Spatial Criteria Used in IUCN Assessment Overestimate Area of Occupancy for Freshwater Taxa

Jun Cheng

A thesis submitted in conformity with the requirements for the degree of Masters of Science Ecology and Evolutionary Biology University of Toronto

Jun Cheng

Masters of Science

Ecology and Evolutionary Biology University of Toronto

2013 Abstract

Area of Occupancy (AO) is a frequently used indicator to assess and inform designation of conservation status to wildlife species by the International Union for Conservation of Nature

(IUCN). The applicability of the current grid-based AO measurement on freshwater organisms has been questioned due to the restricted dimensionality of freshwater habitats. I investigated the extent to which AO influenced conservation status for freshwater taxa at a national level in

Canada. I then used distribution data of 20 imperiled freshwater fish species of southwestern

Ontario to (1) demonstrate biases produced by grid-based AO and (2) develop a biologically relevant AO index. My results showed grid-based AOs were sensitive to spatial scale, grid cell positioning, and number of records, and were subject to inconsistent decision making. Use of the biologically relevant AO changed conservation status for four freshwater fish species and may have important implications on the subsequent conservation practices.

Acknowledgments

I would like to thank many people who have supported and helped me with the production of this Master’s thesis. First is to my supervisor, Dr. Donald Jackson, who was the person that inspired me to study aquatic ecology and conservation biology in the first place, despite my background in environmental toxicology. Don, your mentorship has always been thoughtful, motivating and well-placed, leading me through the transition and through the further research process. To my co-supervisor, Dr. Nicholas Mandrak, your depth of knowledge and enthusiasms towards fishes have constantly excited me and shaped my vision. I cannot express how grateful I am for your generous and inspiring inputs, as well as your encouraging and patient guidance. I am honored to be a graduate student of both of you. To my advisory committee members, Dr.

Ken Minns and Dr. Keith Somers, I am blessed to have your insightful comments and questions that contributed tremendously to making this thesis complete.

To the Jackson lab (Karen, Lifei, Brad, Jaewoo, Brie, Cindy, Sarah, and Georgina) and the extended members (Liset, Sarah, Danielle, Caren, Jonathon, Caroline, Henrique, Jordan and many more): thank you all for being great colleagues and friends that made grad school an enjoyable journey. I will always remember and deeply appreciate what I have learnt from you both in academically and personally.

To my family: Mom, Dad, Grandpa, Juan and Rey. My most sincere appreciation goes to

Mom and Dad. Thank you for the years of supporting my education and believing in me while I was away from home. You have given me the best experience growing up independently yet feeling supported. Dad and Grandpa, you are the initial inspirations for me to be interested in

iii science and research. Juan and Rey, you are the most awesome aunt and uncle, and the most awesome friends. Thank you for giving me a feeling of home ever since I came to Canada.

Table of Contents

Acknowledgments...... iii

List of Tables…………………………………………………………………………..…..…vi

List of Figures………………………………………………………………………...…...... viii

List of Appendices……………………………………………………………………..…..….x

Introduction...... 1

Methods...... 8

Results...... 19

Discussion...... 43

References...... 62

Appendices...... 70

List of Tables

Table 1. Summary of the five quantitative criteria used by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) for assessing conservation status of wildlife species, which is adapted from the IUCN Red List Categories and Criteria…………...... 3

Table 2. Total number of occurrence record, number of geographically distinct localities, 2km- IAO, 1km-IAO, and 0.5km-IAO calculated using grid cells of the same position and orientation, ratio of 2km-IAO to 1km-IAO, and 2km-IAO to 0.5km-IAO in percentage for 20 at-risk freshwater fish species in Ontario……………………………………..….…....25

Table 3. Mean, standard deviation, maximum, minimum values of 2km-IAO calculated using 13 layers of grid cells at varying positions for 20 at-risk freshwater fish species in Ontario. Also shown are COSEWIC reported 2km-IAO, and ratio of COSEWIC-reported values to the calculated mean IAO values in percentage…..………………………………....……..28

Table 4. Habitat classification for the 20 at-risk freshwater fish species included in this study…………………………………………………………………………………….....33

Table 5. Home range estimates based on average body length reported in Ontario (Holm et al. 2010) and the equation of Woolnough et al. (2009), and the adjusted buffer scales used for BioAO calculation for the 20 at-risk freshwater fish species included in this study……...34

Table 6. Stream width (m) predicted by relationship with Strahler stream order. Values are

anti-loge transformed and corrected with bias estimator (Sprugel 1983)…. ………..……36

Table 7. Proposed BioAO as sum of stream occupancy calculated in terms of occupied stream length x stream width, and lake/wetland occupancy calculated in terms of suitable habitat area within circular buffer, in comparison to COSEWIC reported biological AO for 20 at- risk freshwater fish species in Ontario. …………………………………………..…...... 37

Table 8. Summary of species status designated under COSEWIC and after application of BioAO, and the corresponding reasons for designation for 20 at-risk freshwater fish species in Ontario………………………………………………………..……………..….40 vi

Table 9. Summary of advantages and drawbacks of each AO measurement approach for freshwater taxa………………………………………………………………………….....50

vii

List of Figures

Figure 1. Study area……………………………………………………………………..……...10

Figure 2. Example of Index of Area of Occupancy (IAO) measurements for Black Redhorse in the Grand River, Ontario, at three spatial scales: 0.5km, 1km and 2km……………...……11

Figure 3. Example of BioAO measurement for stream occupancy of Black Redhorse in the Grand River, Ontario. The highlighted stream stretch indicates the segments considered occupied habitat. Localities distant from others were considered single locations.……….15

Figure 4. Example of BioAO calculation for lakeshore occupancy for Grass Pickerel in Long Point Bay, Lake Erie……………………………………………………………………….17

Figure 5. Number of freshwater fish (a) and mollusc (b) species listed under COSEWIC in each threat category and the number of Endangered (EN) and Threatened (TH) freshwater fish (c) and mollusc (d) species designated by criterion………………………………………..21

Figure 6. Primary and secondary threats identified responsible for species decline in freshwater fish (a) and mollusc (b) species of Canada that are classified as extinct, extirpated, endangered, threatened and special concerned…………..…………………………………22

Figure 7. Grid-based IAO calculations for 20 freshwater fish species measured at three spatial scales: 2km X 2km (circles); 1km X 1km (triangles); 0.5km X 0.5km (squares), with species arranged on the x-axis in the order of decreasing 2km-IAO size. Dashed lines indicate threshold values for IUCN criteria……………...……………………………..…..24

Figure 8. Mean values of calculated 2km-IAO (triangles) based on 13 placements of grids, COSEWIC-reported 2km-IAO (squares), proposed BioAO (circles), COSEWIC-reported biological AO (diamonds), and HR-BioAO (crosses) for 20 at-risk freshwater fish species in Ontario, with species arranged on the x-axis in the order of decreasing mean 2km-IAO size. Dashed lines indicate threshold values for IUCN criteria….…………………………31

viii

Figure 9. Linear regression (solid line) between Strahler stream order and loge-transformed stream width (m) based on 2431 DFO survey records in Ontario (open circles), P < 0.0001.Predicted stream width (see Table 4) at each Strahler order is estimated by the regression………….……………………………………………………………………..…35

Figure 10. Frequency distribution of total BioAO using adjusted buffer size…………………………………………………………………….……………………38

Figure 11. Linear regression (solid lines) between number of geographically unique occurrence sites and the resultant (a) mean 2km-IAO calculated using 13 gird layers at varying positions (all 20 at-risk freshwater fish species in Ontario, P < 0.0001); (b) Stream BioAO in stream length multiplied by stream width (17 of the 20 species, P =0.097); (c) suitable habitat area within circular buffer (17 of the 20 species, P < 0.001)…..…………………..42

Figure 12. Point distribution records of a freshwater mussel species the Wavy-rayed Lampmussel, Lampsilis fasciola, in the Thames River basin (black circle), and the grid- based IAO of 1km2(shaded grids) and 2km2 (open grids) scales considered by COSEWIC………………………………………………………………………………….48

List of Appendices

Appendix 1. COSEWIC designated conservation status and threat factors identified for freshwater fish species in Canada…...……………………………………………………..70

Appendix 2. COSEWIC designated conservation status and reported threat factors for freshwater mollusc species in Canada……………………………………………………...81

Appendix 3. Type of habitats used by freshwater species at risk in Canada. a) fishes; b) molluscs………………………………………………………………………..…………...83

Appendix 4. Proportion of the two components of BioAO: stream occupancy and lake/wetland occupancy for 20 fish species at risk in Ontario.……………...……………………………84

Appendix 5. Breakdown of stream BioAO for 17 species inhabited stream environment: area for raw length of occupied stream segment, buffer segment area and area for single locations.…………….……………………………………………………………………...85

1. Introduction

Anthropogenic activities have accelerated species extinction rates to an historically high level, leading to an increased demand for conservation actions (Butchart et al. 2010).

Prioritizing conservation efforts has become one of the most crucial steps in preserving biodiversity (Hoffmann et al. 2010; Keith et al. 2004; Mace et al. 2008; Miller et al. 2007). One way of achieving such a goal is through assessing and ranking species by the likelihood of extinction (Keith et al. 2004; Mace and Lande 1991; Mace 1994). Over the past four decades, a number of risk-ranking protocols and subsequent lists have been developed across various geographical scales, ranging from global to regional and local scales (Gärdenfors et al. 2001;

Mace 1994; Miller et al. 2007; Millsap et al. 1990; Milner-Gulland et al. 2006). These lists provide guidance for developing recovery strategies, designating protected habitat area, and informing policy makers (Lamoureux et al. 2003).

The International Union for Conservation of Nature (IUCN) Red List Categories and

Criteria is the most recognized conservation status assessment framework (Martín-López 2011).

The IUCN Red List status assignment incorporates a set of quantitative criteria to predict extinction probabilities (IUCN Standards and Petitions Working Group 2010). Predictors, such as population size, fragmentation, geographic distribution, and rate of decline, are determined and compared against a series of numerical thresholds to differentiate species into various risk levels. Such systematic procedures have been developed to facilitate objective evaluations and to standardize assessments across taxonomic groups (Mace 1994).

IUCN Criteria consist of five major rules, two of which involves spatial analyses of the species’ geographic distribution (Table 1; IUCN Standards and Petitions Working Group 2010). 1

Two spatial indices are used: extent of occurrence (EO); and, area of occupancy (AO). EO measures the total range of a species’ geographic distribution that encompasses all occurrence records, whereas AO is defined as the actual habitat area occupied by individuals of the species.

The two parameters differ in that AO recognizes that not all areas within the geographic range are suitable habitat. These spatial indices are incorporated into conservation assessments to provide insight on population size and its trends, particularly when adequate abundance data are lacking (Gaston and Fuller 2009, Mace et al. 2008; Pritt and Frimpong 2009). Geographic distribution is one of the fundamental ecological and evolutionary characteristics of a species that is both directly and indirectly linked to population health (Hengeveld and Haeck 1982;

Gaston and Lawton 1990b; Gaston et al. 2000). It is often regarded as a surrogate for population abundance in conservation risk assessments, based on the notion that a population with a greater number of individuals tends to be more widespread (Cardoso et al. 2011). Numerous studies have demonstrated this positive correlation between range size and population abundance (Bock and Ricklefs 1983; Gaston 1994; Gaston et al. 2000; Lacy and Bock 1986; Schoener 1987).

