DOI: 10.5061/dryad.b8gtht7rc
title: Decision analysis rooted in Indigenous and Western scientific knowledge identifies cost-effective strategies for managing hyperabundant deer to restore keystone places

description: The hyperabundance of herbivores—a result of altered human relationality
 with the land and the extirpation of predators—is leading to large-scale
 degradation of keystone ecosystems across the globe. Designing and
 implementing socially acceptable and cost-effective strategies that
 meaningfully reduce herbivore populations while allowing for the recovery
 of ecological function and cultural relationality is an inherently complex
 issue. As a result, decision paralysis is common, leading to delayed or
 avoided action and continued ecosystem loss and degradation. Using a
 structured decision-making process that incorporated expert elicitation,
 population modeling, and cost-effectiveness analyses while honoring
 multiple knowledge systems, we identified five discrete and four portfolio
 strategies for managing hyperabundant black-tailed deer (Odocoileus
 hemionus columbianus) in the Southern Gulf Islands of British Columbia,
 Canada, with consideration to benefit, feasibility, and cost objectives.
 Hunting led by local Indigenous Nations was ranked the most cost-effective
 strategy when benefits were considered in terms of the well-being of
 peoples and place holistically, and accounted for both Indigenous and
 Western science worldviews. When only Western perspectives were included,
 increased licensed hunting by local communities and hiring professional
 deer reduction specialists were ranked the most cost-effective. However,
 while increased licensed hunting had a &gt;50% likelihood of project
 uptake and success (i.e., feasibility), the strategy had &lt;50%
 likelihood of achieving any benefit objective. In comparison,
 Indigenous-led hunting, professional deer reduction specialists, and all
 portfolio strategies had &gt;50% likelihood of meeting at least one
 benefit objective, although only Indigenous-led hunting also had
 &gt;50% likelihood of achieving feasibility objectives. We
 provide a roadmap for decision-makers across the globe to robustly and
 transparently assess the problem of herbivore hyperabundance and inform
 solutions within their context. Within the Salish Sea, our work highlights
 the need to support hunting, and in particular, Indigenous-led hunting, as
 cost-effective strategies to promote revitalization of wellbeing of
 peoples and place.
description: # Decision analysis rooted in Indigenous and Western scientific knowledge
 identifies cost-effective strategies for managing hyperabundant deer to
 restore keystone places Dataset DOI:
 [10.5061/dryad.b8gtht7rc](10.5061/dryad.b8gtht7rc) ## Description of the
 data and file structure Our methodology combined structured
 decision-making, expert elicitation, population modeling, and
 cost-effectiveness approaches to identify effective herbivore management
 strategies that are rooted in place-based context and that honor multiple
 knowledge systems. Data was derived from a combination of expert elicited
 and modeled values for the benefits, feasibilities, and costs of
 strategies. Files are available for developed data on consequence and
 tradeoff values, as well as the benefit, feasibility, and cost data
 underlying consequences. Supplementary materials are also available
 describing the values, justification, and sources behind the deer
 population model and the strategy costing variables. ## File:
 ExpertElicitation_BenefitsFeasibilities.xlsx **Description:** Data from
 structured expert elicitation of benefits and feasibilities, as part of
 the Consequences section of structured decision making. The two sheets
 provide the elicitation values from Indigenous and Western experts
 respectively, as each group of experts had tailored elicitation
 approaches. All units of elicited estimates are 0 to 100% likelihood of
 the strategy meeting the objective for the island scope. #### **Indigenous
 Experts** * Expert: anonymous expert name * Objective: objective being
 elicited * Strategy: strategy being elicited * Scope: large- or
 small-island scope * bestguess: best-guess average elicited estimate *
 upper_stand80: upper confidence intervals from the group’s standard error,
 standardized to 80% confidence interval * lower_stand80: lower confidence
 intervals from the group’s standard error, standardized to 80% confidence
 interval #### **Western Experts** * Expert: anonymous expert name *
 Objective: objective being elicited * Strategy: strategy being elicited *
 Scope: large- or small-island scope * bestguess: best-guess average
 elicited estimate * confidence: expert confidence in their lower and upper
 estimates * lower: lower elicited estimate * upper: upper elicited
 estimate * lower_stand80: lower elicited estimate standardized to 80%
 confidence interval * upper_stand80: upper elicited estimate standardized
 to 80% confidence interval ## File: DeerPopulationModel.xlsx
 **Description:** Spreadsheet detailing the variables and justifications
 underlying the deer population model. First sheet is a README that
 outlines the purpose of each subsequent sheet. The second sheet—'Deer
 Density’—outlines the deer density assumptions by island and across island
 scopes. The following sheets outline the variables for plants,
 competition, deer, cougars, or humans. Citations supporting values listed
 in ‘Sources’ columns, with references at the end of this document (p.
