, when subjected to fishing. Body size predicts 32.9% species are at risk, and intrinsic vulnerability score predicts 33.2% species are at risk. In all, 45.2% of currently fished Canadian species without known conservation status are predicted to be vulnerable, with 46.0% (n=81) of these species predicted by both indices. These species predicted as vulnerable by both measures include such commercially important species as Pacific halibut and albacore tuna, and recreationally important species such as muskellunge and lake trout. Species on this list (Appendix 2) could be used to prioritize for further conservation assessment.
4.6 Conclusions
With nearly 90% of the world’s fishes still unassessed for their conservation status, the utility of vulnerability indices in determining potential risk in fishes is high. These indices can be especially important in understanding risk posed by the threat of overfishing.
A conundrum often faced by conservation scientists is how to determine acceptable levels of uncertainty, when the failure to detect effects can lead to irreversible damage such as extirpation or extinction (Gray 1990). Some authors propose reversing the statistical ‘burden of proof:’ that is, reversing the convention of requiring low Type I errors (false positives) to requiring low Type II errors (false negatives) to meet the precautionary principle (Dayton 1998, Field et al. 2004). However, scientists may not be the only suitable group to determine what threshold level of uncertainty is appropriate (Gray 1990). For management or other policy development, a wide range of stakeholders, including industry, independent scientists, and government should be involved in determining what levels of uncertainty are acceptable under the precautionary principle.
Indices have the potential to be used in two major ways. First, indices can be used as a tool for conservation assessment by providing a quantitative method of prioritizing species at risk. For fishes, which are the least-assessed vertebrate group (IUCN 2008), indices such as body size provide the opportunity for rapid prioritization for research and conservation effort. Additionally, countries such as Canada, with scientific advice and government management of fisheries, could use vulnerability indices to prioritize management, after appropriate thresholds of uncertainty are determined. As indices identify species that may become at risk next, thresholds could be determined for membership into COSEWIC’s ‘Special Concern’ category. If listed under the
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Canadian Species at Risk Act (SARA), Special Concern species require a management plan within five years of designation. As well, Special Concern listings raise awareness of vulnerable species to the public and relevant stakeholder groups, encouraging non-government conservation action.
Secondly, indices can be used as a component of conservation assessments themselves. As outlined above, some groups have already incorporated life history traits within fish conservation assessments, such as through the American Fisheries Society and sustainable seafood consumer awareness campaigns such as SeaChoice. However, caution must be exercised in the choice of life history traits, due to their limitations. As it has been shown, the resilience measure, used by AFS and reported on FishBase, may have limited capacity to predict risk. As well, even the indices that worked best, body size and intrinsic vulnerability score as per Cheung et al. (2005), do not provide value in determining whether species are vulnerable due to threats unrelated to fishing. Methodology, limitations and classification success rates should be explicitly included into these efforts, in order to maximize credibility and impact.
No one index value has perfect predictive capacity: for any predictive tool in conservation biology, there is a trade-off between wide applicability across taxonomic groups and fit to individual species (Cardillo et al. 2008). Vulnerability indices may not be a replacement for ongoing research and monitoring of species. However, this study has shown that vulnerability indices can be used as tools for assessment when population data are lacking, as well as identify species for which further data are necessary. The use of indices such as these may help in proactive conservation efforts for fished species in Canada and around the world.
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Tables
Table 1. Vulnerability index values for Canadian fish species at risk and not at risk.
a. Body size (total length, cm) Species group Overall Risk Not at Risk n Mean St Dev n Mean St Dev n Mean St Dev All fish 166 129.72 172.58 103 169.36 66.23 63 64.90 66.23 Freshwater 68 32.02 41.04 37 38.15 50.97 31 24.70 23.29 Diadromous 26 127.09 130.02 16 166.15 149.70 10 64.59 50.56 Marine 72 222.94 209.09 50 267.49 233.32 22 121.68 72.91 Fished 94 202.86 198.12 70 237.47 214.91 24 101.92 76.63 Unfished 72 34.22 38.33 33 24.88 21.82 39 42.12 46.95 b. Trophic level. Species group Overall Risk Not at Risk n Mean St Dev n Mean St Dev n Mean St Dev All fish 166 3.56 0.57 103 3.68 0.53 63 3.37 0.57 Freshwater 68 3.17 0.50 37 3.31 0.52 31 3.01 0.44 Diadromous 26 3.63 0.32 16 3.67 0.32 10 3.55 0.33 Marine 72 3.91 0.45 50 3.96 0.42 22 3.79 0.50 Fished 94 3.77 0.48 70 3.86 0.43 24 3.51 0.52 Unfished 72 3.29 0.56 33 3.30 0.54 39 3.28 0.59 c. Intrinsic vulnerability score. Species group Overall Risk Not at Risk n Mean St Dev n Mean St Dev n Mean St Dev All fish 166 51.26 22.94 103 56.42 22.13 63 42.83 21.87 Freshwater 68 34.94 18.65 37 37.55 19.76 31 31.81 17.03 Diadromous 26 55.35 18.48 16 59.73 17.63 10 48.33 18.50 Marine 72 65.21 17.76 50 69.33 13.97 22 55.87 21.89 Fished 94 64.58 16.20 70 67.26 15.31 24 56.76 16.50 Unfished 72 33.89 18.38 33 33.44 15.93 39 34.26 20.43 d. Resilience* Species group Overall Risk Not at Risk n Mean St Dev n Mean St Dev n Mean St Dev All fish 166 2.5 0.995 103 2.65 0.997 63 2.25 0.997 Freshwater 68 1.809 0.758 37 1.946 0.848 31 1.642 0.608 Diadromous 26 2.5 0.86 16 2.625 0.885 10 2.3 0.823 Marine 72 3.153 0.781 50 3.18 0.8 22 3.091 0.75 Fished 94 2.904 0.868 70 3.014 0.86 24 2.583 0.83 Unfished 72 1.972 0.903 33 1.879 0.82 39 2.051 0.972 * When high resilience = 1, medium resilience = 2, low resilience = 3, and very low resilience = 4.