Both spatial parameters are assessed under IUCN Criterion B, which classifies a species into at- risk categories if the geographic range distribution is very limited (IUCN Standards and

Petitions Working Group 2010). AO is also considered under subcriterion D2, which qualifies species with extremely restricted population distribution as threatened to reflect the extinction risk associated with demographic stochasticity and the elevated susceptibility to single threat event (Lande 1993; IUCN Standards and Petitions Working Group 2010). These two spatial criteria are the most frequently used reasons for qualifying a species into threatened categories under IUCN Red List (Abeli et al. 2009; Gaston and Fuller 2009). Criterion B alone was identified as the measure influencing the listing of more than 40% of all at-risk species (Gaston and Fuller 2009).

Criterion Reasons for designation Indices analyzed

A Decline in total number of mature Number of mature individuals individuals

B Small distribution range and decline EO, AO, and number of locations or fluctuation

C Small and declining number of Number of mature individuals mature individuals

D Very small or restricted total Number of mature individuals, or, AO population and number of locations

E Quantitative analysis The probability of extinction

The current approaches used for calculating the spatial criteria have received numerous criticisms on their effectiveness and applicability (Abeli et al. 2009; Cardoso et al. 2011; Hartley and Kunin 2003; Keith et al. 2000; Mace et al. 2008). EO, measuring the overall range of the species, is usually estimated by a minimal convex polygon (MCP) that encloses all distributional records (IUCN Standards and Petitions Working Group 2010). Inclusion of inappropriate habitats due to discontinuities and disjunctions in the range distribution leads to biases in EO calculation (Burgman and Fox 2003). Although IUCN advices area of unsuitable environments to be excluded from EO estimates, explicit instruction on how to do so is not provided (Willis et al. 2003).

In the case of AO, a grid-based method is recommended. It is performed by overlaying uniform-sized grids on the range of a species and summing the area of the cells in which the species occurred (IUCN Standards and Petitions Working Group 2010). This approach is frequently opposed because it is extremely sensitive to the resolution of grid cells (Hengeveld and Heack 1982; Joseph and Possingham 2008; Keith et al. 2000; Whittaker et al. 2005). The scale at which AO is measured varies greatly across taxonomic groups, where cell sizes ranged from 0.1 to 1000 km2 in a review of 47 species (Gaston and Fuller 2009). Consequently, standardization to a reference scale (currently 2km by 2km grid size as recommended by IUCN) based on scale-area relationship is required (Mace et al. 2008). The ability to detect population decline through grid-AO is also questionable. Joseph and Possingham (2008) showed the AO measured at the traditionally recommended scale was insufficient to accurately detect declines in population abundance.

Freshwater ecosystems support diverse groups of species and, yet, are under the most substantial extinction crisis (Bruton 1995; Duncan and Lockwood 2001). Great proportions of freshwater fauna are imperiled, including freshwater fishes, molluscs, crayfishes, and amphibians (IUCN 2012; Jelks et al. 2008; Régnier et al. 2009; Ricciardi and Rasmussen 1999).

The projected global extinction rate is strikingly higher in these taxa compared to their terrestrial counterparts (Ricciardi and Rasmussen 1999). In North America, 61 species and subspecies of freshwater fishes and 21 species of freshwater molluscs have become extinct (Jelks et al. 2008).

The leading factors contributing to the endangerment of aquatic organisms have been identified as habitat alteration and degradation, followed by overexploitation, pollution, and introduction of alien species (Dextrase and Mandrak 2006; Jelks et al. 2008). However, freshwater fauna have received a disproportionately small amount of conservation attention (Duncan and

Lockwood 2001; Maitland 1995). Only 33% of all described freshwater fish species and 3% of

4 molluscs have been assessed under the IUCN classification regime, in comparison to 100% coverage in mammals and avian species, and 94% in amphibians (IUCN 2012).

The suitability of current spatial criteria for these freshwater species has been debated. In addition to the shortcomings described above, the measurement for AO is thought to be especially problematic for freshwater organisms. During the early development of IUCN Red

List Criteria, grid-measured AO was derived from an array of studies on abundance – distribution relationship measured by number of occupied grids (Mace 1994). However, most of these studies were conducted for land birds over broad geographic ranges (Bock 1984; Brown

1984; Ford 1990; Schoener 1987) and this relationship was rarely shown in aquatic species

(Gaston and Lawton 1990a). The fundamental problem is that the grid-adjacency measure of

AO does not account for the confined geographic characteristics of aquatic habitats (Gaston and

Lawton 1990a). Unlike those in terrestrial and marine environments, where movements are relatively free over large areas, freshwater organisms are restricted to limited space within clear boundaries and dispersal across space is directional and hierarchical within watercourse networks (Hitt and Angermeier 2008). Freshwater organisms are also often associated with linear habitat ranges, such as streams and lakeshore areas (Burgman and Fox 2003; Joseph and

Possingham 2003; Mace et al. 2008) or confined to small waterbodies. As a result, grid-based occupancy measurement may fail to capture the dimensionality of such habitats and to reflect the underlying geographic patterns in species distribution and abundance, leading to subsequent misinterpretation in extinction risk.

The Committee on the Status of Endangered Wildlife in Canada (COSEWIC) is responsible for identification of threatened wildlife species at the national level in Canada, and adopts the IUCN Red List protocol (COSEWIC 2010). The risk categories assigned by

COSEWIC follows that of IUCN, including Extinct (EX), Extirpated (ET), Endangered (EN),

Threatened (TH), Special Concern (SC), Not at Risk (NR), and Data Deficient (DD). EN and

TH are considered the at-risk groups, where critical thresholds in conservation risk criteria are evaluated. SC also represents an at-risk category, which is composed of species with known declines and/or existing threats, but do not meet thresholds for designation in higher risk categories. COSEWIC conservation assessments provide the basis for listing under the federal

Species at Risk Act (SARA), and for development and implementation of species recovery strategies and recovery action plans. COSEWIC separates species into designatable units (DUs), where appropriate, and assesses each DU separately. For simplicity, heretofore all species, subspecies, designatable units, and populations are referred to as species.

Like the IUCN framework, COSEWIC incorporates an occupancy parameter, the Index of Area of Occupancy (IAO), measured following the grid concept. IAO is required to be calculated at a scale of a 2km-grid (i.e. 2km by 2km). An alternative scale of 1km-grid size is recommended in cases where biological relevance can be argued (COSEWIC 2010). However, given that most streams in Canada average less than 100m in width, IAO values measured at these resolutions are likely to include more terrestrial habitat than actual occupied aquatic habitats, grossly overestimating AO for most freshwater taxa. In this study, we used COSEWIC assessments for freshwater organisms in Canada as a case study to: 1) understand the relative importance of spatial criteria for status designation; 2) further explore the potential biases and shortfalls associated with the grid IAO approach; and, 3) develop a biologically relevant measure of AO that reflects the risk of extinction and can be universally applied with relative ease. The first objective was achieved by compiling all existing status assessments available for freshwater species, including fishes and molluscs. The latter two objectives involved measuring

IAO and a proposed biological AO for 20 imperiled freshwater fish species native to southwestern Ontario.

2. Methods

COSEWIC Assessments for Freshwater Species

I compiled all available COSEWIC status assessment reports on Canadian freshwater species, totaling 131 fishes and 22 molluscs (Appendix 1, Appendix 2). Assessment reports were obtained from the SARA Registry (www.sararegistry.gc.ca), as of January 2012. For each species evaluated, the following data were gathered: 1) current conservation status; 2) criteria for designation; 3) reported measures and trends (decline and/or fluctuation) in spatial indices, including EO, AO, number of locations and habitat quality, and method used to calculate indices when applicable; 4) threat factors suspected to have led to observed or projected population decline; and, 5) preferred habitat. Species from all risk categories were included in this compilation. A species could be classified into an at-risk category by meeting the threshold for one or more criteria. The reason(s) for designation was derived from the technical summary section of the assessment report or inferred from the report when not explicitly stated. Threat factors were classified into eight categories, adapted from IUCN (2012): habitat loss and degradation; alien species invasion; over-harvesting; pollution; natural disaster; change in native species dynamic; persecution; and, other human disturbances. Threats were further identified as either primary or secondary causes of endangerment for each given species, similar to the approach taken by Dextrase and Mandrak (2006). A primary threat was defined as a major factor known to cause risk of extinction, while secondary threat referred to factors having a minor role or projected effect, or of unknown significance.

Evaluating IAO Calculations

I used distribution data for 20 imperiled freshwater fish species in Ontario to reproduce

IAO calculations, with the goal of investigating issues surrounding the grid adjacency approach.

Occurrence data of these species were obtained from the Department of Fisheries and Oceans

(DFO) (Mandrak, unpublished data). The distribution data were originally compiled for conducting COSEWIC assessments, which included both historical and recent records collected from all available sources, ranging from community surveys and targeted sampling conducted by various organizations including DFO and Ontario Ministry of Natural Resources (OMNR), independent surveys conducted for research purposes, and museum records from Canadian

Museum of Nature and Royal Ontario Museum (Doolittle et al. 2007). The area covered by the distribution localities was restricted to southern Ontario (Figure 1). This area is known to have undergone urbanization, dam construction, water extraction, pollution, and invasion by several alien species (Dextrase and Mandrak 2006) that have decreased the ecosystem health and imperiled the local freshwater fauna.

IAO measurements were performed using a Geographic Information System (GIS), following the IUCN, and hence COSEWIC, guidelines. The distribution data were georeferenced latitude-longitude point records and were projected in ArcGIS v10. Vector layers of uniform grid cells were created using E.T. GeoWizard and overlaid onto species distribution maps. Using the Spatial Analyst extension, cells were then spatially joined to species occurrence records, which permitted objective tallying of occupied cells. The E.T. GeoWizard tool pack was advantageous in this calculation because it created grid cells as vector features, with the ability to customize grid sizes and location of cell boundaries.

Figure 1. Study area.

Three major issues associated with the grid-adjacency method were examined: 1) scale- dependence; 2) influence of position and orientation; and, 3) reproducibility. To address the scale-dependency issue, I calculated IAO at three spatial scales: 2km, 1km, and 0.5km. These grid sizes reflected the currently recommended scale, the frequently utilized alternative scale for freshwater organisms, and a conservative scale matching the average width of streams, respectively (see Figure 2 for an example). For each species, 2km-IAO was compared to IAO values resulted from the finer resolutions. IAO values at all three resolutions were compared against the critical thresholds. The second issue, grid location and orientation, was approached by producing grid layers of the same resolution with shifted border positions. This was achieved by specifying coordinates of the grid layers’ range in the E.T. GeoWizard. Thirteen 2km-grid

10 layers were generated in this fashion, eleven of which were north-south oriented, whereas, the remaining two were tilted at an angle, which was sometimes used in COSEWIC assessments

(Mandrak, unpublished data). For each species, mean value, maximum, minimum, and standard deviation of the resultant IAO values were recorded for comparison against the critical thresholds. Lastly, I evaluated the reproducibility of the grid-based approach by identifying discrepancies between the calculated IAOs and the COSEWIC-reported values. The average values of all 2km-IAO calculated for each species were compared to that reported by

COSEWIC.

