Difficult climate-adaptive decisions in forests as complex social-ecological systems

Abstract

Climate change threatens the social, ecological, and economic benefits enjoyed by forest-dependent communities worldwide. Climate-adaptive forest management strategies such as genomics-based assisted migration (AM) may help protect many of these threatened benefits. However, such novel technological interventions in complex social-ecological systems will generate new risks, benefits, and uncertainties that interact with diverse forest values and preexisting risks. Using data from 16 focus groups in British Columbia, Canada, we show that different stakeholders (forestry professionals, environmental nongovernmental organizations, local government officials, and members of local business communities) emphasize different kinds of risks and uncertainties in judging the appropriateness of AM. We show the difficulty of climate-adaptive decisions in complex social-ecological systems in which both climate change and adaptation will have widespread and cascading impacts on diverse nonclimate values. Overarching judgments about AM as an adaptation strategy, which may appear simple when elicited in surveys or questionnaires, require that participants make complex trade-offs among multiple domains of uncertain and unknown risks. Overall, the highest-priority forest management objective for most stakeholders is the health and integrity of the forest ecosystem from which all other important forest values derive. The factor perceived as riskiest is our lack of knowledge of how forest ecosystems work, which hinders stakeholders in their assessment of AM's acceptability. These results are further evidence of the inherent risk in privileging natural science above other forms of knowledge at the science-policy interface. When decisions are framed as technical, the normative and ethical considerations that define our fundamental goals are made invisible.

Keywords: British Columbia; climate change adaptation; forests; genomics-based assisted migration; judgment and decision-making.

Fig. 1.

Focus group structure. Each focus group comprised five deliberative sections, a three-part questionnaire, and a short tutorial on anticipated climate change in BC’s forests and reforestation strategies that might help forests adapt. Each of the deliberative sections began with the individual elicitation of concepts on sticky notes followed by deliberation and grouping of concepts and ending with an individual scoring exercise to judge the relative importance of the grouped concepts.

Fig. 2.

Calculating mean scores for coding categories. In each of the five deliberative sections (forest values, forest risks, and AM risks, benefits, and uncertainties), each participant was provided with 10 points with which to judge the importance of the deliberatively grouped concepts. During the analysis, these scores were corrected (i.e., inversely weighted by the number of groupings, which varied from three to five) to ensure that the expected mean score (2.5) was constant across all sessions regardless of the number of groupings. Otherwise, sessions in which there were only three groupings would have had mean scores of 3.3, while sessions with five groupings would have had mean scores of 2.0. These corrected scores were then assigned to the underlying concepts within each grouping. Once the concepts were coded into subthemes, themes, and overarching categories, mean scores were calculated for each participant in each section and overarching category. Categories for which no concept was raised (and, therefore, none were grouped and scored) were assigned a score of zero. Each participant therefore had 45 mean scores for the overarching coding categories (e.g., five deliberative sections in the focus group structure multiplied by nine overarching coding categories). However, coding categories for which no concepts were raised in a section by any participants were excluded from further analysis (e.g., no participants mentioned “AM” or “None” in the forest values section, and nobody mentioned “Intrinsic” in the uncertainties section).

Fig. 3.

Importance of forest values and risks aggregated across all stakeholder groups (n = 103). The diameter of each bubble corresponds to the proportion of total elicited values or risks that were coded into the corresponding category. Participants contributed forest values corresponding to four of the coding categories, which overlapped with the six categories of elicited forest risks; the remaining three categories are therefore excluded from this figure. The stakeholder groups were weighted to account for the different number of participants in each.

Fig. 4.

Importance of AM risks by stakeholder group (n = 103). The diameter of each bubble corresponds to the proportion of elicited uncertainties in the corresponding stakeholder group. The difference in mean score across stakeholder groups for each risk category was tested using a one-way ANOVA (*P < 0.05, **P < 0.01, and ***P < 0.001).

Fig. 5.

Importance of AM uncertainties by stakeholder group (n = 103). The width of each bubble corresponds to the proportion of elicited uncertainties in the corresponding stakeholder group. The difference in mean score across stakeholder groups for each uncertainty category was tested using a one-way ANOVA (*P < 0.05, **P < 0.01, and ***P < 0.001).

Fig. 6.

Importance and prevalence of the four most common categories of risk and uncertainty arising from the implementation of AM as a climate change adaptation strategy in BC’s forests (n = 103). The width of each bubble corresponds to the proportion of elicited risks and uncertainties in all groups. The stakeholder groups were weighted to account for the different number of participants in each. Paired-samples Student’s t tests confirmed the significance (***P < 0.001) of the differences between mean scores that participants assigned to ecological risks and uncertainties, between risks and uncertainties related to AM science, and between climate/weather risks and uncertainties. A, χ² test also confirmed the significance of the differences in prevalence across categories [X² (3, n = 559) = 110.294, P < 0.001].

Fig. 7.

Overall levels of support and opposition for six proposed reforestation strategies before and after deliberation (n = 103). Participants scored each option on an 11-point scale; the endpoints and midpoint of the scale were labeled “Strongly oppose (−5),” “Neutral (0),” and “Strongly support (+5).” Each pair of bars shows participants’ predeliberation (Left) and postdeliberation (Right) answers. *AM outside of natural range was the only strategy with a statistically significant difference between pre- and postdeliberation support in paired-samples Student’s t tests [t(101) = 2.303, P = 0.023].

Fig. 8.

Mean support for six proposed reforestation strategies by stakeholder group (n = 103). The difference in support across stakeholder groups for each strategy was tested using a one-way ANOVA (*P < 0.05, **P < 0.01, and ***P < 0.001).