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Table 2. Spearman correlations between vulnerability index values for assessed Canadian fish species (at risk and not at risk), n=166. SIZE = total body size, log-10 transformed; TROP = trophic level as reported on FishBase, IVUL = intrinsic vulnerability score as per Cheung et al. (2005), RESL = resilience as per Musick (1999). All correlations are significant at p<0.01.
SIZE TROP IVUL RESL SIZE 1 .661 .901 .774 TROP .661 1 .566 .556 IVUL .901 .566 1 .781 RESL .774 .556 .781 1 * Correlation is significant at the 0.01 level (2-tailed).
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Table 3. Analysis of variance (ANOVA) examining effects of external variables on capacity for vulnerability indices to predict risk. Numbers indicate F values at the specified degrees of freedom. Bold text indicate signficance at p<0.05 level.
Variable(s) df SIZE TROP IVUL RESL* Risk status 1, 165 4.43 0.87 4.46 2.17 Habitat 2, 165 23.26 14.40 11.67 19.37 Fishing 1, 165 34.00 2.53 28.93 8.44 Risk status and Habitat 2, 165 1.79 1.60 0.45 0.75 Risk status and Fishing 1, 165 0.18 0.53 0.22 0.10 Habitat and Fishing 2, 165 0.50 0.19 2.86 1.98 Risk status, Habitat, Fishing 2, 165 0.27 0.61 0.28 0.35 R squared 0.686 0.417 0.582 0.458
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Table 4. Capacity for vulnerability indices to predict Canadian fish species at risk: cross- validated logistic regression output. Groups are randomly selected subsets of data with equal proportions of species at risk (RISK) and not at risk (NAR).
a. Body size. Omnibus -2 Log Nagelkerke Group n n Test Chi- df Sig. RISK NAR Likelihood R square ABCD 83 51 13.19 1 .000 164.86 0.13 ABCE 83 51 13.69 1 .000 164.36 0.13 ABDE 83 51 15.91 1 .000 159.25 0.15 ACDE 82 50 11.14 1 .001 164.01 0.11 BCDE 82 50 8.09 1 .004 167.06 0.08 Full set 103 63 15.25 1 .000 205.14 0.12
b. Intrinsic vulnerability score Omnibus -2 Log Nagelkerke Group n n Test Chi- df Sig. RISK NAR Likelihood R square ABCD 83 51 10.33 1 .001 167.72 0.10 ABCE 83 51 12.27 1 .000 165.78 0.12 ABDE 83 51 13.62 1 .000 161.53 0.13 ACDE 82 50 11.57 1 .001 163.59 0.11 BCDE 82 50 9.06 1 .003 166.10 0.09 Full set 103 63 14.13 1 .000 206.26 0.11
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Table 5. Threshold values of body size (maximum length) and intrinsic vulnerability score in predicting Canadian fish species at risk and not at risk, as determined by cross-validated logistic analysis. Cutoff values calculated by the logistic equation, using maximized probability value ( Pmax; see text for details). B values are constants calculated by the logistic regression.
a. Body size. Threshold cutoff value Groups b0 b1 Pmax (cm) included 0.5 Pmax ABCD -1.78 1.26 0.4 25.85 18.75 ABCE -1.66 1.25 0.4 21.53 15.39 ABDE -1.74 1.29 0.5 22.29 22.29 ACDE -1.42 1.10 0.3 19.65 9.04 BCDE -1.10 0.93 0.5 14.87 14.87 Full set -1.52 1.16 0.4 20.69 14.41
b. Intrinsic vulnerability score. Groups Threshold cutoff value b0 b1 Pmax included 0.5 Pmax ABCD -0.92 0.03 0.5 33.66 33.66 ABCE -0.90 0.03 0.5 31.79 31.79 ABDE -0.95 0.03 0.5 32.48 32.48 ACDE -0.87 0.03 0.5 32.05 32.05 BCDE -0.69 0.02 0.5 28.07 28.07 Full set -0.86 0.03 0.5 31.65 31.65
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Table 6. Extrinsic threats to Canadian fishes.
Fishing Habitat Other threats Group n Targeted Bycatch Loss Degredation E.g., Invasives All fish 103 51.46% 32.35% 50.98% 36.27% 14.71% Freshwater 37 18.92% 2.70% 81.08% 67.57% 24.32% Marine 50 74.00% 48.98% 14.29% 2.04% 4.08% Diadromous 16 56.25% 50.00% 93.75% 68.75% 25.00% Note: Categories are not exclusive, and do not sum to 100%.