Figure 2. Example of Index of Area of Occupancy (IAO) measurements for Black Redhorse in the Grand River, Ontario, at three spatial scales: 0.5km, 1km and 2km.

Biological AO

As an alternative to IAO, I propose a biologically relevant measure of Area of

Occupancy for aquatic species living in confined habitats, heretofore referred to as the BioAO. I categorized species occurrences into four major groups of freshwater habitats: stream, lakeshore, wetland, and open lake. Separate approaches were taken for occurrences in streams, and in lakes (lakeshore and open lake) and wetlands. Such separation of habitat type recognized the fundamental differences in geographical shapes of the available habitat and the differences in constraints on fish movements. Total BioAO for a specific species was the sum of the two components.

Biological Area of Occupancy in Streams

BioAO for stream occupancy was calculated as the product of occupied stream segment length (m) and estimated stream width (m). Occupied stream segment length was measured as distances between species distribution points through a stream network following a least-cost routing application (ArcGIS, Network Analyst). Distribution records of the 20 fish species were projected onto a stream network layer of the Great Lakes drainage basin. This stream network was created by converting line features of stream segments developed by Aquatic Landscape

Inventory Software (ALIS) into an interconnecting network. ALIS is a stream segmentation application that classifies stream segments based on a set of variables including hydrography, surficial geology, connectivity, flow barriers, and thermal regime (Valley Segment Committee

2001). The stream network covered in our study area was developed with hydrographic maps at a fine scale (1:10000), which was crucial for recognizing small headwater and accurate stream

12 order classifications (Hughs et al. 2011). Each stream segment in ALIS was described by watershed code, segment length, and Strahler and Shreve stream orders.

As point distribution data typically underestimate actual distribution, a buffered approach was used to determine occupied stream segment length. I introduced a term, buffer distance, to quantify the length of the stream segment I assumed an individual could travel through. This method utilized species-specific spatial scales by considering variations in mobility among species. Assuming each occurrence location represented at least one individual, the capacity by which the individual might move between these interconnected locations through the streams was considered to determine the size of its occupancy. If two occurrence sites of a species were separated by a distance greater than twice that of the buffer length, the stream segment in between was considered unoccupied and excluded from being part of BioAO.

The buffer distance was also introduced to each end of an occupied segment to account for potential habitat usage. Localities with single occurrence record were considered as ‘single locations’, for which the size of occupied stream reach was assigned as one buffer (see Figure 3 for an example).

Buffer distance used in BioAO analysis was determined based on the predicted home ranges. This approach is consistent with the notion that area of occupancy is essentially the sum of home range areas of all individuals within a population (Gaston 1991). Home range is defined as the area over which an individual travels or lives in and has been found to correlate positively with body size in freshwater fish species (Minns 1995, Woolnough et al. 2009). I used the following allometric relationship constructed for lotic species (Woolnough et al. 2009) to predict home range as a function of body size:

log home range (m) = -0.678 + 0.73 x log body size (cm3).

Body size was calculated following method described by Woolnough et al. (2009) using average fish body length (mm) reported in Ontario by Holm et al. (2010). Back-transformation of log- transformed home range sizes were corrected for bias (Sprugel 1983). A set of biological AO was calculated using the predicted home range as buffer length, heretofore, referred as HR-

BioAO.

The final proposed BioAO was an adjusted buffer length based on home range sizes in the following manner: the buffer range for a given species was arbitrarily set to 1km or 2km if home range was less than 60m, or between 60m and 2000m, respectively. For species with home range estimated greater than 2000m, home range size was used directly as the buffer distance. The 1- and 2-km river lengths corresponded to those scales currently being applied under COSEWIC and IUCN assessments for grid measurements, which provide rationale for comparing against the current thresholds. These scales are also similar to those referred to as local segment scales used in other studies, such as linear stream buffer of 5km used by Fagan et al. (2005) and 3.25km used by Groce et al. (2012).

I included a layer of hydrological structures (OMNR 2012) to reflect barriers to fish movements and dispersal in stream habitats. Point locations of dams were used as barriers within the stream network when conducting the least-cost calculation to indicate terminals of occupied stream segments.

Figure 3. Example of BioAO measurement for stream occupancy of Black Redhorse in the

Grand River, Ontario. The highlighted stream stretch indicates the segments considered occupied habitat. Localities distant from others were considered single locations.

Estimates of stream width for the occupied segments were the other component of

BioAO measure. A dataset of stream width information for over 100 Ontario streams was attained (N.E. Mandrak, DFO, unpublished data). It contained information collected from a total of 2431 sampling events, with records of waterbody names, date of the sampling, and latitude- longitude coordinates. Two approaches were taken to estimate occupied stream segment width under different scenarios. If an occupied stream was sampled in the width dataset, then the average width for that particular stream was used. If the information was not immediately available, I predicted stream width as a function of Strahler stream order.

A linear regression analysis between Strahler order and the stream width was constructed

(ln-transformed; Hugh et al. 2011) for streams in Ontario. I projected the sampling records as point vectors onto ALIS stream network and performed spatial join to associate width data with the Strahler order of the stream reach in which it was measured. I manually relocated points that were, based on the waterbody name, snapped onto incorrect waterbodies during the spatial join.

Stream widths estimated through this relationship were corrected using the anti-logarithmic bias corrector (Sprugel 1983).

Lake and Wetland Occupancy

I combined all lakeshore, wetland, and open-lake occurrence sites and measured occupancy using a circular buffer approach (see Figure 4 for an example). For each occurrence locality, a circular buffer was created with a species-specific range. I determined the radius of the buffer size based on the species movement size calculated the same way for the stream occupancy. HR-BioAO was calculated based on a radius of the predicted home range size of the species, and BioAO was calculated based on a radius size of the adjusted buffer length. To eliminate terrestrial area from the AO measure, the buffered zone was overlaid onto the map of major and minor waterbodies from Canada Water Maps (2012). Only areas of the waterbodies intersected within circular buffer were considered as occupied habitat.

Figure 4. Example of BioAO calculation for lakeshore occupancy for Grass Pickerel in Long

Point Bay, Lake Erie.

Applicability of BioAO

I compared the risk of extinction and conservation status according to the COSEWIC guidelines based on IAO and my proposed BioAO. Note that COSEWIC analyses for six of the

20 species (Bridle Shiner, Channel Darter, Grass Pickerel, Northern Brook Lamprey, River

Redhorse, Silver Lamprey) included DUs covering areas for which I did not have point distribution data. For this reason, for the other fourteen species with complete distribution data, the calculated IAO values, BioAO values were compared to the COSEWIC-reported AOs and their conservation status were reassessed.

In addition, I investigated the extent to which the different methods were influenced by the number of distribution localities available. Linear regression of the 2km-IAO, stream

BioAO, and lake BioAO against the number of geographically distinct occurrence localities were performed.

3. Results

Summary of COSEWIC Assessments for Freshwater Species

The review of COSEWIC reports for freshwater fishes and molluscs revealed the high degree of endangerment of these aquatic species (Figure 5a, b). Among 131 freshwater fish species assessed under COSEWIC, 12 species were extinct or extirpated, and 79 existing species were classified in the imperiled categories (EN, TH, or SC). Out of the 22 freshwater mollusc species assessed under COSEWIC, one species was considered extirpated, and 20 species were classified in the imperiled categories (EN, TH, or SC), among which 16 species were

Endangered.

Compilation of these assessments confirmed that the two spatial criteria (Criterion B and

D2) were used most frequently as the factor for designation of at-risk aquatic organisms (Figure

5c, d). Both Criterion B and D2 were used alone, or in conjunction with other criteria, to determine the imperilment of species; 54% of the endangered and 48% of the threatened fish species were assessed using Criterion B, and 57% of the threatened fish species were assessed using Criterion D2. Criterion B was also used for assessing 88% of the endangered, and the only threatened, freshwater molluscs.

AO measures reported in the COSEWIC assessments for the freshwater taxa varied in the methods used. Ninety of the 131 freshwater fishes and 20 of the 22 freshwater molluscs were reported with at least one measure of AO. The most common AO measure reported in

COSEWIC assessments was IAO measured at 2km-scale (38 fish species; 10 mollusc species), whereas 1km-IAO (25 fish species; 5 mollusc species), biological AO in the form of estimated

19 stream area (21 fish species; 13 mollusc species), and preferred habitat area (25 fish species; 1 molluscs species) were also presented. Ten freshwater fish species had IAO reported to be greater than their EO.

A number of threat factors were identified to have contributed to the imperilment of

Canadian freshwater fauna (Figure 6). This review only considered species in categories at

Special Concern or higher because information was not always available for species that were

Not at Risk or Data Deficient. Multiple threats were listed for a majority of the species (71 of the 88 freshwater fishes, 20 of the 21 freshwater molluscs). Habitat loss and degradation were the most significant threat to imperilment (45 fish species; 17 mollusc species; Figure 6).

Introduced species was cited as the primary threat for 19 freshwater fish and 13 mollusc species;

Pollution was identified as the primary threat for 15 freshwater fish and 9 mollusc species; and freshwater fishes also suffered from over-exploitation.

Extinct A

Extirpated B Endangered C Threatened EN D

Special Concern TH Designating Criteria Designating Not at Risk E

Data Defficient 0 5 10 15 20 Number of Species Number of Species (a) (c)

Extinct A

Extirpated B Endangered C Threatened EN D

Special Concern TH Designating Criteria Designating E Not at Risk Data Defficient 0 5 10 15 20 Number of Species Number of Species (b) (d)

Figure 5. Number of freshwater fish (a) and mollusc (b) species listed under COSEWIC in each threat category, and the number of

Endangered (EN) and Threatened (TH) freshwater fish (c) and mollusc (d) species designated by criterion.

Habitat loss/degradation Alien invasive species Harvest Pollution Natural disaster Primary Change in native species Secondary Persecution Human disturbances

0 10 20 30 40 50 Number of Species (a)

Habitat loss/degradation Alien invasive species Harvest Pollution Natural disaster Primary Change in native species Secondary Persecution Human disturbances

0 5 10 15 20 Number of Species (b)

Figure 6. Primary and secondary threats identified responsible for species decline in freshwater fish (a) and mollusc (b) species of Canada that are classified as Extinct, Extirpated, Endangered, Threatened and Special Concern.

IAO calculations

The IAO measures increased with increasing grid scale (Figure 7, Table 2). All 2km-

IAO values were below the TH threshold under subcriterion B2 (2000km2) and above that for

D2 (20km2), and ranged from 64km2 (Warmouth) to 1196km2 (Redside Dace). Based on the

2km-IAO, 16 species met the criteria for Endangered under B2 (500km2). Based on the 1km-

IAO, all species met the threshold for being Endangered (B2), ranging from 25km2 to 409km2.

