Abstract Decision-support models combine ecological understanding with utility considerations to evaluate potential results of management alternatives, thereby facilitating decision-making. They also provide a systematic, consistent, and rigorous framework for decision-making that is highly valuable for transparency in the management process. Despite the broad agreement on their importance, not many examples of organizations implementing their use at broad scale exist in the literature. Here we use the Pennsylvania Department of Conservation and Natural Resources Bureau of Forestry adoption and use of the SILVAH-Oak decision-support tool to guide management of mixed-oak forests as a case study to draw insights on managers’ adherence to decision-support tool recommendations. Of 97 cases evaluated, 69% of the managers chose to follow the decision-support tool recommendation. We attribute this high adherence to the manager-centered development of the SILVAH-Oak tool. When managers did not follow recommendations, they cited reasons related to the tool’s thresholds or considerations not accounted for by SILVAH-Oak. The delivery method was found to make a large difference in the level of adherence to the decision-support tool’s recommendations. Case studies like this one provide unique opportunities to learn about the adoption of decision-support tools. forest management, evidence-based management, ecological decision-making, structured decision-making, state-dependent management Management and Policy Implications Increasing the use of decision-support models to guide natural resource management is crucial to increase science-based management and to provide structure and transparency to the decision-making process. The case study presented here points to the importance of involving managers in the development of the decision-support tools since the early stages to ensure high adherence to the model’s recommendations. In addition, it indicates that decision-support tools with delivery methods that clearly show the model structure and thresholds utilized may be viewed more favorably by managers and be conducive to a higher adherence to management recommendations. This case study also highlights the need for continuous feedback between scientists and managers to improve the decision-support tool and the managers’ adoption of its recommendations. As natural resources experience more intense and varied stressors, the need to integrate ecological knowledge and management goals to develop decision-support tools to guide management becomes crucial for their sustainability. The importance of evidence-based and state-dependent decision-support tools for conservation and management of natural resources has been widely emphasized (Martin et al. 2009, Addison et al. 2016, Cook et al. 2016). Evidence-based tools use the best available evidence to guide management decisions while state-dependent tools, a type of evidence-based tool, focus on the state of the system to inform decision-making (Nichols and Williams 2006, Addison et al. 2016). These tools usually integrate ecological thresholds, arising from the current ecological understanding of the system, and utility thresholds, arising from human values attributed to specific ecological conditions, to define decision triggers (Martin et al. 2009, Cook et al. 2016). Decision triggers describe conditions that should prompt management action and are usually associated with a prescribed management alternative. A major benefit of decision-support tools is the systematic, consistent, and rigorous framework they provide, giving transparency to the decision-making process (e.g., Stout et al. 2007, Addison et al. 2016). Despite the broad agreement on the importance and usefulness of decision-support tools for conservation and management of natural resources, not many examples of organizations implementing the broad-scale use of these tools exist in the literature. However, there are organizations and institutions already taking advantage of the use of decision-support tools, and much can be learned from their experiences. Here we use as a case study a collaborative effort that is in its sixteenth year since implementation, to draw insights on managers’ adherence to decision-support tool recommendations and their preferences about the tool delivery method. A Case Study: The Use of the SILVAH-Oak Decision-Support Tool by Pennsylvania Forest Managers to Meet an Ecological Challenge SILVAH-Oak (Brose et al. 2008) is a state-dependent, ecologically based decision-support tool that provides management alternatives based on ecological and decision thresholds. It is an extension of the SILVAH tool developed by the US Department of Agriculture Forest Service Northeastern Research Station (Marquis and Ernst 1992), and originated in response to the challenge of regenerating mixed-oak forests in the Mid-Atlantic region of the United States. Mixed-oak forests are considered both ecologically and economically important in the region due to the hard mast and other habitat benefits they provide for wildlife and high timber value (Dey 2014). However, after stand-replacing disturbances, these forests are failing to regenerate into the same forest type and thus the area occupied by mixed-oak forests is declining and projected to decline even further unless there is purposeful management to maintain them (Nowacki and Abrams 2008, Dey 2014). In Pennsylvania, the Bureau of Forestry of the Pennsylvania Department of Conservation and Natural Resources (BoF hereafter) is tasked with managing 2.2 million acres of state forestland that harbor thousands of different plant and animal species. One of their multiple management objectives is to ensure the sustainability of mixed-oak forests (BoF 2016). The ecological challenge and the management goal of sustaining mixed-oak forests led to collaboration between the BoF