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Table 7. Capacity for vulnerability indices to predict Canadian fish species at risk under particular extrinsic threats.
a. Body size Omnibus -2 Log Nagelkerke Threat group n n Test Chi- df Sig. RISK(threat) NAR Likelihood R square Fishing threat 67 63 53.321 1 0.000 126.774 0.45 Habitat threat 55 63 1.107 1 0.273 161.933 0.012 Overfishing 49 63 46.14 1 0.000 107.37 0.45 Bycatch 33 63 48.63 1 0.000 74.93 0.55 Habitat loss 50 63 2.03 1 0.154 153.12 0.02 Pollution 34 63 0.17 1 0.681 125.50 0.00
b. Intrinsic vulnerability score Omnibus -2 Log Nagelkerke Threat group n n Test Chi- df Sig. RISK(threat) NAR Likelihood R square Fishing threat 67 63 40.308 1 0.000 139.788 0.36 Habitat threat 55 63 1.22 1 0.267 161.82 0.01 Overfishing 49 63 32.59 1 0.000 120.92 0.34 Bycatch 33 63 35.70 1 0.000 87.85 0.43 Habitat loss 50 63 1.91 1 0.167 153.25 0.02 Pollution 34 63 0.30 1 0.584 125.37 0.00
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Table 8. Cross-validated threshold values for species’ vulnerability to fishing related threats.
a. Body size. Groups Threshold cutoff value (cm) b0 b1 Pmax included 0.5 Pmax ABCD -5.91 3.14 0.50 76.17 76.17 ABCE -6.27 3.31 0.50 78.42 78.42 ABDE -6.19 3.24 0.50 81.54 81.54 ACDE -6.03 3.18 0.50 78.62 78.62 BCDE -5.15 2.73 0.60 76.94 89.17 Full Set -5.90 3.12 0.50 78.33 78.33
b. Intrinsic vulnerability score. Groups Threshold cutoff value b0 b1 Pmax included 0.5 Pmax ABCD -2.72 0.05 0.60 54.47 57.82 ABCE -3.64 0.07 0.60 54.09 56.93 ABDE -3.39 0.06 0.60 54.72 57.53 ACDE -3.32 0.06 0.60 54.01 57.25 BCDE -3.05 0.06 0.60 54.61 57.68 Full Set -3.21 0.06 0.60 54.40 57.41
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Table 9. Cross-validated threshold values for species’ vulnerability to overfishing and bycatch threats.
a. Body size and overfishing. b. Body size and bycatch.
Groups Threshold Groups Threshold b0 b1 b0 b1 included (p=0.5) included (p=0.5)
ABCD -5.88 3.01 90.12 ABCD -7.72 3.60 139.60 ABCE -6.99 3.47 103.64 ABCE -8.55 3.97 143.17 ABDE -6.22 3.08 104.68 ABDE -10.85 4.91 161.44 ACDE -6.63 3.26 107.59 ACDE -7.92 3.66 145.33 BCDE -5.68 2.84 99.88 BCDE -7.72 3.50 160.56 Full Set -6.248 3.118 100.00 Full Set -8.39 3.86 149.93
c. Intrinsic vulnerability score and overfishing. d. Intrinsic vulnerability score and bycatch.
Groups Threshold Groups Threshold b0 b1 b0 b1 included (p=0.5) included (p=0.5)
ABCD -2.69 0.05 59.02 ABCD -3.91 0.06 68.42 ABCE -4.10 0.07 59.49 ABCE -4.78 0.07 65.22 ABDE -3.39 0.06 59.77 ABDE -5.34 0.08 69.37 ACDE -3.58 0.06 60.08 ACDE -4.74 0.07 66.87 BCDE -3.46 0.06 59.46 BCDE -4.97 0.07 68.79 Full Set -3.41 0.06 59.86 Full Set -4.70 .069 67.76
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Table 10. Species misclassified by both body size and intrinsic vulnerability indices in cross- validated models. Thresholds as determined by logistic regression for each crossvalidated model (see Table 8). False-positive suggests risk when conservation assessment suggests safety, and false-negative suggests safety when conservation assessment suggests risk.
False negatives False positives • Arctic skate ( Amblyraja hyperborean ) • Queen triggerfish ( Balistes vetula ) • Wolf-eel ( Anarrhichthys ocellatus ) • Atlantic whitefish ( Coregonus huntsmani ) • Richardson's ray ( Bathyraja richardsoni ) • Kiyi ( Coregonus kiyi ) • Roughtail stingray ( Dasyatis centroura ) • Bering cisco ( Coregonus laurettae ) • Chain pickerel ( Esox niger ) • Shortnose cisco ( Coregonus reighardi ) • Narrownose chimaera ( Harriotta raleighana ) • Shortjaw cisco ( Coregonus zenithicus ) • Smalleyed rabbitfish ( Hydrolagus affinis ) • Lined seahorse ( Hippocampus erectus ) • Sea lamprey ( Petromyzon marinus ) • Yellowtail flounder ( Limanda ferruginea ) • Big skate ( Raja binoculata ) • Sockeye salmon ( Oncorhynchus nerka ) • Spearnose chimaera ( Rhinochimaera • Atlantic rainbow smelt ( Osmerus mordax atlantica ) mordax ) • Sea trout ( Salmo trutta trutta ) • Acadian redfish ( Sebastes fasciatus ) • Charr ( Salvelinus alpinus alpinus ) • Tench ( Tinca tinca ) • Pacific electric ray ( Torpedo californica )
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Figures
Figure 1. Conservation-assessed Canadian fish species (n=166) that are at risk or not at risk of extinction, classified by fishing and environment type.
80 Diadromous 70 Freshwater
Marine 15 60
50 8
40 1 1 30 24
Number of species 47 20 9 29 10 7 14 8 0 3 Not at Risk Risk Not at Risk Risk Unfished Fished
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Figure 2. Classification success of vulnerability indices to predict risk, using cross-validated models in receiver-operating characteristic (ROC) space. Diamonds indicate classification accuracy for each of five cross-validated models, when threshold cutoff point determined by maximized probability value (see Materials and Methods section of text for further details). True positive rate is the total number of correctly classified species at risk (RISK), and false positive rate is the total number of not at risk (NAR) species classified as RISK in the logistic model. Models show improvement as values approach a true positive rate of 1, and a false positive rate of 0. The reference line indicates point at which model is equal to a randomized assignment to RISK or NAR groups.
a. Body size (maximum total length).