Based on the 0.5km-IAO, almost half of the species (8 out of 20) qualified for being geographically restricted (8.5km2 to 124.75km2). On average, 2km-IAO values were 325% of the corresponding 1km-IAO values, and 1120% of the corresponding 0.5km-IAO values.

Variation in grid placement yielded insignificant amounts of variation in 2km-IAO measures for majority of the species (Table 3; Figure 8). Maximum difference among measures was greatest for Redside Dace (80km2) and Grass Pickerel (60km2). Blackstripe Topminnow,

Eastern Sand Darter, and Silver Chub also showed variation in IAO results to a certain degree

(greater than 40 km2 maximum differences). For Silver Shiner, maximum and minimum values were 476km2 and 508km2, respectively, which were below or above the critical threshold for

EN.

Relatively poor reproducibility of the grid-approach was demonstrated by the discrepancies between the mean calculated 2km-IAO measures and COSEWIC-reported values

(Table 3). The mean 2km-IAO values that I calculated strongly deviated from the IAOs reported by COSEWIC for five species: Blackstripe Topminnow, Bridle Shiner, Lake

Chubsucker, Pugnose Minnow, and Silver Shiner, where the COSEWIC reported IAO values exceeded my calculations by more than 50%.

10000

TH: B2

1000

EN: B2

100 IAO (km2)IAO

TH: D2

10 2X2 IAO (km2)

1X1 IAO (km2) 1 0.5X0.5 IAO (km2)

Table 2. Total number of occurrence record, number of geographically distinct localities, 2km-IAO, 1km-IAO, and 0.5km-IAO calculated using grid cells of the same position and orientation, ratio of 2km-IAO to 1km-IAO, and 2km-IAO to 0.5km-IAO in percentage for 20 at-risk freshwater fish species in Ontario.

Common name Scientific name COSEWIC Total Number of 2km-IAO 1km-IAO 0.5km- 2km-IAO to 2km-IAO status number of geographically (km2) (km2) IAO 1km-IAO to 0.5km- occurrences unique (km2) (%) IAO localities (%)

Black Redhorse Moxostoma TH 338 208 464 133 36.25 348.87% 1280.00% duquesnei

Blackstripe Fundulus SC 287 166 328 101 30.5 324.75% 1075.41% Topminnow notatus

Bridle Shiner1 Notropis SC 132 111 292 83 22.5 351.81% 1297.78% bifrenatus

Channel Percina TH 156 105 228 62 17.25 367.74% 1321.74% Darter1 copelandi

Eastern Sand Ammocrypta TH 945 576 544 172 53.5 316.28% 1016.82% Darter pellucida

Grass Pickerel1 Esox SC 533 460 768 245 71.5 313.47% 1074.13% americanus vermiculatus

Lake Erimyzon EN 247 200 240 80 25.25 300.00% 950.50% Chubsucker sucetta

Lake Sturgeon Acipenser TH 71 60 180 51 13.25 352.94% 1358.49% (Great Lakes - fulvescens Upper St. Lawrence populations)

Northern Brook Ichthyomyzon SC 54 42 112 32 8.5 350.00% 1317.65% Lamprey (Great fossor Lakes - Upper St. Lawrence populations)1

Northern Noturus EN 127 103 124 43 14.25 288.37% 870.18% Madtom stigmosus

Pugnose Opsopoeodus TH 72 53 132 37 9.5 356.76% 1389.47% Minnow emiliae

Pugnose Shiner Notropis EN 302 261 352 118 37 298.31% 951.35% anogenus

Redside Dace Clinostomus EN 1314 972 1196 409 124.75 292.42% 958.72% elongatus

River Moxostoma SC 66 46 124 32 8.75 387.50% 1417.14% Redhorse1 carinatum

Silver Chub Macrhybopsis EN 759 359 816 259 72.75 315.06% 1121.65% storeriana

Silver Lamprey Ichthyomyzon SC 122 62 192 51 13.25 376.47% 1449.06% (Great Lakes - unicuspis Upper St. Lawrence populations)1

Silver Shiner Notropis TH 534 271 484 150 42.5 322.67% 1138.82% photogenis

Spotted Gar Lepisosteus TH 525 362 120 53 23.5 226.42% 510.64% oculatus

Spotted Sucker Minytrema SC 173 116 272 79 21 344.30% 1295.24% melanops

Warmouth Lepomis SC 796 413 64 25 10.5 256.00% 609.52% gulosus

1 COSEWIC species assessments included distribution records outside of Ontario which were not available to be incorporated in this analysis. 2 COSEWIC reported 2km-IAO included historical data. 3 IAO in 1km-scale, calculated with single grid layer or reported by COSEWIC. * 2km-IAO value for Ontario occurrence only (Mandrak, unpublished data).

Table 3. Mean, standard deviation, maximum, minimum values of 2km-IAO calculated using 13 layers of grid cells at varying positions for 20 at-risk freshwater fish species in Ontario. Also shown are COSEWIC reported 2km-IAO, and ratio of COSEWIC- reported values to the calculated mean IAO values in percentage.

Common name COSEWIC Mean 2km- Standard Max 2km-AO Min 2km-IAO COSEWIC COSEWIC- status IAO deviation in (km2) (km2) 2km-IAO reported 2km- (km2) 2km-IAO (km2) IAO to mean (km2) 2km-IAO (%)

Black Redhorse TH 456 10.07 468 440

Blackstripe Topminnow SC 306 12.92 328 288 516 169%

Bridle Shiner1 SC 279 9.54 300 264 6242* 224%

Channel Darter1 TH 230 7.41 244 220

Eastern Sand Darter TH 540 11.66 560 520 556 103% (1723) (3043) (177%3)

Grass Pickerel1 SC 750 18.38 780 720

Lake Chubsucker EN 243 5.26 252 236 400 165% (803) (2433) (304%3)

Lake Sturgeon TH 186 4.77 192 180 (Great Lakes - Upper St. Lawrence populations)

Northern Brook SC 121 6.41 132 112 Lamprey (Great Lakes - Upper St. Lawrence populations)1

Northern Madtom EN 127 7 136 116 180 141%

Pugnose Minnow TH 126 6.84 136 112 2752 219%

Pugnose Shiner EN 348 11.78 368 332 308 89%

Redside Dace EN 1207 24.39 1256 1176 (4413) (108%3) (4093)

River Redhorse1 SC 122 4.18 128 116

Silver Chub EN 824 12.33 848 804 8362

Silver Lamprey SC 193 3.79 200 188 (Great Lakes - Upper St. Lawrence populations)1

Silver Shiner TH 490 8.57 508 476 896 183%

Spotted Gar TH 122 6.03 136 112

Spotted Sucker SC 278 3.87 284 272

Warmouth SC 62 5.55 76 56

1 COSEWIC species assessments included distribution records outside of Ontario which were not available to be incorporated in this analysis. 2 COSEWIC reported 2km-IAO included historical data. 3 IAO in 1km-scale calculated with single grid layer or reported by COSEWIC. * 2km-IAO value for Ontario occurrence only (Mandrak, unpublished data).

TH: B2 1000

EN: B2

100

TH: D2 10

Area of Occupancy (km2) Occupancy of Area Mean 2km-IAO 1 COSEWIC reported 2km-IAO

BioAO 0 COSEWIC reported biological AO HR-BioAO

Proposed Biological AO calculations

Seventeen of the 20 species had stream occupancies, of which Black Redhorse and

Blackstripe Topminnow were strictly stream species (Table 4), and 18 species occurred in lakes and wetland habitats, of which Spotted Gar and Warmouth were restricted to wetlands. Linear individual home range calculations based on body length ranged from 29.3m to 36798.4m

(Table 5). Four cyprinid species and two percid species had linear home range estimates of less than 60m and, therefore, were assigned with a minimum buffer distance of 1km. Four large- bodied species had home ranges predicted to be greater than 2km, and the remaining species had adjusted buffer distance set to 2km.

The total biological AO calculated as the sum of occupancies in two habitat types yielded AO measures of small values (Table 8 and Appendix 4). All BioAO calculated with the adjusted buffer length were below the TH threshold under subcriterion B2 (2000km2). All species except for Silver Chub (1643km2), had BioAO below the EN threshold under subcriterion B2 (500km2). Three species that met the TH threshold under subcriterion D2

(20km2) were Blackstripe Topminnow (3.28km2), Northern Brook Lamprey (18.01km2), and

Pugnose Minnow (12.31km2). Another three species also demonstrated relatively limited occupancy: Black Redhorse (25.68km2), Redside Dace (30.06km2), and Silver Shiner

(27.92km2). Frequency distribution showed the majority of the assessed species (13 out of 20) had BioAO sizes between 20km2 and 200km2 (Figure 10).When home range predicted from the allometric relationship was used directly as buffer distance, all HR-BioAO were smaller than the

EN threshold under subcriterion B2, eleven of which fell below the TH threshold of subcriterion

D2.

Table 4. Habitat classification for the 20 at-risk freshwater fish species included in this study.

Habitat Type Common Name Stream Nearshore lake Wetland/marsh Open lake Black Redhorse X

Blackstripe Topminnow X

Bridle Shiner X X

Channel Darter X X

Eastern Sand Darter X X X

Grass Pickerel X X X

Lake Chubsucker X X X

Lake Sturgeon X X X

Northern Brook Lamprey X X X

Northern Madtom X X

Pugnose Minnow X X

Pugnose Shiner X X X

Redside Dace X

River Redhorse X X

Silver Chub X

Silver Lamprey X X X

Silver Shiner X

Spotted Gar X

Spotted Sucker X X X

Warmouth X

Average body Raw home Home range Adjusted buffer Common name length in range predicted after bias distance (m) Ontario (mm) (m) correction (m)

Black Redhorse 400 679.99 3507.63 3507.63

Blackstripe Topminnow 50 7.16 36.92 1000

Bridle Shiner 50 7.16 36.92 1000

Channel Darter 45 5.68 29.31 1000

Eastern Sand Darter 60 10.67 55.04 1000

Grass Pickerel 175 111.24 573.79 2000

Lake Chubsucker 200 149.02 768.70 2000

Lake Sturgeon 1170 7133.81 36798.43 36798.43 Northern Brook 150 79.37 409.39 2000 Lamprey Northern Madtom 80 20.03 103.34 2000

Pugnose Minnow 50 7.16 36.92 1000

Pugnose Shiner 50 7.16 36.92 1000

Redside Dace 75 17.39 89.72 2000

River Redhorse 450 880.10 4539.81 4539.81

Silver Chub 120 48.69 251.14 2000

Silver Lamprey 255 253.70 1308.66 2000

Silver Shiner 100 32.66 168.46 2000

Spotted Gar 510 1157.64 5971.47 5971.47

Spotted Sucker 255 253.70 1308.66 2000

Warmouth 155 85.27 439.87 2000

Stream-width records were associated with streams, with Strahler stream order ranging from 1 to 9 on the 1:10 000 scale ALIS map. Regression analysis showed that Strahler order was a significant predictor for stream width (r2 = 0.62, P < 0.0001) (Figure 9). The predicted width estimates ranged from 3.66m for 1st order streams to 340.36m for 9th order streams (Table 6).