 comm. P. Arcese & T. Martin) (Arcese et al., 2014; Beckwith, 2004;
 Endara and Coley, 2011; Gillingham, 2008; Gillingham et al., 1997;
 Gladders, 2003; Gonzales, 2008; Hahn, 2001; Hanley and McKendrick, 1985;
 Hatter, 1988; Hatter and Janz, 1994; KNOPFF et al., 2010; McNay and
 Voller, 1995; Ministry of Environment, 1996; Mowat, 2023; Murphy, 1998;
 Parker et al., 1999; Ruth and Murphy, 2009; Shackelford et al., 2019;
 Sharpe, 1999; Waller, 2008; Wielgus et al., 2013). #### **Deer Density**
 By Island: * Island: island name(s) * Scope: large- or small-island scope
 * Area_km2: area of island (kilometers squared) * Deerkm2_Estimate:
 best-guess average number of deer per kilometer squared for each island *
 Deerkm2_Low: lower-guess average number of deer per kilometer squared for
 each island * Deerkm2_High: upper-guess average number of deer per
 kilometer squared for each island Summarized Large & Small Islands: *
 Scope: large- or small-island scope * Islands: number of islands in scope
 group * Area: total area per scope group (kilometers squared) *
 Deer_BestEstimate: total best-guess estimate of number of deer per scope *
 Deer_LowerEstimate: lower estimate of number of deer per scope *
 Deer_UpperEstimate: upper estimate of number of deer per scope ####
 **Plants** * Scope: large- or small-island scope * Palatability Class:
 deer palatability class groups * Variable: model variable * Estimate:
 best-guess estimate of model variable * Low: lower estimate of model
 variable * High: upper estimate of model variable * Source: references
 behind values * Justification: evidence and arguments behind values ####
 **Competition** * Competition Species: palatability class of first species
 * Competition Effect: palatability class of second species * Estimate:
 impact of first species on second species, best-guess estimate * Low:
 impact of first species on second species, lower estimate * High: impact
 of first species on second species, upper estimate * Source: references
 behind values * Justification: evidence and arguments behind values ####
 **Deer** * Deer Variables: abbreviated name of model variable * Full name:
 full name of model variable * Estimate: best-guess estimate of model
 variable * Low: lower estimate of model variable * High: upper estimate of
 model variable * Source: references behind values * Justification:
 evidence and arguments behind values #### **Cougars** * Cougar Variables:
 abbreviated name of model variable * Estimate: best-guess estimate of
 model variable * Low: lower estimate of model variable * High: upper
 estimate of model variable * Source: references behind values *
 Justification: evidence and arguments behind values #### **Humans** *
 Human Variables: abbreviated name of model variable * Estimate: best-guess
 estimate of model variable * Low: lower estimate of model variable * High:
 upper estimate of model variable * Source: references behind values *
 Justification: evidence and arguments behind values ## File: Costing.xlsx
 **Description:** Spreadsheet detailing the items underlying each of the
 strategies. First sheet is a README that outlines the purpose of each
 subsequent sheet. Following sheets outline the overarching actions of each
 strategy ('Strategies'), the annual costs per item of each
 discrete strategy computed by the deer population model, the action items
 of each of the discrete strategies ('Strategy "X"'),
 the portfolio combinations of discrete strategies ('Portfolio'),
 and the list of enabling actions behind each strategy ('Enabling
 Actions'). Costs per unit are not shared as they were determined from
 confidential interviews with managers and experts, whom are recognized in
 the manuscript and supplementary materials. #### **Strategies** *
 Strategies: numbered strategy name * Name: textual strategy name * Goal:
 the goal of each discrete strategy * List of Actions: the list of action
 components behind each discrete strategy * Description: general
 description of each strategy action item * Abbreviation: abbreviated form