1.0 E D 0.9 B 0.8 C
0.7 A 0.6
0.5
0.4 Truepositive rate 0.3
0.2
0.1
0.0 0.0 0.2 0.4 0.6 0.8 1.0 False positive rate
Classification success Reference line
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b. Intrinsic vulnerability score (as per Cheung et al. 2005).
1.00 E 0.90 D B 0.80 C
0.70 A
0.60
0.50
0.40 Truepositive rate 0.30
0.20
0.10
0.00 0.00 0.20 0.40 0.60 0.80 1.00 False positive rate
Classification success Reference line
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Figure 3. Classification success of vulnerability indices to predict under the threat of fishing in Canadian fish species, using cross-validated models in receiver-operating characteristic (ROC) space. Diamonds indicate classification accuracy for each of five cross-validated models, when threshold cutoff point determined by maximized probability value.
a. Body size.
1.0 A 0.9 B
0.8 C D, E 0.7
0.6
0.5
0.4 Truepositive rate 0.3
0.2
0.1
0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 False positive rate
Classification success Reference line
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b. Intrinsic vulnerability score.
1.0
0.9 E A
0.8 B
0.7 C D
0.6
0.5
0.4 Truepositive rate 0.3
0.2
0.1
0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 False positive rate
Classification success Reference line
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Figure 4. Frequency distribution and vulnerability of fished Canadian species of unknown risk status. Species with vulnerability index values falling above dashed lines are predicted to be vulnerable.
a. Body size. Median =59.05, interquartile range =62, n =389. Dashed line indicates threshold value of 78.33.
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b. Intrinsic vulnerability score. Median =48.15, interquartile range =27.57, n =389. Dashed line indicates threshold value of 57.41.
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Appendices
Appendix 1. Vulnerability index values for assessed Canadian species.
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Genus Species Common Name Habitat Fished SIZE TROP IVUL RESL Risk Status Threat Acipenser oxyrinchus Atlantic sturgeon DIAD Yes 403.0 3.4 85.4 VLow RISK SC Overfishing, habitat loss, pollution oxyrinchus Acipenser medirostris Green sturgeon DIAD Yes 250.0 3.5 80.1 Low RISK SC Bycatch, habitat loss, pollution Acipenser brevirostrum Shortnose sturgeon DIAD Yes 143.0 3.3 82.5 VLow RISK SC Bycatch, habitat loss, pollution Acipenser transmontanus White sturgeon DIAD Yes 610.0 3.6 87.4 VLow RISK EN Bycatch, habitat loss, pollution Acipenser fulvescens Lake sturgeon FW Yes 274.0 3.3 85.8 VLow RISK EN Overfishing, bycatch, habitat loss Anarhichas lupus Wolf-fish MAR Yes 150.0 3.4 66.7 Low RISK SC Overfishing, habitat loss Anarhichas denticulatus Northern wolffish MAR Yes 180.0 3.8 78.0 Low RISK TH Bycatch, habitat loss Anarhichas minor Spotted wolffish MAR Yes 180.0 3.6 80.6 Low RISK TH Bycatch, habitat loss Anguilla rostrata American eel DIAD Yes 152.0 3.6 67.5 Med RISK SC Overfishing, habitat loss, pollution Balistes vetula Queen triggerfish MAR Yes 60.0 3.3 36.2 Med RISK TH Overfishing Carcharhinus limbatus Blacktip shark MAR Yes 275.0 4.5 55.4 Low RISK SC Overfishing, habitat loss Carcharhinus obscurus Dusky shark MAR Yes 420.0 4.4 87.5 VLow RISK SC Overfishing, bycatch Carcharhinus signatus Night shark MAR Yes 280.0 4.5 74.1 VLow RISK TH Overfishing, bycatch Carcharhinus longimanus Oceanic whitetip MAR Yes 396.0 4.4 74.9 VLow RISK TH Overfishing, bycatch shark Galeocerdo cuvier Tiger shark MAR Yes 750.0 4.4 63.8 Low RISK SC Overfishing, bycatch Prionace glauca Blue shark MAR Yes 400.0 4.4 67.2 VLow RISK SC Overfishing, bycatch Carcharhinus plumbeus Sandbar shark MAR Yes 250.0 4.2 86.2 VLow RISK SC Overfishing Carcharias taurus Sand tiger shark MAR Yes 320.0 4.4 70.3 VLow RISK TH Overfishing Catostomus catostomus Longnose sucker FW Yes 64.0 3.1 64.4 Low RISK EN Habitat loss,pollution, other catostomus Moxostoma hubbsi Copper redhorse FW No 72.0 3.2 65.7 