forest managers, scientists from the US Department of Agriculture Forest Service, and faculty from The Pennsylvania State University. This collaboration started in the year 2000, managers were involved from the beginning, ecological understanding was formalized through workshops where expert opinion was used to determine variables of relevance and thresholds, and research needs were identified to keep improving the understanding of oak regeneration ecology (see Stout et al.  for a description of the tool development process). SILVAH-Oak requires a standardized inventory of a forest stand’s overstory and understory to characterize the state of the ecosystem and estimate the value of the variables for which ecological thresholds are set. Those variables include site characteristics, regeneration abundance and size, overstory species composition, and variables characterizing interference with the oak regeneration process. Management alternatives in this context are different silvicultural treatments and encompass the release of the advanced oak regeneration when regeneration is above a threshold, the enhancement of advanced regeneration before an overstory removal when the understory oak regeneration does not reach the threshold, or the artificial regeneration when conditions are such that no other treatment will lead to an oak-dominated forest (Brose et al. 2008). The decision-support tool is delivered in two ways: 1) computer software analyzes the data and returns a summary of the forest condition and a silvicultural prescription; and 2) users arrive to a silvicultural prescription by navigating decision flowcharts using estimates from the software forest stand summary (e.g., Figure 1). Figure 1. View largeDownload slide Example of a decision flowchart from one of the cases in the sample. Figure 1. View largeDownload slide Example of a decision flowchart from one of the cases in the sample. After its development, the BoF adopted the use of the SILVAH-Oak tool to obtain management recommendations prior to an intended timber harvest. It is the policy of the BoF that its forest managers can choose to implement the recommended management treatment or, with justification, can choose to implement a different treatment. When foresters choose not to follow SILVAH-Oak recommendations and implement an overstory removal (harvest), the consequences could be of a changed forest type post-harvest, or of regenerating a mixed-oak forest with less time and economic investment if the manager is contemplating aspects of the ecosystem not considered by the decision-support tool. Managers’ Adherence to SILVAH-Oak Management Prescriptions Given that the BoF requires the use of the decision tool but allows for a manager to disagree with the recommended management, we wanted to learn about what proportion of managers adhere to the recommended management alternative, and of those that do not adhere, what are the motivating reasons to disagree. We expected a high level of adherence since the managers were involved in the process from the beginning of the tool’s development and the tool provides solid technical guidance; both aspects that have been reported to be valued by managers in general (Addison et al. 2016). We selected a sample of 97 cases where the BoF managers used SILVAH-Oak before implementing an overstory removal and had a management goal of regenerating a mixed-oak forest. We sampled from the earliest time period following the tool’s development (years 2001–2008) because this is a critical time for the tool’s adoption success. In addition, we focused only on those cases where an overstory removal occurred because that is the most crucial treatment recommendation: if the overstory is removed without the presence of enough advanced oak regeneration, the likelihood of regenerating a mixed-oak stand is practically non-existent. For each case, we had access to documentation about the required standardized forest inventory, the SILVAH-Oak run and recommendation, and a letter where the manager presented the case to follow or not follow the model recommendation. As we expected, a large percentage of the managers (69%) followed the recommendation from at least one of the decision-support tool’s delivery methods. When they didn’t follow the recommendations, they cited reasons that we classified into either 1) disagreement with the thresholds (33%), or 2) factors not considered by the decision-support tool (67%). The cases of disagreement with the thresholds were in some instances a result of the threshold being a single number and the value obtained by the manager somewhat close to such number. For example, for the variable percent cover of interfering vegetation, the threshold is set to 30%. In one of the cases where the inventory results indicated that the percent cover was 35%, the manager chose to follow the <30% path. In other cases, it seems managers simply disagreed with the threshold (e.g., following the path above the threshold when the estimate was largely below it), likely due to the knowledge of site-specific conditions not considered by SILVAH-Oak. A way to reduce disagreement when values are close to thresholds is to use range thresholds rather than point thresholds, as this may facilitate managers to add their experience and site-related knowledge when values describing the state of the ecosystem are borderline. Range thresholds can also be more realistic, as they can account for different sources of uncertainty affecting the management alternatives under consideration (e.g., Ascough et al. 2008). In terms of factors not considered by SILVAH-Oak, there were a few arguments that appeared in the managers’ justification letters. These included: 1) the presence of poor overstory health condition because of mortality, gypsy moth attacks, or other events, 2) poor overstory species composition, and 3) timber value considerations. Often