 of each action item, used as action abbreviation in subsequent sheets ####
 **Costing** * Strategy: strategy number * Item: abbreviated name of the
 budget item * Units: metric unit of the budget item * Budget: whether item
 part of the reduced cost analysis (yes or no) * Year 1 to Year 10: columns
 provide the total cost ($CAD) per year per item of each strategy ####
 **Strategy 1-5/Enabling Actions** * Action Abbreviation: strategy action
 type per item * Item Abbreviation: item name (abbreviated form) * Item
 Description: description of item * Units: metric unit of item * Notes:
 additional details behind the item * Budget: whether item is included in
 reduced budget version (yes or no), with those marked as “no” highlighted
 in pink to visualize items removed from strategy under the budgeted
 version #### **Portfolio** * Strategy: numbered name of portfolio strategy
 * Name: long-form name of portfolio strategy including discrete strategy
 composition * Description: discrete strategies composing portfolio
 strategy * Percent Strategy "X" Budget: the proportion of the
 discrete strategy costs used for costing the portfolio strategy *
 Rationale Strategy "X": the justification behind the proportion
 of each strategy used ## File: Consequences.csv **Description:** Data from
 the Consequences section of structured decision making, providing benefit,
 feasibility, and cost values developed from previous steps in the
 analysis. #### **Consequences** * Strategy: strategy number * Scope:
 large-or small-island scope * Estimate: bestguess, lower, or upper
 sensitivity values * Maximize wellbeing: 0-100% likelihood of strategy
 meeting objective * Maximize ecosystem function: 0-100% likelihood of
 strategy meeting objective * Maximize project success:0-100% likelihood of
 strategy meeting objective * Maximize project uptake:0-100% likelihood of
 strategy meeting objective * Minimize deer density:0-100% likelihood of
 strategy meeting objective * InterestRate: various discount rates (0,
 0.04, 0.07) * BudgetCosts: net present value ($CAD) for reduced budget
 costs * TotalCosts: net present value ($CAD) for total costs ## File:
 Tradeoffs.csv **Description:** Data from the cost-effectiveness (CE)
 analysis of the Trade-offs section of structured decision making,
 developed from previous steps in the analysis. #### **Tradeoffs** *
 strategy: strategy number per alternative * scope: large- or small-island
 scope * estimate: best guess, upper, or lower estimate value *
 interest_rate: various discount rates (0, 0.04, 0.07) *
 MaxEcoFunction_TotalCost: CE value for the maximize ecosystem function
 objective using total costs * MinDeerDensity_TotalCost: CE value for the
 reduce deer density objective using total costs *
 MaxWellbeingBenefit_TotalCost: CE value for the maximize wellbeing
 objective using total costs * MaxWellbeingBenefit_TotalCost_NoFeas: CE
 value for the maximize wellbeing objective using total costs and no
 feasibility values * MaxEcoFunction_ReducedCost: CE value for the maximize
 ecosystem function objective using reduced costs *
 MinDeerDensity_ReducedCost: CE value for the reduce deer density objective
 using reduced costs * MaxWellbeingBenefit_ReducedCost: CE value for the
 maximize wellbeing objective using reduced costs *
 MaxWellbeingBenefit_ReducedCost_NoFeas: CE value for the maximize
 wellbeing objective using reduced costs and no feasibility values ####
 **References** Arcese, P., Schuster, R., Campbell, L., Barber, A., Martin,
 T.G., 2014. Deer density and plant palatability predict shrub cover,
 richness, diversity and aboriginal food value in a North American
 archipelago. Divers. Distrib. 20, 1368–1378.
 [https://doi.org/10.1111/ddi.12241](https://doi.org/10.1111/ddi.12241)
 Beckwith, B.R., 2004. The queen root of this clime: ethnoecological
 investigations of blue camas (Camassia leichtlinii (Baker) Wats., C.
 quamash (Pursh) Greene; Liliaceae) and its landscapes on southern
 Vancouver Island, British Columbia. Endara, M., Coley, P.D., 2011. The
 resource availability hypothesis revisited: a meta‐analysis. Funct. Ecol.
 25, 389–398.