7 y = 0.5667x + 0.2897 r² = 0.6227 6

transformed Width (m) Width transformed -

2 loge

0 0 1 2 3 4 5 6 7 8 9 -1 Strahler Order

Figure 9. Linear regression (solid line) between Strahler stream order and loge-transformed stream width (m) based on 2431 DFO survey records in Ontario (open circles), P < 0.0001. Predicted stream width (see Table 6) at each Strahler order is estimated by the regression.

Table 6. Stream width (m) predicted by relationship with Strahler stream order. Values are anti- loge transformed and corrected with bias estimator (Sprugel 1983).

Predicted Width Strahler order (m)

1 3.66 2 6.44

3 11.36 4 20.02

5 35.28 6 62.17 7 109.58 8 193.12 9 340.37

COSEWIC Lake/ COSEWIC Stream Total reported Common Name Wetland Status BioAO BioAO biological BioAO AO Black Redhorse TH 25.68 0.00 25.68 18.5a Blackstripe Topminnow SC 3.28 0.00 3.28

Bridle Shiner1 SC 22.52 46.33 68.85

Channel Darter1 TH 16.89 45.70 62.59

Eastern Sand Darter TH 9.87 59.23 69.11 21a Grass Pickerel1 SC 37.88 232.79 270.67 Lake Chubsucker EN 1.74 159.21 160.95 200b Lake Sturgeon TH 208.81 130.63 339.44

Northern Brook SC 5.92 12.10 18.02 26a Lamprey1 Northern Madtom EN 44.97 33.20 78.17

Pugnose Minnow TH 6.14 6.17 12.31

Pugnose Shiner EN 18.57 98.37 116.94

Redside Dace EN 30.36 0.00 30.36 4a River Redhorse1 SC 25.54 29.50 55.03 178.5a Silver Chub EN 0.00 1643.20 1643.20

Silver Lamprey1 SC 32.12 168.39 200.51 36962b Silver Shiner TH 23.60 4.32 27.92 19.3a Spotted Gar TH 0.00 117.51 117.51 51.57b Spotted Sucker SC 44.62 92.39 137.01 1090a Warmouth SC 0.00 55.08 55.08 52.99b 1 COSEWIC species assessments included distribution records outside of Ontario and were not available to be incorporated in this analysis. aCOSEWIC-reported biological AO calculated as length of stream between uppermost and lowermost sites X average width of stream. bCOSEWIC-reported biological AO based on available habitat area.

14 D2: TH 13 B2: EN B2: TH

Frequency 6

4 3 3

2 1 0 0 20 200 500 2000 More BioAO (km2)

Figure 10.Frequency distribution of total BioAO using adjusted buffer size.

Application of the BioAO measures to the COSEWIC criteria generated new conservation assessments for four species (Table 8). Eastern Sand Darter and Silver Shiner, which were classified by COSEWIC as TH using Criterion B, now, qualify for EN. The species classified as SC, Blackstripe Topminnow, qualify for TH under subcriterion D2. Black

Redhorse assessed as TH, now, only qualify for SC because the BioAO exceeding TH threshold of subcriterion D2. In addition, the EN status of Northern Madtom due to its small EO was further supported by the BioAO. On the other hand, Silver Chub, which was assessed as EN by both criteria A and B, remained EN status due to criterion A, whereas its BioAO exceeded the

EN threshold under subcriterion B2.

Linear regression analyses showed the lack of independence between AO measures and the number of occurrence sites (Figure 11). The mean 2km-IAO measures increased with increasing number of occurrence sites with r2 of 0.64 (P < 0.0001). The wetland/lake BioAO calculated using the circular buffer also increased proportionally with number of occurrence sites in lakes and wetlands for each species (r2 = 0.46, P < 0.001). Linear BioAO for stream occupancies, however, did not correlate with number of stream localities (r2 = 0.00054, P =

0.922).

Table 8. Summary of species status designated under COSEWIC and after application of BioAO, and the corresponding reasons for designation for 20 at-risk freshwater fish species in Ontario.

COSEWIC BioAO-based Common Name Reason(s) for COSEWIC Designation Reason(s) for New Designation Status Status TH: D2 (small AO and highly AO meet B2: EN, b; but not a or c; AO Black Redhorse TH SC* fragmented habitat) does not meet D2

Blackstripe Topminnow SC N/A TH* D2

BioAO calculation did not cover Bridle Shiner1 SC N/A N/A entire DU to generate new status Original COSEWIC data met EN: BioAO calculation did not cover Channel Darter1 TH B2abc, reason for down-listing not N/A entire DU to generate new status stated B2ab (I, iii, iv, v); same if COSEWIC Eastern Sand Darter TH TH: B2ab(i, iii, iv, v) EN* used 1km-IAO or reported biological AO BioAO calculation did not cover Grass Pickerel1 SC TH: B2ab(ii - v) but rescue effect N/A entire DU to generate new status

Lake Chubsucker EN EN: B2ab(ii-iv) EN EN: B2ab(ii-iv)

Lake Sturgeon TH TH: A2abcd TH AO meet B2: EN, b; but no a, c

BioAO calculation did not cover Northern Brook Lamprey1 SC N/A N/A entire DU to generate new status

Northern Madtom EN EN: B1ab (iii) EN B1ab iii+2ab iii

Pugnose Minnow TH TH: B1ab(i, ii, iii) + 2ab (I, ii, iii) TH D2

Pugnose Shiner EN EN: B2ab(iii-v) EN EN: B2ab(iii-v)

Redside Dace EN EN: B2ab(i-v) EN EN: B2ab(i-v)

BioAO calculation did not cover River Redhorse1 SC N/A N/A entire DU to generate new status EN: A2ace - AO no longer meets EN: Silver Chub EN EN: A2bce + B2ab (v) EN B2 BioAO calculation did not cover Silver Lamprey1 SC N/A SC entire DU to generate new status B2ab iii - Same if COSEWIC used Silver Shiner TH TH: B1+2ab(iii) EN* reported biological AO TH: D2 (# locations and threats to AO meet B2: EN, b, but not a or c; AO Spotted Gar TH TH habitat) does not meet D2 Does not meet any spatial criteria. Spotted Sucker SC N/A SC May meet other criteria AO meets TH: D2, but rescue effect is AO meets TH: D2, but rescue effect is Warmouth SC SC likely likely 1 COSEWIC species assessments included distribution records outside of Ontario and were not available to be incorporated in this analysis. *Species status changed after applying BioAO

1800 1400 250 y = 1.0149x + 97.96 1600

1200 r² = 0.64 y = 2.1985x - 0.7047 200 1400 r² = 0.46 1000 1200

800 150 1000 IAO (km2) IAO - 800 600 100 600 400 y = -0.0045x + 28.65

r² = 0.00054 400 Mean 2km Mean STream BioAO (km2) BioAO STream 50 200 200 0 0 (km2) BioAO Lake/Wetland 0 0 500 1000 0 500 1000 0 200 400 Number of Sites in Lakes and Number of Occurrence Site Number of Occurrence Site a) b) c) Wetlands

Figure 11. Linear regression (solid lines) between number of geographically unique occurrence sites and the resultant (a) mean 2km- IAO calculated using 13 gird layers at varying positions (all 20 at-risk freshwater fish species in Ontario, P < 0.0001); (b) Stream BioAO in stream length multiplied by stream width (17 of the 20 species, P = 0.922); (c) suitable habitat area within circular buffer (17 of the 20 species, P < 0.001).

4. Discussion

Area of Occupancy was the most frequently utilized criterion for listing freshwater fishes and molluscs under imperiled categories; however, the measurement of AO was not consistent across assessment reports. By reproducing AO measures with actual distribution records for 20 imperiled freshwater fish species, my results showed that the currently recommended grid-based

AO approach was dependent on the spatial scale, relative position of grids to the distribution records, and number of occurrence localities. I also found inconsistencies between estimated

IAO values and those reported by COSEWIC. Finally, I propose a more biologically relevant approach to measure AO. The BioAO were smaller than IAO values, all below the threatened threshold under B2. Incorporation of BioAO changed the conservation status for four species, suggesting up-listing of three species and down-listing of one species.

The review of COSEWIC assessment reports on the freshwater taxa showed that AO was the most used criterion for designation of conservation status. The frequent use of this spatial criterion for species conservation status ranking was also seen in previous studies. In a conservation risk assessment for freshwater fishes in France, size and fluctuations of AO were revealed to be important deciding factors of final status designation (Keith and Marion 2002).

Giam et al. (2011) reported extinction risk of freshwater fish fauna at the national level in

Singapore to be correlated strongly with local geographic range. AO also served as an important indicator in other taxonomic groups exhibiting small ranges, such as butterflies (Lewis and

Senior 2010), arthropods and spiders (Cardoso et al. 2011), peripheral isolated plants (Abeli et al. 2009), and herbarium species (Willis et al. 2003). In my study, IAO measures for the 20

COSEWIC designated at-risk species increased with decreasing grid resolution as expected. I 43

did not include more variants in cell sizes because the relationship between grain resolution and resultant AO values has been illustrated in previous studies (Callaghan 2008; Hartley and Kunin

2003; Kunin 1998; Willis et al. 2003). Rather, I aimed to understand how the different spatial scales affected species ranking. Even at the coarser scales, (2km- and 1km-grids), all species occupancy metrics met the TH threshold. If the grid method and the corresponding threshold values were biologically appropriate, I would anticipate that some species (especially those assessed as Special Concern) to have IAO greater than the minimum thresholds of the B2 criterion. The use of IAO is not informative given the current set of thresholds, leaving listing by criterion B2 entirely based on other signs of population imperilment, such as severe fragmentation, continuous decline and/or fluctuations in population abundance, range size indices, and habitat quality.

Choosing an adequate spatial resolution for grid-based AO measurements suitable for freshwater organisms is challenging. The narrow dimensionality of freshwater habitats justifies the use of finer scales for aquatic species compared to their terrestrial counterparts. Other studies have discussed the inclusion of large proportion of terrestrial area as inappropriate habitat for aquatic species when coarse grid scales were used (Mace et al. 2008; Zaragozi et al.

2012), leading to over-estimation of AO. This phenomenon was confirmed by our initial compilation of COSEWIC reports, which revealed 10 species with IAO values exceeding their total range as measured by EO.

Alternatively, COSEWIC (2010) allows IAO measured at 1km2 or a biological AO for freshwater organisms. However, utilization of this finer scale is not universal, nor do explicit guidelines exist for when it is appropriate. I found a number of COSEWIC assessments for aquatic species included AO measured with alternative methods, but were not used for final

designation of species status (Table 8). For example, the 1km-IAO reported for Eastern Sand

Darter was 304km2, below the minimum threshold for EN under criterion B2 (500km2) and met the indication of severely fragmentation and declining population, which would qualify the species as Endangered. Yet, the designation by COSEWIC was based on the reported 2km-IAO of 556km2 (COSEWIC 2009). Similar mismatches in status ranking were also found for Silver

Shiner, where the reported biological AO would have warranted a higher threat category.