these factors were linked. Some of these aspects may be possible to incorporate into SILVAH-Oak. In fact, the continuous feedback between managers and modelers has been a characteristic of the SILVAH-Oak tool development over the years (Stout et al. 2007). For example, in this early time period the presence of cohorts of “new oak seedlings” were not taken into account by the tool’s assessment of regeneration. Because the managers’ experience indicated that those seedlings were ecologically important in the regeneration process, SILVAH-Oak was reviewed and the contribution of these seedlings was incorporated in the determination of decision thresholds. Aspects such as timber value could be included in SILVAH-Oak through utility thresholds, although it may require substantial modifications to the tool. In cases where incorporating some of the factors may be beyond the scope of SILVAH-Oak, it may still be important to develop guidelines for those instances, such as how timber value considerations should interact with the different SILVAH-Oak management prescriptions. This will ensure consistency and maintain the overall decision-making structure that has been provided by the adoption of the decision-support tool. Preferences and Ease of Use of Different Delivery Methods We also wanted to understand whether the decision-tool delivery method, that is, software or decision flowcharts, influenced its use. Specifically, we wanted to know whether there was a difference in their correct use and whether managers adhere to the management prescription of one delivery method more often than the management prescription of the other. Of the 97 cases, 60% had records for both the decision flowcharts and the software output, 18% had records for the decision flowcharts and the software calculations but not for the software prescription, and 22% only had the software output (no decision flowcharts were used). We found that 70% of managers that used decision flowcharts followed them correctly and thus arrived at the correct management alternative. Of those that use the decision flowcharts incorrectly, 64% arrived at an incorrect prescription while the mistakes made by the other 36% did not result in a prescription change (Figure 2). The correct use of the software was more difficult to determine, with cases where it was certain managers had used the software incorrectly (6.3% of all those that used software), cases where we couldn’t determine (82.3%), and cases where it was certain they used it correctly (11.3%, Figure 2). Wrong use of the software would have resulted in the wrong management recommendation, but not in the wrong calculation of the forest stand variables. In this early period, a common mistake in the use of the software was to use the wrong default settings. Since then, both the software and the output have become more user-friendly. An increased emphasis in the use of the software during annual trainings has resulted in managers having a better understanding of what the default settings mean and how to change them. Figure 2. View largeDownload slide Use of the decision flowcharts and software by managers. For the decision flowchart: in dark gray is percent of managers that used them correctly, hatching is the percent that used them incorrectly but arrived at the correct recommendation, and white is the percent that used it wrong and arrived at an incorrect recommendation. For the software: dark gray is the percent of managers that used it correctly, hatching is the percent we could not determine, and white is the percent that used it incorrectly. Figure 2. View largeDownload slide Use of the decision flowcharts and software by managers. For the decision flowchart: in dark gray is percent of managers that used them correctly, hatching is the percent that used them incorrectly but arrived at the correct recommendation, and white is the percent that used it wrong and arrived at an incorrect recommendation. For the software: dark gray is the percent of managers that used it correctly, hatching is the percent we could not determine, and white is the percent that used it incorrectly. Of those having records for the decision flowcharts, 76% followed the management alternative prescribed by the decision flowcharts. In contrast, only 30% of those cases that had the software prescription documentation followed its recommended management alternative. In about half of the cases where the managers did not follow the software prescription, managers had differing prescriptions from the software and flowcharts, and preferred to follow the alternative prescribed by the flowcharts (94% of cases where there was disagreement in the prescriptions given by the two delivery methods). In some instances of prescription disagreement between the software and decision flowcharts, there is evidence of incorrect use of the software, while in a few cases it was the decision flowchart that was followed incorrectly. This case study highlights the importance of the decision-support tool delivery method. The more familiar and comfortable managers are with the delivery method (in this case the decision flowcharts), the more inclined to follow the management recommendation they may be. A potential additional advantage of the decision flowcharts is that they allow managers a better understanding of how the decision-support tool works; the manager that works through the flowcharts learns what the model takes into consideration to make a prescription and what the threshold value at each split is. The prescription from the software, on the other hand, could be perceived as less transparent if the manager is not familiar with the tool’s literature. This case study also points to the advantage of using more than one delivery method when there is a transition in technology. In this case study, even though both the software and decision flowcharts were