 [https://doi.org/10.1111/j.1365-2435.2010.01803.x](https://doi.org/10.1111/j.1365-2435.2010.01803.x) Gillingham, M.P., 2008. Ecology of black-tailed deer in north coastal environments. Presented at the Lessons from the Islands: Proceedings from the Research Group on Introduced Species 2002 Conference, Queen Charlotte City, British Columbia. Canadian Wildlife Service, Environment Canada, Ottawa, Canada, pp. 39–47. Gillingham, M.P., Parker, K.L., Hanley, T.A., 1997. Forage intake by black-tailed deer in a natural environment: bout dynamics. Can. J. Zool. 75, 1118–1128. Gladders, A.D., 2003. Predation behaviour of Vancouver Island cougar (Puma concolor vancouverensis) and its relation to micro-and macroscale habitat. Gonzales, E.K., 2008. The effects of herbivory, competition, and disturbance on island meadows (PhD Thesis). University of British Columbia. Hahn, A.M., 2001. Social and spatial organization of Vancouver Island cougar (Puma concolor vancouverensis, Nelson and Goldman, 1943). University of British Columbia. [https://doi.org/10.14288/1.0090208](https://doi.org/10.14288/1.0090208) Hanley, T.A., McKendrick, J.D., 1985. Potential nutritional limitations for black-tailed deer in a spruce-hemlock forest, southeastern Alaska. J. Wildl. Manag. 103–114. Hatter, I., 1988. Effects of wolf predation on recruitment of black-tailed deer on northeastern Vancouver Island. Wildlife Branch, Ministry of Environment. Hatter, I.W., Janz, D.W., 1994. Apparent demographic changes in black-tailed deer associated with wolf control on northern Vancouver Island. Can. J. Zool. 72, 878–884. [https://doi.org/10.1139/z94-119](https://doi.org/10.1139/z94-119) KNOPFF, K.H., KNOPFF, A.A., KORTELLO, A., BOYCE, M.S., 2010. Cougar Kill Rate and Prey Composition in a Multiprey System. J. Wildl. Manag. 74, 1435–1447. McNay, R.S., Voller, J.M., 1995. Mortality causes and survival estimates for adult female Columbian black-tailed deer. J. Wildl. Manag. 138–146. Ministry of Environment, 1996. Safety Guide to Cougars [WWW Document]. URL [http://wwwt.env.gov.bc.ca/wld/documents/cougsf.htm](http://wwwt.env.gov.bc.ca/wld/documents/cougsf.htm) (accessed 1.29.24). Mowat, G., 2023. A review of cougar biology and management in British Columbia, Technical report. Province of British Columbia, Victoria, BC. Murphy, K.M., 1998. The ecology of the cougar (Puma concolor) in the northern Yellowstone ecosystem: interactions with prey, bears, and humans. University of Idaho. Parker, K.L., Gillingham, M.P., Hanley, T.A., Robbins, C.T., 1999. Energy and protein balance of free-ranging black-tailed deer in a natural forest environment. Wildl. Monogr. 3–48. Ruth, T.K., Murphy, K., 2009. Cougar-prey relationships. Cougar Ecol. Conserv. Univ Chic. Press Chic. 138–155. Shackelford, N., Murray, S., Bennett, J., Lilley, P., Starzomski, B., Standish, R., 2019. Ten years of pulling: Ecosystem recovery after long-term weed management in Garry oak savanna. Conserv. Sci. Pract. 1. [https://doi.org/10.1111/csp2.92](https://doi.org/10.1111/csp2.92) Sharpe, S., 1999. Management of deer on the Queen Charlotte Islands: biology of the species. Presented at the Proceedings of the Cedar Symposium: Growing Western Redcedar and Yellow-Cypress on the Queen Charlotte Islands/Haida Gwaii. Edited by GG Wiggins. BC Ministry of Forests/Canada–British Columbia South Moresby Forest Replacement Account, Victoria, BC, Citeseer, pp. 118–124. Waller, D.M., 2008. White-tailed deer impacts in North America and the challenge of managing a hyperabundant herbivore. Presented at the Proceedings from the research Group on Introduced Species 2002 Symposium, Queen Charlotte City, Queen Charlotte Islands, British Columbia, Canada. Canadian Wildlife Service, Environment Canada, Ottawa, Ontario, Canada, pp. 135–147. Wielgus, R.B., Morrison, D.E., Cooley, H.S., Maletzke, B., 2013. Effects of male trophy hunting on female carnivore population growth and persistence. Biol. Conserv. 167, 69–75. [https://doi.org/10.1016/j.biocon.2013.07.008](https://doi.org/10.1016/j.biocon.2013.07.008) #### Code & Software All data files can be viewed in microsoft excel spreadsheets, or any application able to view .xlsx or .csv files. #### Access Information Other publicly accessible locations of the data: * No other publicly accessible locations Data was derived from the following sources: * Expert elicitation * Regional deer population modeling literature * Regional costing literature and interviews