VLow RISK EN Habitat loss, pollution, other Erimyzon sucetta Lake chubsucker FW No 41.0 3.1 32.3 Med RISK EN Habitat loss, pollution Moxostoma carinatum River redhorse FW Yes 77.0 3.6 65.5 Low RISK SC Habitat loss, pollution Moxostoma duquesnii Black redhorse FW No 51.0 3.0 57.7 Med RISK TH Habitat loss, pollution Ictiobus cyprinellus Bigmouth buffalo FW Yes 123.0 3.1 74.7 Low RISK SC Habitat loss Minytrema melanops Spotted sucker FW Yes 50.0 3.4 56.9 Med RISK SC Habitat loss Lepomis gulosus Warmouth FW Yes 31.0 3.4 37.8 Med RISK SC Habitat loss Cetorhinus maximus Basking shark MAR Yes 900.0 3.3 86.2 VLow RISK EN Overfishing, bycatch Myoxocephalus thompsonii Deepwater sculpin FW No 23.0 3.2 40.5 Med RISK SC Pollution, other Cottus hubbsi FW No 11.4 3.3 48.5 Med RISK SC Habitat loss, pollution Cottus confusus Shorthead sculpin FW No 15.0 3.7 32.8 Med RISK TH Habitat loss Macrhybopsis storeriana Silver chub FW No 23.0 3.6 29.7 Med RISK SC Pollution Notropis bifrenatus Bridle shiner FW No 6.5 2.8 14.3 High RISK SC Pollution Clinostomus elongatus Redside dace FW No 12.0 3.2 29.6 Med RISK EN Habitat loss, pollution Hybognathus argyritis Western silvery FW No 12.0 2.0 23.2 High RISK EN Habitat loss, pollution
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minnow Notropis anogenus Pugnose shiner FW No 5.8 2.7 11.5 High RISK EN Habitat loss, pollution Notropis photogenis Silver shiner FW No 14.0 3.3 24.6 High RISK SC Habitat loss, pollution Opsopoeodus emiliae emiliae Pugnose minnow FW No 6.6 2.9 12.9 High RISK SC Habitat loss, pollution Rhinichthys umatilla Umatilla dace FW No 12.0 2.9 23.2 High RISK SC Habitat loss, pollution Notropis percobromus Carmine shiner FW No 7.6 2.8 17.3 High RISK TH Habitat loss Rhinichthys cataractae Longnose dace FW No 22.5 3.2 35.7 Med RISK EN Habitat loss Rhinichthys osculus Speckled dace FW No 11.0 2.9 21.9 High RISK EN Habitat loss Esox americanus Redfin pickerel FW Yes 40.6 3.7 38.9 Med RISK SC Habitat loss americanus Fundulus diaphanus Banded killifish FW No 13.0 3.2 17.9 High RISK SC Habitat loss, pollution diaphanus Fundulus notatus Blackstripe FW No 8.0 3.1 11.5 High RISK SC Habitat loss, pollution topminnow Gadus morhua Atlantic cod MAR Yes 200.0 4.3 67.8 Med RISK EN Overfishing Melanogrammus aeglefinus Haddock MAR Yes 112.0 3.6 63.1 Med RISK TH Overfishing Hexanchus griseus Bluntnose sixgill MAR Yes 482.0 4.4 84.0 Low RISK SC Overfishing, bycatch shark Noturus stigmosus Northern madtom FW No 13.0 3.7 23.2 High RISK EN Pollution, other Lachnolaimus maximus Hogfish MAR Yes 91.0 3.9 66.2 Low RISK TH Overfishing Carcharodon carcharias Great white shark MAR Yes 792.0 4.5 86.2 VLow RISK EN Overfishing, bycatch Isurus oxyrinchus Shortfin mako MAR Yes 400.0 4.3 63.5 VLow RISK TH Overfishing, bycatch Lamna nasus Porbeagle MAR Yes 350.0 4.4 78.8 VLow RISK EN Overfishing, bycatch Isurus paucus Longfin mako MAR Yes 417.0 4.5 76.2 VLow RISK TH Bycatch Lepisosteus oculatus Spotted gar FW Yes 150.0 4.0 68.3 Med RISK TH Habitat loss, pollution Brosme brosme Tusk MAR Yes 120.0 4.0 63.2 Low RISK TH Overfishing Lutjanus cyanopterus Cubera snapper MAR Yes 160.0 4.0 64.9 Low RISK TH Overfishing, 'other' Coryphaenoides rupestris Roundnose MAR Yes 110.0 3.5 68.5 Low RISK EN Overfishing grenadier Macrourus berglax Onion-eye grenadier MAR Yes 110.0 3.6 75.1 VLow RISK SC Bycatch Morone saxatilis Striped bass DIAD Yes 200.0 4.5 60.6 Low RISK TH Bycatch, habitat loss Manta birostris Giant manta MAR Yes 910.0 3.5 80.7 VLow RISK SC Overfishing, bycatch, habitat loss and pollution Rhinoptera bonasus Cownose ray MAR Yes 213.3 3.5 60.0 Low RISK SC Overfishing, bycatch Osmerus mordax mordax Atlantic rainbow DIAD Yes 35.6 3.4 50.0 Med RISK TH All threats smelt Ammocrypta pellucida Eastern sand darter FW No 8.4 3.6 17.7 High RISK TH Habitat loss, pollution (Etheostoma pellucidum) Percina copelandi Channel darter FW No 7.2 3.3 15.0 Med RISK TH Habitat loss Ichthyomyzon fossor Northern brook FW No 17.0 4.5 49.6 Low RISK SC Pollution lamprey