IAO calculated with high grid resolution was also found to be misleading. For freshwater species occupying stream habitats, I observed that as grid size decreases, the grid cells incorporated less stream structures between distribution points, and subsequently the occupied sites became disconnected through the stream system (Figure 1). Finer spatial scales are also less robust to incomplete sampling (Kunin 1998; Kunin et al 2000). The IUCN states the scale at which AO is measured needs to be coarser than the census data (IUCN Standards and

Petitions Working Group 2010) to prevent underestimation of occupied area and subsequent over-listing of threatened species (Mace et al. 2008, Willis et al. 2003). As a result, extensive distribution data are, therefore, required; however, accurate and comprehensive distribution data are rarely available for freshwater taxa.

The grid-IAO approach was shown to be dependent on the number of distribution points used in the calculation. Species with higher numbers of distribution points available tended to result in larger IAO measures, which might lead to incorrect designation of the subsequent conservation status based on distribution data alone. Species of particular conservation concerns typically received greater amount of conservation attention, which resulted in greater sampling efforts and hence documented occurrence. An example from my case study is the species

Redside Dace, an endangered cyprinid species found only in small tributaries in Ontario, where

heavy urban development has led to loss of several populations (COSEWIC 2007). Its habitat area has undergone extensive surveys in effort of protection and restoration of the species, resulting in an exceptionally high number of distribution points available for this species (972 localities). This resulted in a 2km-IAO of 1207 km2, in contrast to COSEWIC-reported biological AO of 4km2 and BioAO of 30.36. This 2km-IAO measure exceeded the EN threshold under criterion B, contradicting the evident imperilment of this species.

The IAO calculation was also found to be sensitive to grid placement, which might have influenced the assessment of conservation status. For example, differential grid positioning for

Silver Shiner, whose IAO was at the margin of EN threshold, could lead to a different conclusion in its status ranking. Currently, there is no explicit guideline under COSEWIC or

IUCN for how to position grid cells. Without proper spatial reference, such variation can also result in irreproducibility and misinterpretation of changes in occupied area and pose difficulties when comparing across species or to future assessments (Rivers et al. 2011; Willis et al. 2003).

Therefore, I recommend the implementation of explicit approaches for placing the grid cells used in IAO calculations in order to facilitate comparisons across species and time, or alternatively, the estimation based on the use of several (e.g. 10) random starting points in order to assess the sensitivity of the results and, hence the assessment, to the starting points for grid placement.

Significant discrepancies between the COSEWIC-reported IAO and those calculated using my procedure were observed. For the four species (Blackstripe Topminnow, Bridle

Shiner, Lake Chubsucker and Silver Shiner) with the greatest disagreement, COSEWIC-IAO values were twice as high on average and resulted in lower status ranking. Such inconsistency might have arisen, in part, due to involvement of subjective expert opinion during COSEWIC

IAO analyses. When performing the IAO calculation for riverine species, COSEWIC assessors sometimes count the unoccupied grid cells between distribution points to include habitat thought to be suitable (Figure 12; T. Morris, Department of Fisheries and Oceans, Burlington, Ontario; personal communication 2010). Although this approach attempts to account for the more ecologically important habitat, it brings in subjectivity into the standard calculation and potentially resulted in overestimation of occupancy.

he main rationale for using grid-based AO approach is to ensure easy, quantitative measurement of occupancy in order to avoid involvement of subjective opinion (IUCN

Standards and Petitions Working Group 2010). However, the inherent shortfalls of the grid- based AO approach have led to the opposite. Lukey and Crawford (2009) revealed mismatches between conservation status predicted based on objective application of the criteria and the actual COSEWIC designated status, suggesting influence by expert opinion. Lukey et al. (2011) showed that discrepancies in status designation were more likely to occur when there was high degree of uncertainty in the risk-indicating variables. Regan et al. (2005) reported similar inconsistency in status designation among assessors that adopted the IUCN Red List protocol, and suggested that subjective opinion was more likely to be involved when parameter value hovered around the critical thresholds.

Figure 12. Point distribution records of a freshwater mussel species the Wavy-rayed Lampmussel, Lampsilis fasciola, in the Thames River basin (black circle), and the grid-based IAO of 1km2(shaded grids) and 2km2 (open grids) scales considered by COSEWIC (from COSEWIC).

Towards a Biological Approach

One of my main objectives was to develop a quantitative occupancy index that is suitable for aquatic environments and can be used consistently with explicit guidelines for designating freshwater species status. The proposed approach is the first one to incorporate life history-informed spatial scale and habitat connectivity into simple GIS-based calculations for

area of occupancy. I developed separate methods for stream occupancy and lake/wetland occupancy procedures to account for differences in fish movements and limitations posed by the dimensionality of these habitats, for which I argue provide better approximations to the biologically defensible areas of occupied habitat for freshwater species. In general, my BioAO measurements generated smaller values than IAO, but were in better agreement with biological

AO when reported in a COSEWIC report. The BioAO approaches are discussed in comparison to other alternative occupancy indices from previous studies, including the grid-IAO approach, biological AO based on occupied stream length between outermost distribution points, biological AO based on available area of preferred habitat, and AO resulting from distribution modeling (Table 9). Here, I argue that my proposed BioAO measure presented advantages over the other approaches in calculating AO for the purpose of conservation status ranking, because it accounts for species-specific scale, uses distribution of all localities in the calculation, is robust against number of available records, grid placement, and can be performed with relative ease in respect to both sampling and computation.

Table 9. Summary of advantages and drawbacks of each AO measurement approach for freshwater taxa.

Attribute Method Buffered Circular Total Available occupied buffer 2km X 2km occupied preferred Habitat segment X intersected Grid-based length X habitat modeling width of with aquatic IAO width of area habitat habitat habitat wetland, wetland, stream, stream, nearshor nearshore Habitat type nearshore all nearshore e and all and open lake lake open lake lake

Species-specific Yes Yes No No Yes Yes

Depend on number of No Yes Yes No - - records Account for all Yes Yes Yes No - - localities

Sensitive to No No Yes No - - grid placement Demand extensive No No No No Yes Yes sampling Ease to Yes Yes Yes Yes Yes No conduct

Habitat-Specific Occupancy Methods

Occupancy for stream species measured as the product of linear distance through the ecosystem and the width of the habitat more accurately captured actual area of occupancy as defined by COSEWIC compared to the two-dimensional grid method. The resultant BioAO accounted strictly for the area within the aquatic environment. This approach was recommended by the freshwater division of IUCN (Fagan et al. 2005) and applied in a number of COSEWIC assessments. A similar approach was taken by Fagan et al. (2005) to evaluate population 50

persistence of native fish species in the lower Colorado River. This procedure is also more suitable for species found in other types of linear habitats such as those living in intertidal, riparian, and coastal areas. For example, Callaghan (2008) studied the scale-area relationship using linear scales measured as river length for a moss species that inhabits river banks.

Distributions in the littoral zones of lake habitats can also be considered linear habitat.

Specifically, nearshore occupancy can be determined by breadth of the littoral zone inferred from bathymetry layers and the distance along the shoreline where the species occurred. In this study, I did not separate littoral distribution from the rest of the lake occupancy measures because preliminary attempts revealed that identification of these distributions was complicated by record accuracy and involved considerable numbers of subjective judgments.

My proposed buffered approach to linear occupancy accounted for the habitat heterogeneity in streams by excluding unoccupied stream reaches between buffered distribution points. The resultant AO metric was not as dependent on sample number and accounted for all distribution records (Table 9). In contrast, when reporting biological AO for riverine organism,

COSEWIC defines the occupied stream length as the entire reach between uppermost and lowermost distribution sites along a stream. Although this approach may account for imperfect distribution data, it is sensitive to the locations of the outermost occurrence sites and is insensitive at recognizing isolated subpopulations within one waterbody. For instance, the distribution of Black Redhorse includes a 39km unoccupied stretch between the river mouth and the nearest upstream distribution site. I suggest that considering this segment as part of AO is inappropriate because it not only exceeds the longest migration distance reported for the species

(15km; COSEWIC 2005a) and home range (3508m), but is fragmented by the presence of

Dunnville Dam.

Human barriers are important threats that alter distributional patterns of freshwater fishes, leading to elevated risk of extinction (Clavero et al. 2004). Fragmentation restricts access to upstream spawning areas and overwintering grounds in many migratory species that have large home ranges, such as members of Acipenseridae, Catostomidae, and Petromyzontidae

(Jager et al. 2008; Reid et al. 2008a; Woolnough et al. 2009). Absence or decreased abundance of fish species in upstream habitats of fragmented streams has been documented in these species

(Gido et al. 2010; Quinn and Kwak 2003; Reid et al. 2006). Dams also increase the rate of local extinction by disrupting metapopulation structure (Angermeier 1995) as reported for Cyprinidae

(Frassen 2012) and Percidae (Haponski et al. 2007). Many of the major streams in our study area were heavily fragmented by dams and impoundments, contributing to population decline.

Of the 17 species associated with streams in our analysis, dams were identified as the major threat factor for five of them and secondary threat for two species (Appendix 1). Although permeability of the dams may be improved with the construction of fish passways (Reid et al.

2008a; Reid et al. 2008b), the degree to which it maintains in-stream connectivity is debatable

(Bunt et al. 2000). Therefore, positions of dams were incorporated in our BioAO calculation where fragmented stream segments were excluded as part of AO. Consideration of barriers should also be made when dealing with other stream taxa. Barriers have been shown to pose significant threats to unionid mussels because these species depend upon fish hosts for dispersal during larval stage (Schwalb et al. 2011; Vaughn 2012).

Differential stream width estimations could explain differences observed between my proposed BioAO and those used by COSEWIC. Stream width used in COSEWIC assessments were typically rough approximations, whereas, my method was based on direct observations or predicted by Strahler order. The allometric relationship between stream width and Strahler order was fairly robust because it covered a continuous gradient of waterbody sizes and Strahler

orders (Hughes et al. 2011). The regression was not perfect, however, and tended to underestimate sizes in large rivers, such as the St Lawrence River, Detroit River, and St Clair

River, which all average about 1000m in width. In these cases, average stream width was used based on field survey data.

I adopted a circular buffer method for occupancies in lakes and wetlands to replace the use of grid cells. Unlike stream species, fish movements in lakes and wetlands are generally non-directional (Moilanen et al. 2008). Circular buffers better estimated the potential area in which an individual may occur, compared to imaginary grid boundaries (Gaston 1991) and this approach avoided the issue of sensitivity to grid placement (Rivers et al. 2010). The additional step of removing terrestrial area from the circular buffer accounted for these species being confined by clear boundaries of the aquatic habitat, allowing more biologically accurate occupancy measure. This approach required maps of the aquatic habitats, such as lakeshore contours and flooded areas in wetlands, which are often available for major Canadian waterbodies (Canada Water Maps 2012). It was performed with relative ease using GIS-based functions and can be applied to other species when suitable habitat boundaries are well defined.

