Quantifying trade-offs between ecological gains, economic costs, and landowners’ preferences in boreal mire protection

Private land often encompasses biodiversity features of high conservation value, but its protection is not straightforward. Commonly, landowners’ perspectives are rightfully allowed to influence conservation actions. This unlikely comes without consequences on biodiversity or other aspects such as economic considerations, but these consequences are rarely quantitatively considered in decision-making. In the context of boreal mire protection in Finland, we report how acknowledging landowners’ resistance to protection changes the combination of mires selected to conservation compared to ignoring landowners’ opinions. Using spatial prioritization, we quantify trade-offs arising between the amount of landowners’ resistance, protected biodiversity, and financial costs in different conservation scenarios. Results show that the trade-offs cannot be fully avoided. Nevertheless, we show that the systematic examination of the trade-offs opens up options to alleviate them. This can promote the evaluation of different conservation policy outcomes, enabling better-informed conservation decisions and more effective and socially sustainable allocation of conservation resources. Supplementary Information The online version contains supplementary material available at 10.1007/s13280-021-01530-0.

. Province-specific information of landowners' preferences concerning protection of their land in the processed survey data.

Extrapolation of the survey data
Altogether 892 candidate mires were excluded from the survey and the data was deficient for 57 included candidate mires due to lacking survey answers (Table S1). Due to the shortages, we extrapolated citizen respondents' resistance to protection to cover mires lacking the observed data.
Whether citizen landowners would voluntarily protect their land or resist protection may depend on their relationship to regional advisors and authors (Salomaa et al. 2016). Presumably, landowners living in the same region have more parallel attitudes towards conservation than randomly picked landowners do. Provinces included to the survey had different levels of resistance (Table S1). Therefore, for the mires of such provinces where at least part of the mires were included to the survey (provinces 1-10 in Fig. S1 and Table S1), we extrapolated citizen landowners' resistance to protection based on the observed survey data. We calculated average resistance for each mire of which we had observed resistance data. For the mires lacking the observed data, we extrapolated the resistance randomly from the resistance distribution of the mires within the province in question. For the provinces that were totally excluded from the survey (provinces 11-13 in Fig. S1 and Table S1), we repeated the same procedure, but used the observed average resistance distribution of all the provinces included to the survey. We did randomization by Shuffle tool of Microsoft Excel 2016.

Correlations in the data
For background knowledge, we tested correlations of the candidate mires' area, price, resistance, and conservation priority using two-way Pearson correlation (Table S1). While preparing the analysis in phases, with respect to the used data and other considerations (see e.g. Kareksela et al. 2013), we also constructed an analysis version with all the presented biodiversity features included, but without connectivity, costs, and resistance considered. We used the rank order of the candidate mires of this analysis version as a variable for conservation priority.
Area and cost of the candidate mires correlated negatively likely because large mires are commonly treeless and therefore their acquire costs are lower than those of small mires (Table S1).
Conservation priority and costs of the candidate mires correlated positively. This is intuitive as more fertile mires have higher tree growth, higher species richness, and they consist of more threatened habitat types. Area and conservation priority of the candidate mires correlated negatively, likely because large mires always include both highly and less valuable biodiversity features, so the average conservation value in relation to area is lower on large than on small mires. Landowners' resistance to protection did not correlate with any variable.

Weighing of features on local and national scale (from Kareksela et al. 2020)
Weights for the features inside feature groups (e.g. mire complexes, mire habitats, threatened mosses and vascular plants, birds, streams and bonds) were following the features' Red List status classifications (Raunio et al. 2008, Rassi et al. 2010 and the between biodiversity feature group importance was defined together with the experts in the working group (Tables S1 and S2). For the features lacking Red List status, the weight was based on their relative importance according to the experts' opinion. The expert working group comprised 14 stakeholders and experts, including experts of mire ecology, land-use planners, conservation scientists, and representatives of the environmental and forestry administration, the land owner's association and conservation NGOs.  3  3  3  3  3  3  -3  3  -Emberiza rustica  3  3  3  3  3  3  -3  3  -Falco peregrinus  3  3  3  3  3  3  -3  3  -Phalaropus lobatus  3  3  3  3  3  3  -3    This kind of a more balanced spatial coverage of the protected area network in Finland was also supporting the goals of the CMPP.
The planning area was divided into 9 regions or administrative units according to forest vegetation zones. Using the method required us to calculate relative weights for each administrative unit by dividing each unit's number of cells by the total number of all units' cells (Table S3). The weights were used to ensure a balanced solution over the whole planning area (Moilanen et al. 2014). In addition to the national-scale weights of biodiversity features, we gave regional weights for biodiversity features in each administrative unit according to their regional Red List statuses (Table   S1). In the case of features unclassified in the habitat Red List, we used an expert opinion. We verified the balance between regional and national scale rarity of features by iterating the analysis and checking the biodiversity features' performance curves, which showed that local rarity was affected as planned without overly compromising the national scale performance (i.e. emphasis on nationally threatened habitats and species).

Hierarchical masks
When prioritizing Obligatory and Balancing scenarios, we used a two-phase mask that removed first all the candidate mires and then the existing protected mires (for more information about masks, see e.g. Kremen et al. 2008;Moilanen et al. 2014). When prioritizing Voluntary scenario, we used a three-phase hierarchical mask to force the exclusion of the mires where even a single landowner resisted protection. The mask determined the removal order of the mires: first the resistance-free candidate mires, then the candidate mires having resistance, and finally the existing protected mires.

Effect of resistance in Balancing scenario
When prioritizing Balancing scenario, we included landowners' resistance to protection as a feature layer, where the cell values were set as zero in the existing protected mires and between 0 and 1 in the candidate mires, according to their resistance proportion. To make the prioritization avoid resisted mires, we set a minus weight for the resistance layer . We iterated the analysis with weights of 0, -20, -50 and -100 for the resistance to investigate the relative differences of different weights and resulting trade-offs. Prioritization curves for resistance showed that using the weight of -100 would have been almost equivalent to the total exclusion of all resisted mires (Fig. S2). Instead, the weight of -50 effectively excluded resisted mires from the top fraction, still allowing valuable biodiversity features located in resisted mires to prevent all the exclusions. On the other hand, biodiversity representation produced by the weight of -20 differed only little from that produced by the weight of -50, but caused almost double resistance. Therefore, we chose the weight of -50 to serve as a minus weight for the resistance layer in Balancing scenario. Figure S2. Average representation of biodiversity features (green curve), landowners' total resistance (red curve), and costs of conservation (blue curve) for each prioritization testing the effect of different minus weights of resistance to protection. Small dotted lines represent zero weighting, medium dotted lines weight of -20, dash lines weight of -50, and solid lines weight of -100.