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Lampetra macrostoma Vancouver lamprey FW No 25.0 4.5 44.8 Med RISK TH 'Other' threat Ichthyomyzon castaneus Chestnut lamprey FW No 38.0 4.5 58.3 Low RISK SC Habitat loss, pollution Lampetra richardsoni Western brook FW No 15.0 2.4 34.0 Med RISK EN Habitat loss lamprey Hippoglossoides platessoides American plaice MAR Yes 82.6 3.6 66.3 Med RISK TH Overfishing Hippoglossus hippoglossus Atlantic halibut MAR Yes 470.0 4.3 88.1 VLow RISK EN Overfishing Limanda ferruginea Yellowtail flounder MAR Yes 64.0 3.0 36.5 High RISK TH Overfishing Leucoraja ocellata Winter skate MAR No 110.0 3.6 62.1 Low RISK EN Overfishing, bycatch Dipturus laevis Barndoor skate MAR Yes 152.0 4.1 77.4 Low RISK EN Bycatch Oncorhynchus kisutch Coho salmon DIAD Yes 108.0 4.2 53.0 Med RISK EN Overfishing, habitat loss, pollution Coregonus laurettae Bering cisco DIAD Yes 36.0 3.8 45.0 Med RISK SC Overfishing, habitat loss Coregonus huntsmani Atlantic whitefish DIAD No 40.0 3.5 40.5 Med RISK EN Overfishing, bycatch, habitat loss and pollution Salmo salar Atlantic salmon DIAD Yes 150.0 3.8 54.8 Med RISK EN Overfishing, bycatch, habitat loss and pollution Salvelinus fontinalis Brook trout DIAD Yes 94.8 3.7 41.3 Med RISK EN Habitat loss, pollution Salvelinus confluentus Bull trout DIAD Yes 103.0 3.7 64.7 VLow RISK TH Habitat loss Oncorhynchus tshawytscha Chinook salmon DIAD Yes 150.0 3.8 68.3 Med RISK TH All threats Oncorhynchus clarkii clarkii Cutthroat trout DIAD Yes 99.0 3.8 43.0 Med RISK TH All threats Oncorhynchus nerka Sockeye salmon DIAD Yes 84.0 3.2 31.8 Med RISK EN All threats Coregonus kiyi Kiyi FW No 35.0 3.5 26.0 Med RISK SC Overfishing, 'other' Coregonus zenithicus Shortjaw cisco FW No 40.0 3.4 40.5 Low RISK TH Overfishing, 'other' Coregonus reighardi Shortnose cisco FW No 36.0 3.4 37.4 Med RISK EN Overfishing, 'other' Thunnus obesus Bigeye tuna MAR Yes 250.0 4.5 72.4 Med RISK TH Overfishing Sebastes paucispinis Bocaccio MAR Yes 91.0 3.5 62.9 Low RISK TH Overfishing Sebastes pinniger Canary rockfish MAR Yes 80.6 3.8 60.8 Low RISK TH Overfishing Sebastes ruberrimus Yelloweye rockfish MAR Yes 104.0 4.0 73.5 VLow RISK SC Overfishing Sebastes fasciatus Acadian redfish MAR No 30.0 3.2 43.8 Low RISK EN Overfishing Sebastolobus alascanus Shortspine MAR Yes 97.8 3.7 69.5 VLow RISK EN Overfishing thornyhead Sebastolobus altivelis Longspine MAR No 39.0 3.7 60.1 Low RISK SC Overfishing thornyhead Epinephelus morio Red grouper MAR Yes 125.0 3.6 62.7 Low RISK SC Overfishing Epinephelus niveatus Snowy grouper MAR Yes 122.0 4.0 72.9 Low RISK TH Overfishing Centroscymnus coelolepis Portuguese dogfish MAR Yes 120.0 4.2 62.9 Low RISK SC Overfishing, bycatch Somniosus microcephalus Greenland shark MAR Yes 730.0 4.3 90.0 VLow RISK SC Overfishing, bycatch Sphyrna mokarran Great hammerhead MAR Yes 610.0 4.4 85.4 Low RISK EN Overfishing, bycatch Sphyrna zygaena Smooth MAR Yes 500.0 4.3 77.9 Low RISK SC Overfishing hammerhead Squalus acanthias Piked dogfish MAR Yes 160.0 3.9 87.5 VLow RISK TH Overfishing, bycatch
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Hippocampus erectus Lined seahorse MAR Yes 19.0 3.5 31.2 High RISK TH Overfishing, bycatch, habitat loss Sphoeroides pachygaster Blunthead puffer MAR Yes 46.0 4.2 44.0 Med RISK TH 'Other' threat Galeorhinus galeus Tope shark MAR Yes 193.0 4.3 66.3 VLow RISK SC Overfishing, habitat loss Mustelus canis Dusky smooth-hound MAR Yes 150.0 3.7 87.1 Low RISK SC Overfishing Anarrhichthys ocellatus Wolf-eel MAR Yes 240.0 3.5 84.6 Low NAR NAR NAR Labidesthes sicculus Brook silverside FW No 13.0 3.4 10.0 High NAR NAR NAR Rhizoprionodon terraenovae Atlantic sharpnose MAR Yes 110.0 4.5 46.3 VLow NAR NAR NAR shark Catostomus platyrhynchus Mountain sucker FW No 25.0 2.3 34.1 Med NAR NAR NAR Moxostoma erythrurum Golden redhorse FW Yes 78.0 3.0 65.4 Low NAR NAR NAR Lepomis cyanellus Green sunfish FW Yes 31.0 3.5 31.4 Med NAR NAR NAR Lepomis megalotis Longear sunfish FW No 24.0 3.6 50.0 Med NAR NAR NAR Hydrolagus affinis Smalleyed rabbitfish