COSEWIC sometimes reports AO in terms of available area of preferred habitat for species occurring in wetlands and lakes. An endangered species, Spotted Gar, was reported to occupy 51.57km2 in Ontario, measured as the vegetated area of the only three wetlands where it occurred. The AO of species endemic to a single lake, such as the Misty Lake Threespine

Stickleback species pair, was measured as the area of the lake. For species living in large lakes, estimation of suitable habitat is often based on total area at preferred depth, which provides a useful estimation of potential occupancy if depth preference is well defined. However, a species may be absent at sites of suitable habitats because distribution can be affected by factors other

than habitat availability, such as dispersal limitation (Joy and Death 2004), biological interactions with prey, predator, and competitor species (Guisan and Zimmerman 2000; Jackson et al. 2001), and local extirpation caused by extreme events (Joy and Death 2004).

Consequently, AO reported in this manner may not have the resolution to mirror changes in population sizes, and subsequently provides limited insights to the species conservation conditions. An example is the Upper Great Lakes Kiyi, a subspecies of declining population size that lives in the deep, cold waters of the Great Lakes. Biological AO reported by

COSEWIC was determined by the area of lake of greater than 100m depth, generating an AO of

67, 755km2 (COSEWIC 2005b). This measure of AO was entirely based on bathymetric maps of the lakes, and therefore lacked the ability to reflect the decreasing abundance observed in the species.

Another commonly adopted practice for determining size of AO is through distribution and occupancy modeling. Species distribution models (SDM) link environmental variables with species occurrence data statistically to characterize suitable aquatic habitat. The modeling approach generates highly species-specific analyses and aids in mechanistic understanding for population decline (Lecis and Norris 2004). Many studies predicted local species occupancies for freshwater taxa based on SDM, including stream fishes (Anderson et al. 2012; Hopkins and

Burr 2009), freshwater molluscs (Vaughn 2012; Wilson et al. 2011), and aquatic amphibians

(Lecis and Norris 2004). However, conducting SDM analysis for all species to be assessed for conservation status is unrealistic because building SDMs require comprehensive surveys of the species and habitat to understand species-habitat association (Anderson et al. 2012). Further, similar to the area of suitable habitat approach, SDM-based AO is likely to be less sensitive at reflecting population decline because it represents preferred habitat area instead of actual occupancy. For these reasons, most existing SDM research is focused on species that were

reported imperiled or known to be rare, in attempt to designate protected areas (Carvalho et al.

2010; Moilanen et al. 2008), rather than of being used for determination of conservation status designation.

Gaston (1991) described the area of occupancy as “… the sum of the areas of the home ranges (or equivalent) of all individuals, reduced to account for zones where these overlap one another”. Based on this notion, I used home range size to determine the biologically appropriate scale for calculating occupancy measures. Assuming each distribution record represented an individual, buffered area covered within the home range distance was regarded as the occupied area for that individual. This method provided rationale for species-specific consideration of scales. Previous studies showed it was at the spatial scale similar to the home range size of a species where AO is proportionate to abundance (Hartley and Kunin 2003; Joseph and

Possingham 2008). Meyer and Thuiller (2006) concluded the home range scale to be the most appropriate in predicting suitable habitat through a meta-analysis of distribution modeling studies. Using home range to determine stream and lake/wetland occupancies can also be useful for explicitly identifying number of locations, as seen in studies on freshwater fishes using buffer stream distance (Fagan et al. 2005) and on herbaria using circular buffers (Rivers et al.

2011). Using estimates of home range in AO calculation is already utilized in status designations for bird species, in terms of number of breeding pairs multiplied by their average home range (COSEWIC 2010).

Body size and home range have been shown to be strongly related in freshwater fish species (Minns 1995; Woolnough et al. 2009). The allometric relationship used in my analysis was particularly applicable because it was composed exclusively on home range reports for stream fish species (Woolnough et al. 2009). It also predicted home range in linear units, which

simplified the procedure in GIS-based calculations. However, drawbacks are associated with the use of allometric relationship from literature data compilations. First, the large variations in methods of data collection and methods of home range measure among the original studies compiled for constructing the allometric relationship might prevent accurate estimation of home range. Also, extrapolation was also required because some species sizes exceeded those used in the data compilations. Further, the reported allometric relationship did not account for different body shape in fish species, although correction for body shape did not seem to make significant differences in the original analysis (D.A. Woolnough, Central Michigan University, Mount

Pleasant, Michigan; personal communication 2012).

Arbitrary rules to increase buffer size instead of using home range directly in BioAO calculation were necessary to prevent underestimation of AO due to uncertainties in the distribution data, which are inherently inevitable for imperiled species as a result of typically low detectability. Currently, COSEWIC suggests use of ‘the best available’ data, which includes all presence-only records from various sources, in order to overcome such detection difficulties

(COSEWIC 2010). These data are often the result of multiple surveys undertaken for different purposes and using a variety of sampling techniques. As a result, the point records can never represent the distribution of all individuals in the population, which is the assumption of the HR-

BioAO calculation. Further, some home ranges predicted from the allometric relationship were extremely small, in the range of 29m to 60m, especially in small-bodied cyprinid and percid species. It is unlikely that these distribution data are based on surveys conducted at such a fine scale. Since AO should always be measured at a spatial scale equivalent or greater than the scale of sampling (IUCN Standards and Petition Working Group 2010), allometric home range was inappropriate for these species.

However, it is challenging to confirm the suitability of these arbitrary rules as to how well the resulting segments represented the actual AO, without absence data or information on species detectability for these at-risk species. In the future, I suggest conducting targeted field surveys for species under assessment designed to sample repeatedly at each site to allow the computation of probability of detection. It will also be useful to include absence sites on species distribution maps when information is available. Detectability and presence-absence data together can be utilized to determine buffer length that predicts occupied stream segments by using absent records with sufficient sampling effort to further refine the extents of the buffers.

Nonetheless, body size-home range relationships provided explicit basis for modeling species distributions at spatial scales that are biologically meaningful. Such rationale for choosing biologically relevant spatial scales may extend to conservation risk assessments of various taxa, given the large amount of body size-home range relationship data available (Hendriks et al.

2009).

Other estimators could be considered to buffer distribution points, such as area per individual (API) and minimum area for population viability (MAPV). Unlike home range which considers movement capability, API is determined by habitat resource availability (Minns et al.

1996) and tends to be smaller than home range in freshwater fish species, because of high degree of habitat overlap (Hendriks et al. 2009; Velez-Espino et al. 2008). Using API as indicator of occupancy scale is potentially more precise based on the COSEWIC definition of

AO. However, utilization of API as buffer for each distribution point requires occurrence records that more closely reflect abundance, which are typically not available for at-risk species.

MAPV can be calculated as the product of API and minimal viable population (MVP) or the product of inverse of density and MVP (Velez-Espino et al. 2008). Using this metric as buffer length would tend to result in overestimation of AO because it assumes each distribution point

to represent a viable population, which is erroneous especially in imperiled species. This method would generate very large buffer distance because the aim of this measure is to determine critical habitat area necessary for species recovery (Velez-Espino et al. 2008).

Re-evaluation of Losses – Application of BioAO

Using the proposed biological AO measurements, conservation status was reassessed under the COSEWIC framework, using the current set of thresholds. Only Eastern Sand Darter and Silver Shiner were reassessed as endangered under subcriterion B2, meeting the AO threshold as well as satisfying the additional conditions a-c (Table 8). Although BioAO for another three species (Black Redhorse, Lake Sturgeon, and Spotted Gar) also fell below the critical threshold for being endangered, there was not enough evidence for population fragmentation, decline or fluctuation in population size, range or habitat quality to support up- listing based on subcriterion B2 (Table 8). Consideration of these indicators of population decline complementary to the spatial indices is required under criterion B because distribution ranges do not correlate to risk of extinction as tightly as population abundance measures (Mace et al. 2008). Up-listing of status ranking into the threatened category occurred when the BioAO met the threshold of being extremely restricted (subcriterion D2), as in the case of Blackstripe

Topminnow, which had limited distributions in the Lake Erie drainage, yet were classified as

Special Concern by COSEWIC based on the IAO measures. On the other hand, BioAO for the threatened species, Black Redhorse, exceeded the threshold for D2, suggesting downgrading in status rank to Special Concern. The increase in BioAO compared to the COSEWIC assessment could be the result of a number of reasons. First, the COSEWIC biological AO did not account for locations with single records, which were included in our calculations. Second, the

COSEWIC report estimated 50m width for the major streams where Black Redhorse occurred, different from what we used (77m for Grand River and 40m for Thames River). Third, the buffer zone added to the ends of occupied segments could have raised the length of streams deemed occupied (Appendix 5).

The challenge for adopting the BioAO measure for freshwater taxa lies in the set of standardized threshold values that defines risk categories. Freshwater species with smaller body sizes often have lower spatial requirements due to higher density of individuals in preferred habitat (Hendriks et al. 2009) and most freshwater fishes, by nature, are more geographically restricted than their terrestrial counterparts (Olden et al. 2010), resulting in generally smaller

BioAO. Consequently, although standard thresholds provide the opportunity to compare across taxa, they may become insufficient to differentiate the freshwater fish species into different conservation categories (Keith et al. 2000) and may lead to over- or under-listing depending on taxon (Mace et al. 2008). As observed in my study, the calculated HR-BioAO all fell below the

EN threshold under criterion B, among which 11 species would qualify as geographically restricted under criterion D. Although this measure of AO represented the most conservative case where the underlying assumption that the distribution data represent all individuals in a single population is unlikely to be realistic, the order of magnitude of the difference between

HR-BioAO and the threshold values was intriguing. When applying the proposed BioAO calculated with the adjusted buffer size, current thresholds again failed to discriminate species into different risk categories. As stated earlier, since species included in this study covered risk categories from SC to EN, I expected BioAO size of these species to cross the threshold levels.

Both cases suggest that the current standard thresholds may be inappropriate for assessing extinction risk in freshwater fish species. In addition, no rationale for the current thresholds values was found during the literature review of this study. These threshold sizes were

determined presumably based on the early distribution - abundance studies, which were mainly conducted for land birds and large terrestrial mammals with grid sizes of 10s of kilometers

(Gaston and Lawton 1990a, 1990b). Similar threshold issue was also noted in status assessments for other taxa with small ranges. Reduction in thresholds for criterion B was suggested for Red

List evaluation in small-bodied arthropods and spiders (Cardoso et al. 2011) and sessile vascular plants in Britain (Keith et al. 2000).

To differentiate species into appropriate conservation categories, new sets of critical thresholds should be adopted to reflect biologically meaningful scales, and should match the spatial scales at which AO is measured. One way of achieving this goal may be to use home range –body size allometric relationships specific to different taxonomic groups (Hendriks et al.

2009) to correct the threshold levels to appropriate scales. A simpler approach to this problem is currently used by NatureServe, which provides another frequently used conservation status assessment procedure using a set of indices rated individually and then weighted to produce the final status rank for a species (Faber-Langendoen et al. 2012). In their assessments, differential sets of threshold scales for AO were adopted, based on the types of ecosystems: matrix, large patch, small patch, and linear ecosystem (Master et al. 2012). The recommended threshold scale for species found in linear habitats (2km2 and smaller is considered imperiled) is 100 times finer than that used for matrix ecosystems (Master et al. 2012).

