Model evaluation metrics play a critical role in the selection of adequate species distribution models for conservation and for any application of species distribution modelling (SDM) in general. The responses of these metrics to modelling conditions, however, are rarely taken into account. This leads to inadequate model selection, downstream analyses and uniformed decisions. To aid modellers in critically assessing modelling conditions when choosing and interpreting model evaluation metrics, we analysed the responses of the True Skill Statistic (TSS) under a variety of presence-background modelling conditions using purely theoretical scenarios. We then compared these responses with those of two evaluation metrics commonly applied in the field of meteorology which have potential for use in SDM: the Odds Ratio Skill Score (ORSS) and the Symmetric Extremal Dependence Index (SEDI). We demonstrate that (1) large cell number totals in the confusion matrix, which is strongly biased towards ‘true’ absences in presence-background SDM and (2) low prevalence both compromise model evaluation with TSS. This is since (1) TSS fails to differentiate useful from random models at extreme prevalence levels if the confusion matrix cell number total exceeds ~30,000 cells and (2) TSS converges to hit rate (sensitivity) when prevalence is lower than ~2.5%. We conclude that SEDI is optimal for most presence-background SDM initiatives. Further, ORSS may provide a better alternative if absence data are available or if equal error weighting is strictly required.

Though agricultural landscape biodiversity and ecosystem service (ES) conservation is crucial to sustainability, agricultural land is often underrepresented in ES studies, while cultural ES associated with agricultural land is often limited to aesthetic and tourism recreation value only. This study mapped 7 nonmaterial-intangible cultural ES (NICE) valuations of 34 rural farmers in western Taiwan using the Social Values for Ecosystem Services (SolVES) methodology, to show the effect of farming practices on NICE valuations. However, rather than a direct causal relationship between the environmental characteristics that underpin ES, and respondents’ ES valuations, we found that environmental data is not explanatory enough for causality within a socio-ecological production landscape where one type of land cover type (a micro mosaic of agricultural land cover) predominates. To compensate, we used a place-based approach with Google Maps data to create context-specific data to inform our assessment of NICE valuations. Based on 338 mapped points of 7 NICE valuations distributed among 6 areas within the landscape, we compared 2 groups of farmers and found that farmers’ valuations about their landscape were better understood when accounting for both the landscape’s cultural places and environmental characteristics, rather than environmental characteristics alone. Further, farmers’ experience and knowledge influenced their NICE valuations such that farm areas were found to be sources of multiple NICE benefits demonstrating that farming practices may influence ES valuation in general.

Local farmer knowledge is key to sustainable agriculture when organic farming promotes biodiversity conservation. Yet, farmers may not recognize ecosystem service (ES) benefits within their agricultural landscape. Surveys were administered to 113 farmers, and the opinions of 58 respondents toward organic farming were analyzed to identify influential variables when deciding to farm organically. We classified responses by geographic category within a socio-economic production landscape (SEPL), and by social influence categories. With principal component analysis (PCA), a two-scale, four-phased analysis was conducted. Coastal farmers (n = 22) were the most positive towards organic farming trends due to consumer demand. Plains farmers (n = 18) were highly interested in future opportunities for achieving consumer health and food safety objectives. Mountain farmers (n = 18) perceived the most organic transitioning barriers overall, namely irrigation. In all three geographic categories, farming decisions were not primarily related to biodiversity conservation or ES management, but rather to farming community patterns, consumer feedback, and a lack of barriers. Further, farmer opinions toward organic practices were more influenced by their life experiences than by school-taught concepts. Since no previous studies have assessed the knowledge, values, and opinions on organic farming of Taiwan’s west coast farmers from an ES perspective, the proposed approach both identifies farmers’ knowledge and opinions, and verifies a satoyama landscape with PCA results for informed decision making.