MAR No 130.0 4.0 67.2 Low NAR NAR NAR Hydrolagus colliei Spotted ratfish MAR No 97.0 3.7 49.6 Low NAR NAR NAR Alosa aestivalis Blueback shad DIAD Yes 47.1 3.4 56.3 Med NAR NAR NAR Sardinops sagax South American MAR Yes 46.6 2.6 41.2 Med NAR NAR NAR pilchard Triglopsis quadricornis Fourhorn sculpin DIAD Yes 60.0 3.7 48.8 Low NAR NAR NAR Cottus ricei Spoonhead sculpin FW No 13.4 3.0 31.8 Med NAR NAR NAR Acrocheilus alutaceus Chiselmouth FW Yes 30.0 2.4 44.0 Med NAR NAR NAR Campostoma anomalum Central stoneroller FW No 22.0 2.0 31.3 Med NAR NAR NAR Exoglossum maxillingua Cutlips minnow FW No 16.0 3.0 26.6 High NAR NAR NAR Hybognathus regius Eastern silvery FW No 12.0 2.0 28.1 Med NAR NAR NAR minnow Hybopsis dorsalis Bigmouth shiner FW No 8.0 2.9 15.0 High NAR NAR NAR Luxilus chrysocephalus Striped shiner FW No 24.0 3.3 24.9 Med NAR NAR NAR Lythrurus umbratilis Redfin shiner FW No 8.8 3.0 15.1 High NAR NAR NAR Nocomis biguttatus Hornyhead chub FW No 26.0 3.0 36.7 Med NAR NAR NAR Nocomis micropogon River chub FW No 33.0 3.2 39.8 Med NAR NAR NAR Notropis heterodon Blackchin shiner FW No 7.1 3.3 13.8 High NAR NAR NAR Notropis buchanani Ghost shiner FW No 6.4 2.9 12.9 High NAR NAR NAR Notropis rubellus Rosyface shiner FW No 9.0 3.1 17.1 High NAR NAR NAR Notropis texanus Weed shiner FW No 8.6 2.5 16.6 High NAR NAR NAR Pimephales notatus Bluntnose minnow FW No 11.0 2.4 21.9 Med NAR NAR NAR Rhinichthys falcatus Leopard dace FW No 12.0 2.7 23.2 High NAR NAR NAR Scardinius erythrophthalmus Rudd FW Yes 51.0 2.9 67.5 Low NAR NAR NAR Tinca tinca Tench FW Yes 82.2 3.3 63.0 Med NAR NAR NAR Squaliolus laticaudus Spined pygmy shark MAR No 22.0 4.2 11.3 Low NAR NAR NAR
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Dasyatis centroura Roughtail stingray MAR Yes 220.0 3.8 90.0 VLow NAR NAR NAR Esox niger Chain pickerel FW Yes 99.0 3.3 58.1 Med NAR NAR NAR Etmopterus gracilispinis Broadbanded MAR No 35.0 4.3 27.1 VLow NAR NAR NAR lanternshark Gasterosteus aculeatus Three-spined DIAD Yes 11.0 3.4 37.8 High NAR NAR NAR aculeatus stickleback Pungitius pungitius Ninespine DIAD Yes 9.0 3.4 27.0 Med NAR NAR NAR stickleback Proterorhinus marmoratus Tubenose goby DIAD No 11.5 3.4 15.5 High NAR NAR NAR Noturus miurus Brindled madtom FW No 13.0 3.5 23.2 High NAR NAR NAR Etheostoma blennioides Greenside darter FW No 17.0 3.4 28.6 Med NAR NAR NAR Etheostoma microperca Least darter FW No 4.4 3.5 13.5 High NAR NAR NAR Etheostoma olmstedi Tessellated darter FW No 11.0 2.9 22.9 Med NAR NAR NAR Gymnocephalus cernuus Ruffe FW Yes 25.0 3.3 57.8 High NAR NAR NAR Percina shumardi River darter FW No 7.8 3.4 15.8 High NAR NAR NAR Petromyzon marinus Sea lamprey DIAD Yes 120.0 4.4 71.8 Low NAR NAR NAR Amblyraja hyperborea Arctic skate MAR No 106.0 3.8 62.1 Low NAR NAR NAR Bathyraja richardsoni Richardson's ray MAR No 175.0 4.0 84.6 Low NAR NAR NAR Bathyraja interrupta Sandpaper skate MAR No 86.0 3.4 56.4 VLow NAR NAR NAR Malacoraja spinacidermis Roughskin skate MAR No 70.0 3.5 50.5 Low NAR NAR NAR Raja binoculata Big skate MAR Yes 244.0 3.9 86.0 Low NAR NAR NAR Raja rhina Longnose skate MAR Yes 140.0 3.6 54.8 Low NAR NAR NAR Harriotta raleighana Narrownose MAR No 120.0 3.6 61.1 Low NAR NAR NAR chimaera Rhinochimaera atlantica Spearnose chimaera MAR No 140.0 3.6 74.4 Low NAR NAR NAR Coregonus autumnalis Arctic cisco DIAD Yes 64.0 3.3 49.7 Low NAR NAR NAR Coregonus artedi Cisco DIAD Yes 57.0 3.4 43.5 Low NAR NAR NAR Salmo trutta trutta Sea trout DIAD Yes 159.3 3.8 58.2 Med NAR NAR NAR Salvelinus alpinus alpinus Charr DIAD Yes 107.0 3.5 74.8 Low NAR NAR NAR Coregonus hoyi Bloater FW No 37.0 3.4 46.0 Med NAR NAR NAR Thunnus albacares Yellowfin tuna MAR Yes 264.8 4.5 50.3 Med NAR NAR NAR Apristurus manis Ghost catshark MAR No 85.0 3.1 45.4 VLow NAR NAR NAR Scyliorhinus retifer Chain catshark MAR No 48.0 4.4 29.3 Low NAR NAR NAR Sphyrna tiburo Bonnethead MAR Yes 150.0 3.9 53.9 VLow NAR NAR NAR Lumpenopsis hypochroma Y-prickleback MAR No 7.6 3.0 22.2 High NAR NAR NAR Torpedo californica Pacific electric ray MAR No 140.0 4.5 80.8 Low NAR NAR NAR