Conclusion

This study comprehensively reviewed the measurement of area of occupancy used in the assessment of conservation status for freshwater organisms. Characterizing local conservation biogeographic patterns demands careful considerations of where species actually occur. 60

Inappropriate measures of area of occupancy compromises its usefulness in conservation ranking practices that precede conservation actions. For the freshwater taxa that are inherently restricted by natural habitat boundaries, current methodological approaches are likely to produce incorrect estimations on the relative likelihood of extinction, leaving imperiled species misclassified and resulting in misallocation of limited conservation resources. I recommend replacement of the traditional grid-based approach with an ecologically meaningful measurement that accounts for habitat dimensionality and mobility on a species basis. The proposed method would also apply to other species that occur in linear and small patchy habitats. Subsequent revision of critical thresholds will be a crucial next step for adapting to scales that are biologically meaningful for these species.

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6. Appendices

Appendix 1. COSEWIC designated conservation status and threat factors identified for freshwater fish species in Canada.

Threats Common COSEWIC Scientific Name Name status Change in Habitat Alien Natural Native Human Loss/ Invasive Harvest Pollution Persecution Disaster Species Disturbance Degradation Species Dynamics Banff Rhinichthys EX P P S Longnose cataractae Dace smithi

Blue Walleye Sander vitreus EX S P S glaucus

Deepwater Coregonus EX P P Cisco johannae

Hadley Lake Gasterosteus EX P Benthic aculeatus Threespine Stickleback

Hadley Lake Gasterosteus EX P Limnetic aculeatus Threespine Stickleback

Lake Ontario Coregonus kiyi EX S P Kiyi orientalis

Gravel Chub Erimystax ET P S x-punctatus

Paddlefish Polyodon ET S S spathula

Striped Bass Morone saxatilis ET S P (St. Lawrence Estruary population)

Atlantic Coregonus EN P S S Whitefish huntsmani

Aurora Trout Salvelinus EN S S P fontinalis timagamiensis

Copper Moxostoma EN P S S P Redhorse hubbsi

Enos Lake Gasterosteus EN P P P Benthic aculeatus Threespine Stickleback

Enos Lake Gasterosteus EN P P P Limnetic aculeatus Threespine Stickleback

Lake Erimyzon sucetta EN P S S Chubsucker

Lake Sturgeon Acipenser EN S P (Nelson River fulvescens populations)

Lake Sturgeon Acipenser EN S P (Saskatchewan fulvescens River populations)

Lake Sturgeon Acipenser EN S P S S (Red- fulvescens Assiniboine Rivers - Lake Winnipeg populations)

Lake Sturgeon Acipenser EN S P S S (Winnipeg fulvescens River - English River populations)

Lake Sturgeon Acipenser EN S P (Western fulvescens Hudson Bay populations)

Misty Lake Gasterosteus EN S P S Lentic aculeatus Threespine Stickleback

Misty Lake Gasterosteus EN S P S Lotic aculeatus Threespine Stickleback

Nooksack Rhinichthys EN P S S S Dace cataractae ssp

Northern Noturus EN S P Madtom stigmosus

Paxton Lake Gasterosteus EN P P S Benthic aculeatus Threespine Stickleback

Paxton Lake Gasterosteus EN P P S Limnetic aculeatus Threespine Stickleback

Pugnose Notropis EN P S S Shiner anogenus

Redside Dace Clinostomus EN P S P S S elongatus

Salish Sucker Catostomus EN P S P catostomus

Shortnose Coregonus EN S P S Cisco reighardi

Speckled Dace Rhinichthys EN P S P osculus

Spring Cisco Coregonus sp. EN S P P

Vananda Gasterosteus EN P P S Creek Benthic aculeatus Threespine Stickleback

Vananda Gasterosteus EN P P S Creek Limnetic aculeatus Threespine Stickleback

Western Brook Lampetra EN P S Lamprey richardsoni (Morrison Creek population)

Western Hybognathus EN P P Silvery argyritis Minnow

White Acipenser EN P P Sturgeon transmontanus

Atlantic Acipenser TH S P S S Sturgeon oxyrinchus (St Lawrence population)

Atlantic Acipenser TH S P S S Sturgeon oxyrinchus (Maritime population)

Black Moxostoma TH P S P Redhorse duquesnei

Carmine Notropis TH P S S S S Shiner percobromus

Channel Percina TH P S Darter copelandi

Coastrange Cottus aleuticus TH S S S Sculpin (Cultus population)

Eastern Sand Ammocrypta TH P S P Darter pellucida (Ontario population)

Eastern Sand Ammocrypta TH P S P Darter pellucida (Quebec population)

Lake Sturgeon Acipenser TH S P S (Great Lakes - fulvescens Upper St. Lawrence populations)

Mountain Catostomus TH P S S Sucker platyrhynchus (Milk River populations)

Rainbow Smelt Osmerus mordax TH P P P (Lake Utopia Small-bodied population)

Rainbow Smelt Osmerus mordax TH P P P (Lake Utopia Large-bodied population)

Mountain Cuttus sp TH P P Sculpin (Eastslope populations)

Shortjaw Cisco Coregonus TH S P P S zenithicus

Silver Shiner Notropis TH P S P photogenis

Spotted Gar Lepisosteus TH P oculatus

Striped Bass Morone saxatilis TH P (Southern Gulf of St. Lawrence population)

Stiped Bass Morone saxatilis TH P S S (Bay of Fundy population)

Umatilla Dace Rhinichthys TH P P umatilla

Vancouver Lampetra TH P S Lamprey macrostoma

Westslope Oncorhynchus TH P P P S Cutthroat clarkii lewisi Trout (Alberta population)

American Eel Anguilla rostrata SC P P S

Banded Fundulus SC P Killifish diaphanus (Newfoundlan d population)

Bering Cisco Coregonus SC S S laurettae

Bigmouth Ictiobus SC P S S S S S Buffalo cyprinellus (Saskatchewan - Nelson River populations)

Blackstripe Fundulus notatus SC S Topminnow

Bridle Shiner Notropis SC P S S P bifrenatus

Charlotte Gasterosteus SC S S S Unarmoured aculeatus Threespine Stickleback

Columbia Cottus hubbsi SC P S Sculpin

Deepwater Myoxocephalus SC P S S Sculpin thompsonii (Great Lakes - Western St. Lawrence populations)

Dolly Varden Salvelinus SC P P S (Western malmamalma Arctic population)

Giant Gasterosteus SC S Threespine aculeatus Stickleback

Grass Pickerel Esox americanus SC P vermiculatus

Green Acipenser SC S S S Sturgeon medirostris

Upper Great Coregonus kiyi SC S P Lakes Kiyi kiyi

Lake Sturgeon Acipenser SC P P S (Rainy River- fulvescens Lake of the Woods populations)

Lake Sturgeon Acipenser SC S P (Southern fulvescens Hudson Bay and James Bay populations)

Mountain Catostomus SC P S S S Sucker platyrhynchus (Pacific populations)

Northern Ichthyomyzon SC S S S Brook fossor Lamprey (Great Lakes - Upper St. Lawrence populations)

Orangespotted Lepomis humilis SC S S S Sunfish

Pugnose Opsopoeodus TH S Minnow emiliae

River Moxostoma SC P S P S Redhorse carinatum

Rocky Cottus sp SC P Mountain Sculpin (Westslope populations)

Shorthead Cottus confusus SC S S Sculpin

Shortnose Acipenser SC S S S Sturgeon brevirostrum

Silver Chub Macrhybopsis EN P P storeriana

Silver Lamprey Ichthyomyzon SC S S P (Great Lakes - unicuspis Upper St. Lawrence populations)

Spotted Minytrema SC P Sucker melanops

Squanga Coregonus sp. SC S S Whitefish

Warmouth Lepomis gulosus SC S

Westslope Oncorhynchus SC P P P S Cutthroat clarkii lewisi Trout (British Columbia population) P - primary threat factor; S - secondary threat factor

Appendix 2. COSEWIC designated conservation status and reported threat factors for freshwater mollusc species in Canada.

Threats Change In COSEWIC Habitat Alien Common Name Scientific Name Natural Native Human Intrinsic Status Loss/ Invasive Harvest Pollution Disaster Species Disturbance Factors Degradation Species Dynamics Dwarf Alasmidonta EX P Wedgemussel heterodon

Banff Springs Physella johnsoni EN P S S Snail

Eastern Ligumia nasuta EN S S Pondmussel

Fawnsfoot Truncilla EN P P S donaciformis

Hickorynut Obovaria olivaria EN P P P P

Hotwater Physa Physella wrighti EN S S S S

Kidneyshell Ptychobranchus EN P P P S fasciolaris

Lake Winnipeg Physa sp. EN P P Physa Snail

Mapleleaf Quadrula EN P P Mussel quadrula

Norhtern Epioblasma EN P P P S Riffleshell torulosa rangiana

Rainbow Mussel Villosa iris EN P P

Rayed Bean Villosa fabalis EN P P

Rocky Mountain Gonidea EN P P S Ridged mussel angulata

Round Obovaria EN P P P S Hickorynut subrotunda

Round Pigtoe Pleurobema EN P P P sintoxia

Salamander Simpsonaias EN P P P S Mussel ambigua

Snuffbox Epioblasma EN P P P S triquetra

Mapleleaf Quadrula TH P P Mussel quadrula

Brook Floater Alasmidonta SC P S S S varicosa

Wavy-rayed Lampsilis SC P S P Lampmussel fasciola

Yellow Lampsilis cariosa SC S S Lampmussel

P - primary threat factor; S - secondary threat factor

Appendix 3. Type of habitats used by freshwater species at risk in Canada. a) fishes; b) molluscs.

15 EN

10 TH

NumberTaxa of SC 5

0 Stream Lake wetland Estuary Marine water (a) 14

8 EN TH 6 SC

NumberSpecies of 4

0 Stream Lake Hot spring (b)

Appendix 4. Proportion of the two components of BioAO: stream occupancy and lake/wetland occupancy for 20 fish species at risk in Ontario.

Warmouth Spotted Sucker Spotted Gar Silver Shiner Silver Lamprey Silver Chub River Redhorse Redside Dace Pugnose Shiner Pugnose Minnow Lake/Wetland Northern Madtom Stream North Brook Lamprey Lake Sturgeon Lake Chubsucker Grass Pickerel Eastern Sand Darter Channel Darter Bridle Shiner Blackstripe Topminnow Black Redhorse

0% 20% 40% 60% 80% 100%

Appendix 5. Breakdown of stream BioAO for 17 species inhabited stream environment: area for raw length of occupied stream segment, buffer segment area and area for single locations.

Spotted Sucker Silver Shiner Silver Lamprey River Redhorse Redside Dace Pugnose Shiner Pugnose Minnow Northern Madtom Raw Stream Segment North Brook Lamprey Lake Sturgeon Single Location Lake Chubsucker

Grass Pickerel Buffer Stream Segment Eastern Sand Darter Channel Darter Bridle Shiner Blackstripe Topminnow Black Redhorse

0% 20% 40% 60% 80% 100%