introduced at the same time, the decision flowcharts were more user-friendly than the software. We would expect the difference in preference to have decreased since that early period due to the improvement of the software user interface. On the Value of a Manager-Centered Decision-Support Tool The BoF has been using the SILVAH-Oak model for more than a decade now. The large adherence to its management prescriptions even in this very early period is, in our opinion, a result of this being a manager-centered effort. Managers were involved in the development of the decision-support tool from the beginning. In addition, managers receive training in the use of the tool, from collecting the inventory data to interpreting the final management prescription (Figure 3; Stout et al. 2007). As important as those trainings are for knowledge acquisition, they crucially provide an opportunity for feedback between managers and scientists, thereby ensuring managers’ participation in the continued improvement of the decision-support tool. We also believe that giving the managers the opportunity to disagree with SILVAH-Oak recommendations, given a justification, allows for an appreciation of unique forest stand situations and the manager’s knowledge of the particular stand, which is likely very valued by the managers. We suggest that keeping track of reasons managers provide for not following the tool’s management recommendation can also provide important feedback for its improvement and development. Figure 3. View largeDownload slide Managers participating in the field component of the SILVAH-Oak training where they practice obtaining the standardized inventory sample. Figure 3. View largeDownload slide Managers participating in the field component of the SILVAH-Oak training where they practice obtaining the standardized inventory sample. Conclusions In this case study, we evaluate the use of SILVAH-Oak, a decision-support tool that has been adopted at the state level in Pennsylvania to sustainably manage a particular forest type. We conclude that the managers’ high adherence to the tool’s management recommendation is likely the result of it being developed and continuously updated in close collaboration with the managers. Results also indicate that the decision-support tool delivery method is important in determining the level of adherence to management recommendations, with more user-friendly alternatives being preferred. Case studies like the one presented here provide unique opportunities to learn about the adoption of decision- support tools. Next steps should include evaluations of the effectiveness of these tools to achieve management goals. Acknowledgments This work was funded by the Pennsylvania Department of Conservation and Natural Resources Bureau of Forestry and by an Undergraduate Research Grant from The Pennsylvania State University College of Agricultural Science. Reviews and suggestions by Robert Beleski, Patrick Brose, Andrew Duncan, Brian Salvato, Susan Stout, and two anonymous reviewers have improved this manuscript and are greatly appreciated. Literature Cited Addison , P.F.E. , C.N. Cook , and K. de Bie . 2016 . Conservation practitioners’ perspectives on decision triggers for evidence-based management . J Appl Ecol . 53 : 1351 – 1357 . Google Scholar CrossRef Search ADS Ascough , J.C. II , H.R. Maier , J.K. Ravalco , and M.W. Strudley . 2008 . Future research challenges for incorporation of uncertainty in environmental and ecological decision-making . Ecol Model . 219 : 383 – 399 . Google Scholar CrossRef Search ADS BoF . 2016 . 2016 State forest management plan . Pennsylvania Department of Conservation and Natural Resources, Bureau of Forestry . Available online at http://www.dcnr.state.pa.us/cs/groups/public/documents/document/dcnr_20032045.pdf; last accessed Jan. 11, 2018 . Brose , P.H. , K.W. Gottschalk , S.B. Horsley , P.D. Knopp , J.N. Kochenderfer , B.J. McGuinness , et al. 2008 . Prescribing regeneration treatments for mixed-oak forests in the Mid-Atlantic region. USDA For. Serv . Gen. Tech. Rep . NRS-GTR-33, 100 p . Cook , C.N. , K. de Bie , D.A. Keith , and P.F.E. Addison . 2016 . Decision triggers are critical part of evidence-based conservation . Biol Conserv . 195 : 46 – 51 . Google Scholar CrossRef Search ADS Dey , D.C . 2014 . Sustaining oak forests in Eastern North America: Regeneration and recruitment, the pillars of sustainability . For Sci . 60(5) : 926 – 942 . Marquis , D.A. , and R.L. Ernst . 1992 . User’s guide to SILVAH: Stand analysis, prescription and, management simulator program for hardwood stands of the Alleghenies. USDA For. Serv . Gen. Tech. Rep . NE-GTR-162, 124 p . Martin , J. , M.C. Runge , J.D. Nichols , B.C. Lubow , and W.L. Kendall . 2009 . Structured decision making as a conceptual framework to identify thresholds for conservation and management . Ecol. Appl . 19(5) : 1079 – 1090 . Google Scholar CrossRef Search ADS Nichols , J.D. , and B.K. Williams . 2006 . Monitoring for conservation . Trends Ecol. Evol . 21(12) : 668 – 673 . Google Scholar CrossRef Search ADS Nowacki , G.J. , and M.D. Abrams . 2008 . The demise of fire and “mesophication” of forests in the eastern United States . Bioscience . 58(2) : 123 – 138 . Google Scholar CrossRef Search ADS Stout , S. , P. Brose , K. Gottschalk , G. Miller , P. Knopp , G. Rutherford , M. Deibler , et al. 2007 . SILVAH-OAK: Ensuring adoption by engaging users in the full cycle of forest research . P. 229 – 238 in Proceedings: International Conference on Transfer of Forest Science Knowledge and Technology , Miner , C. , R. Jacobs , D. Dykstra , and B. Bittner (eds.). USDA For. Serv. Gen. Tech. Rep. PNW-GTR-726. © 2018 Society of American Foresters This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
Journal of Forestry – Oxford University Press
Published: May 23, 2018
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