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Appendix 2. Vulnerable Canadian species of unknown conservation status. Body size (total Intrinsic Genus Species Common name length, cm) vulnerability score Albatrossia pectoralis Giant grenadier 210.00 71.67 Alectis ciliaris African pompano 150.00 66.79 Alepocephalus bairdii Baird's smooth-head 108.90 68.06 Alopias vulpinus Thintail thresher 760.00 76.66 Alopias superciliosus Bigeye thresher 487.98 78.63 Aluterus scriptus Scrawled filefish 110.00 70.38 Amblyraja radiata Thorny skate 105.00 69.63 Anguilla anguilla European eel 133.00 63.16 Argentina silus Greater argentine 81.06 64.15 Atheresthes stomias Arrowtooth flounder 84.00 63.78 Atractoscion nobilis White weakfish 166.00 72.04 Beryx decadactylus Alfonsino 100.00 58.62 Brama brama Atlantic pomfret 100.00 70.43 Caranx hippos Crevalle jack 124.00 63.36 Carcharhinus altimus Bignose shark 300.00 74.40 Caulolatilus princeps Ocean whitefish 102.00 64.25 Centrolophus niger Blackfish 187.80 84.78 Centroscyllium fabricii Black dogfish 107.00 65.78 Conger oceanicus American conger 230.00 83.91 Coryphaenoides acrolepis Pacific grenadier 104.00 79.40 Cyprinus carpio carpio Common carp 137.56 63.68 Dalatias licha Kitefin shark 182.00 77.96 Dipturus linteus Sailray 123.00 67.20 Echinorhinus brucus Bramble shark 310.00 85.10 Erilepis zonifer Skilfish 183.00 58.22 Esox lucius Northern pike 145.22 67.65 Esox masquinongy Muskellunge 183.00 84.63 Euthynnus alletteratus Little tunny 122.00 63.07 Evoxymetopon taeniatus Channel scabbardfish 205.80 63.14 Fistularia petimba Red cornetfish 200.00 71.00 Fistularia tabacaria Cornet fish 200.00 71.00 Gadus macrocephalus Pacific cod 119.00 57.56 Gempylus serpens Snake mackerel 109.41 59.60 Gymnothorax funebris Green moray 250.00 73.67 Gymnura micrura Smooth butterfly ray 137.00 75.17 Hippoglossoides platessoides American plaice 82.60 66.29 Hippoglossus stenolepis Pacific halibut 258.00 86.03 Hyperoglyphe perciformis Barrelfish 91.00 58.37 Ictalurus punctatus Channel catfish 132.00 72.05 Ictiobus niger Black buffalo 123.00 74.25 Lamna ditropis Salmon shark 305.00 67.50 Lampris guttatus Opah 200.00 81.92 Lepidocybium flavobrunneum Escolar 233.20 84.78 Lepisosteus osseus Longnose gar 200.00 79.34 Lophius americanus American angler 120.00 76.95 Lopholatilus chamaeleonticeps Great northern tilefish 125.00 64.80 Lota lota Burbot 152.00 86.03 52
Megalops atlanticus Tarpon 250.00 76.87 Merluccius productus North Pacific hake 91.00 59.97 Mola mola Ocean sunfish 333.00 81.92 Molva molva Ling 200.00 65.25 Molva dypterygia Blue ling 155.00 75.42 Moxostoma valenciennesi Greater redhorse 80.00 68.00 Mugil curema White mullet 90.00 58.97 Nesiarchus nasutus Black gemfish 145.41 70.62 Ophiodon elongatus Lingcod 152.00 63.48 Pogonias cromis Black drum 170.00 61.73 Pollachius virens Saithe 130.00 61.19 Polyprion americanus Wreckfish 210.00 72.05 Pomatomus saltatrix Bluefish 130.00 58.65 Pylodictis olivaris Flathead catfish 155.00 74.23 Regalecus glesne King of herrings 1100.00 90.00 Reinhardtius hippoglossoides Greenland halibut 80.00 69.31 Ruvettus pretiosus Oilfish 300.00 85.40 Salvelinus malma malma Dolly varden 127.00 70.01 Salvelinus namaycush Lake trout 150.00 72.02 Sebastes miniatus Vermilion rockfish 91.00 63.22 Sebastes aleutianus Rougheye rockfish 97.00 67.62 Seriola lalandi Yellowtail amberjack 250.00 68.59 Spectrunculus grandis Pudgy cuskeel 127.00 71.72 Sphyraena argentea Pacific barracuda 156.17 68.14 Sphyraena guachancho Guachanche barracuda 200.00 79.79 Stenodus leucichthys Inconnu 150.00 74.36 Taractichthys longipinnis Bigscale pomfret 121.90 68.59 Tautoga onitis Tautog 91.00 69.59 Thunnus alalunga Albacore 152.46 72.45 Thunnus orientalis Pacific bluefin tuna 323.62 77.78 Thunnus thynnus Northern bluefin tuna 458.00 85.57 Urophycis tenuis White hake 133.00 71.27 Xiphias gladius Swordfish 488.22 63.96 Zoarces americanus Ocean pout 110.00 67.06
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