DataSheet_1_A methodological approach to identify priority zones for monitoring and assessment of wild bee species under climate change.zip

Climate change is affecting wild populations worldwide, and assessing the impacts on these populations is essential for effective conservation planning. The integration of advanced analytical techniques holds promise in furnishing detailed, spatially explicit information on climate change impacts on wild populations, providing fine-grained metrics on current environmental quality levels and trends of changes induced by estimated climate change scenarios. Here, we propose a framework that integrates three advanced approaches aiming to designate the most representative zones for long-term monitoring, considering different scenarios of climate change: Species Distribution Modeling (SDM), Geospatial Principal Component Analysis (GPCA) and Generalized Procrustes Analysis (GPA). We tested our framework with a climatically sensible Neotropical stingless bee species as study case, Melipona (Melikerria) fasciculata Smith, 1854. We used the SDM to determine the climatically persistent suitable areas for species, i.e. areas where the climate is suitable for species today and in all future scenarios considered. By using a GPCA as a zoning approach, we sliced the persistent suitable area into belts based on the variability of extremes and averages of meaningful climate variables. Subsequently, we measured, analyzed, and described the climatic variability and trends (toward future changes) in each belt by applying GPA approach. Our results showed that the framework adds significant analytical advantages for priority area selection for population monitoring. Most importantly, it allows a robust discrimination of areas where climate change will exert greater-to-lower impacts on the species. We showed that our results provide superior geospatial design, qualification, and quantification of climate change effects than currently used SDM-only approaches. These improvements increase assertiveness and precision in determining priority areas, reflecting in better decision-making for conservation and restoration.</p

Mean potential shift in the pollinator occurrence probability related to projected climate change for 2050 in the Brazilian municipalities where the 13 analyzed crops are produced.

<p>Values vary from -1 (decrease of 100% in pollinator occurrence probability; red to yellow) to +1 (increase of 100%; green to blue). Blank areas correspond to the municipalities where there is no production of the analyzed crops.</p

Projected climate change threatens pollinators and crop production in Brazil - Fig 2

<p>A) Frequency of municipalities that will face potential negative and positive shifts in pollinator occurrence probability considering the gross domestic product (GDP); B) the percentage of the production of the analyzed crops in the total GDP (acerola was not included due to the lack of data); and C) population.</p

Potential shift in the pollinator occurrence probability related to projected climate change for 2050 in the Brazilian municipalities where each crop is produced.

<p>Values vary from -1 (decrease of 100% in pollinator occurrence probability; red to yellow) to +1 (increase of 100%; green to blue). Crops have different levels of dependence on animal pollination (according to Giannini et al. 2015b). The list of pollinators for each crop can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182274#pone.0182274.s001" target="_blank">S1 Table</a>.</p

Shifts for each crop analyzed considering A) the decrease and B) the increase in the pollinator occurrence probability and the number of municipalities potentially affected (scientific name of each crop can be found in S1 Table).

<p>Shifts for each crop analyzed considering A) the decrease and B) the increase in the pollinator occurrence probability and the number of municipalities potentially affected (scientific name of each crop can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182274#pone.0182274.s001" target="_blank">S1 Table</a>).</p

Acai flower-visitor richness (sum data)

Dataset used to test effects of anthropogenic disturbance on pollinator richness and similarity (Jaccard's index). Data calculated from sum data (all surveys per site, n = 18) and presented in table 2 and figure 3 in main text (see README.txt for more details)

Acai flower-visitor surveys

Dataset for repeat observations of flower-visiting insect activity on acai palm (Euterpe oleracea) in the Amazon river delta. A total of 322 observations (10 minutes visual survey + 10 minutes for species collection. Data from observations and collections were combined - thus, where an insect was collected but not observed a value of 0.1 was attributed. Data was analysed using generalised mixed effects models and presented in Table 1, Table 2, and Figure 3 in the main manuscript (see README.txt for more details)

Acai fruit production data

Fruit set on marked inflorescences - including data from pollinator-excluded, open and hand-pollinated inflorescences (see README.txt for more details