DOI: 10.5061/dryad.g79cnp609
title: Data for: Structured decision-making in suburban and semi-rural community deer management

description: Urban deer management (UDM) decision-makers face social, ecological,
 regulatory, and economic pressures when creating an agreeable deer
 management plan for stakeholders. Historically, decision-making techniques
 (e.g., consensus-based analyses) have not effectively balanced UDM
 elements leading to short-lived management progress. Structured
 decision-making (SDM) is a formal, values-based approach to identifying an
 optimal management solution. Although SDM has been applied to other
 wildlife management decisions, it has not been applied in UDM. We provide
 the first case study of SDM-based UDM and streamline the process for
 wildlife managers. We focused on one suburban and one semi-rural community
 near Atlanta, Georgia, USA. We established a problem statement to capture
 what decision-makers must address in UDM programs and reviewed the primary
 literature and UDM plans from 5 states to establish four fundamental
 objectives. We then utilized a Support–Effectiveness Analysis to identify
 acceptable UDM alternatives and gathered expert insights to calculate the
 consequences of alternatives on objectives. Finally, we asked each
 community’s decision-makers to weigh objectives against each other. Using
 this framework, education was the most optimal UDM technique to implement
 in the suburban community, and sharpshooting was the most optimal UDM
 technique to implement in the semi-rural community. This paper positions
 SDM as a transparent, defensible, inclusive, and adaptive approach to UDM.
 Furthermore, our SDM framework provides managers with the means and
 justification to create an optimal plan of action for communities in need
 of UDM.
description: # Data for: Structured Decision-Making in Suburban and Semi-rural
 Community Deer Management 
 [https://doi.org/10.5061/dryad.g79cnp609](https://doi.org/10.5061/dryad.g79cnp609) ## Description of the data and file structure We used the 5-step Structured Decision Making approach known as PrOACT for community-based deer management.  ### Files and variables #### File: Structured Decision-Making in Suburban and Semi-rural Community Deer Management.xlsx **Description:** In our research, we utilized Structured Decision-Making for community-based deer management. Our case-study utilized one suburban and one semi-rural community near Atlanta, GA, USA and we distributed a questionnaire in both communities. Structured Decision-Making uses a 5-step approach known as PrOACT (Problem, Objectives, Alternatives, Consequences, and Tradeoffs). We have included data for each step of PrOACT. Some respondents did not answer each question of the questionnaire and for those that left a question blank, we have designated that non-answer with “n/a” in the dataset.   In the “**Problem**” stage, we identified the top three conflicts each community faced. The data included in the problem stage included: Occurrence data (Scale: 1(never)-5(Very often)), Tolerance data (Scale: 1(very tolerable) - 5(Very Intolerable)),  Experience with deer (Scale: 1(very negative) - 5(very positive)), Satisfaction with community (Scale: 1(Very dissatisfied) - 5(very satisfied)), Issue severities (Scale: 1(Not an issue) - 5(Extreme issue)). In the “**Objectives**” stage, we used a principal component analysis to reduce 25 objectives-based items. These data included Occurrence data (Scale: 1(never)-5(Very often)), Tolerance data (Scale: 1(very tolerable) - 5(Very Intolerable)), Desired population changes (Scale: 1(Dramatically Decrease) - 5(Dramatically Increase)), Concern items (Scale: 1(Very Unconcerned) - 5(Very Concerned)), Issue severities (Scale: 1(Not an issue) - 5(Extreme issue)), and Satisfaction with the community (Scale: 1(Very dissatisfied) - 5(very satisfied)). In the “**Alternatives**” stage, we identified acceptable techniques using a Support-Effectiveness Analysis with a data-centered central axis that split the analysis into 4 quadrants (see manuscript for more information). The data used in this stage include: Support (Scale: 1(Very Unsupportive) - 5 (Very supportive)), and Effectiveness (Scale: 1 (Very ineffective) - 5(Very Effective)) data. In the “**Consequences**” stage, we used expert insight to estimate how well each acceptable deer management technique would achieve each objective. The data used in the consequences stage can be found on the sheets labeled “ConsqUppr,” “ConsqLwr,” and “ConsqExact.” These sheets respectively correspond to the upper, lower, and exact probability estimates detailed in our manuscript. In the “Tradeoffs” stage, we asked each community’s decision-makers to assign importance values or weights to each objective. These data can be found on the “Tradeoffs” sheet. In future research using structured decision-making in community deer management, researchers can utilize our data to compare their collected data to our data.   We removed several indirect identifiers (i.e., residents’ demographics) of respondents per Dryad’s instructions to properly anonymize the data. For additional clarification on our research please refer to our manuscript titled “Strengthening Urban Deer Management with Structured Decision Making” in Wildlife Biology (DOI: 10.1002/wlb3.01370). If you have any additional questions about the data, please reach out to the corresponding author, Shane Boehne, at [shaneboehne@gmail.com](mailto:shaneboehne@gmail.com).  ##### Variables * Community ID (BT1_ML2) * Survey ID * Occurrence_Windows * Tolerance_Windows * Occurrence_Landscaping Plants * Tolerance_Landscaping Plants * Occurrence_Garden Plants * Tolerance_Garden Plants * Occurrence_Bird Feeders * Tolerance_Bird Feeders * Occurrence_Vehicle * Tolerance_Vehicle * Occurrence_Aggression * Tolerance_Aggression * Occurrence_Eating Seed * Tolerance_Easting Seed * Occurrence_Neighbors Feeding * Tolerance_Neighbors Feeding * Occurrence_Illegal Hunt * Tolerance_Illegal Hunt * Occurrence_Sabotage * Tolerance_Sabotage * Occurrence_Disagreements * Tolerance_Disagreements * Experw/Deer_RevCo * Satw/Comm_RevCo * IssueSeverity_DXH * IssueSeverity_HXH * Desired PopChng_RevCo * Level_Concern*_*Personal_Expense * Level_Concern_Community_Expense * Level_Concern*_*Humaneness * Level_Concern_Deer_Health * Level_Concern*_Mngt Impact* * Level_Concern*_Impact2 Neighbors* * Level_Concern*_Disease Risk* * Level_Concern*_Judgement Views* * Level_Concern*_Concern Time* * Level_Concern*_Judgement Mngt* * Support_habitat modification * Effective_habitat modification * Support_habitat improvement * Effective_habitat improvement * Support_hazing * Effective_hazing * Support_exclusion * Effective_exclusion * Support_repellants * Effective_repellants * Support_sharpshooting * Effective_sharpshooting * Support_controlled hunt * Effective_controlled hunt * Support_traditional hunt * Effective_traditional hunt * Support_eliminate feeding * Effective_eliminate feeding * Support_infrastructure * Effective_infrastructure * Support_speed * Effective_speed * Support_education * Effective_education * Support_do nothing * Effective_do nothing ## Code/software You can view the data in Microsoft Excel and we used SPSS for the principal component analysis