- Introduction
- Methods
- Results
- Discussion
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URL: https://link.springer.com/article/10.1007/s10113-024-02250-3
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URL: https://link.springer.com/journal/10113
The goal of Regional Environmental Change is to publish scientific research and opinion papers that improve our understanding of the extent of these changes, their causes, their impacts on people, and the options for society to respond. "Regional" refers to the full range of scales between local and global, including regions defined by natural criteria, such as watersheds and ecosystems, and those defined by human activities, such as urban areas and their hinterlands.
Qualis Periódicos 2017-2020: A2 (Biodiversidade+)
JCR 2023: 3.4
SJR 2023: 1.032
Amanda Stefanie Sérgio da Silva
Qual será a disponibilidade futura de espécies de plantas silvestres comestíveis, nutricional e economicamente importantes, no semiárido brasileiro?
This study aimed to estimate the future availability of nutritionally and economically important WFP species in the Brazilian semiarid and determine their spatiotemporal variation in future scenarios of climate change.
Here, our objective was to determine the spatiotemporal dynamics of WFPs in the Brazilian semiarid and evaluate their potential availability in future climate change scenarios [@silva2024].
The effects of climate change on productive activities may lead to reductions in global agricultural production and, consequently, food availability (Zhu et al. 2022) [@silva2024].
Plant communities may become more homogeneous or heterogeneous in terms of spatial distribution, owing to changes in the average size of distribution areas through the expansion or contraction of geographical ranges and the extinction or introduction of species with restricted distribution (Ochoa-Ochoa et al. 2012; Lima et al. 2019) [@silva2024].
Plantas Silvestres Comestíveis (PSC) or Plantas Alimentícias Silvestres (PAS).
Rich in nutrients.
Contributes to the diversification of food.
Serve as genetic resources for closely related crop species in breeding programs.
Fig 3. Wild food plants with greater potential for popularization, considering the average of the attributes accessed according to the perception of the residents of the rural settlement Dom Helder Câmara, in the municipality of Murici, state of Alagoas, northeastern Brazil. (A) Psidium guineense Sw.; (B) American genipa L.; (C) Xanthosoma sagittifolium (L.) Schott, and (D) Dioscorea trifida L.f. [@medeiros2021].
The Caatinga is the only phytogeographic domain exclusive to Brazil.
A more precise definition is given by the Köppen climate classification, which treats steppe climates (BSh and BSk) as intermediates between desert climates (BW) and humid climates (A, C, D) in ecological characteristics and agricultural potential https://en.wikipedia.org/wiki/Semi-arid_climate.
Fig. 1 Geographic location of the Brazilian semiarid region in the Neotropical realm, with limits highlighted in black on a phytogeographic map. The relative area (%) represents the proportion of each domain in relation to the total area of the semiarid region. Sources: Brazilian Institute of Geography and Statistics, Biomes of Brazil, 1:250,000, 2019, Development Superintendency of the North- east (SUDENE), Delimitation of the Semiarid, 2017 [@silva2024].
Caminhos socioeconômicos compartilhados.
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Color palette 1: Viridis Inferno (8 categories).
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Color palette 2: Our World in Data.
See also @ipcc2021[p. 571, fig. 4.2].
Figure 10. Surface air temperature differences (°C) for late 21st century 2081–2100. Minus 1995–2014 of the corresponding historical ensemble member for the non-interactive climate model. (a) SSP1-2.6; (b) SSP2-4.5; (c) SSP4-6.0; (d) SSP5-8.5. [@nazarenko2022].
Dataseries:
- Historical climate data (1970–2000).
- Historical monthly weather data (1960-2018): Downscaled data from CRU-TS-4.06.
- Future climate data (2041–2060) under four SSPs (SSP1 (2.6), SSP2 (4.5), SSP3 (7.0), and SSP5 (8.5)): includes downscaled climate projections from CMIP6 models.
11 soil property variables, 10 at four depths (0–5, 5–15, 15–30, and 30–60 cm), and 1 at a single depth (0–30 cm).
Variable examples: Total nitrogen (N); Soil pH; Proportion of clay particles; Proportion of sand particles; Organic carbon density.
See:
- https://www.isric.org/explore/soilgrids
- https://data.isric.org/geonetwork/srv/por/catalog.search#/metadata/6d86f10a-d898-4ba9-b41d-b11c767dde8b
- https://soilgrids.org
- https://www.isric.org/explore/soilgrids/faq-soilgrids
- https://www.isric.org/explore/soilgrids/soilgrids-access
Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year).
BIO1 = Annual Mean Temperature
BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
BIO3 = Isothermality (BIO2/BIO7) (×100)
BIO4 = Temperature Seasonality (standard deviation ×100)
BIO5 = Max Temperature of Warmest Month
BIO6 = Min Temperature of Coldest Month
BIO7 = Temperature Annual Range (BIO5-BIO6)
BIO8 = Mean Temperature of Wettest Quarter
BIO9 = Mean Temperature of Driest Quarter
BIO10 = Mean Temperature of Warmest Quarter
BIO11 = Mean Temperature of Coldest Quarter
BIO12 = Annual Precipitation
BIO13 = Precipitation of Wettest Month
BIO14 = Precipitation of Driest Month
BIO15 = Precipitation Seasonality (Coefficient of Variation)
BIO16 = Precipitation of Wettest Quarter
BIO17 = Precipitation of Driest Quarter
BIO18 = Precipitation of Warmest Quarter
BIO19 = Precipitation of Coldest Quarter
See: https://www.worldclim.org/data/bioclim.html
Fig. S8. Projected geographic distribution of wild food plants for the current scenario (1970-2000) within the extent of the Brazilian Semiarid region. The blue dots indicate occurrence records of each species within the semiarid region. Occurrence records were gathered from the Global Biodiversity Information Facility (GBIF, 2022).
Fig. S6. Representation of β diversity estimation in a grid. Spatial β diversity (A) was estimated by quantifying the average β diversity between each hexagon (focal cell in blue) and the six cells surrounding it (dashed blue line equivalent to a radius of 0.5 degrees, same resolution as the hexagon, ~55.6 km), for each climatic scenario (current and future). Temporal β diversity (B) was estimated by comparing the current species composition with the species composition in each future scenario (dashed blue line), considering the same hexagon (focal cell in blue).
Fig. 2 Relative balance in the geographical distribution area of wild food plants of the Brazilian semiarid in future climate change scenarios (2041–2060).
The relative balance (bars) indicates the balance between gain and loss in the distribution area of each species and is proportional to the current (1970–2000) distribution area. Boxplots show the median, quartiles, and standard error of the relative balance of all species (
Effects of climate change (2041–2060): Suitable areas for most wild food plants (WFPs) in the Brazilian semiarid region will shrink.
Economic impact: Species experiencing the greatest loss of suitable areas are important for local income, such as pequi, licuri, buriti, and mangaba, whose sales reached R$ 700.00 in 2016/2017.
Fig. S9. Lost area, gained area, and intersection area in the geographic distribution of each species (within the semiarid region) in the four future climate change projections (period 2041-2060). The lost area (in red) and gained area (in green) are relative to the current projected distribution area of the species. The intersection area (in grey) represents the distribution area maintained between current and future scenarios. The projections range from a more optimistic to a more pessimistic scenario regarding greenhouse gas emissions (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively).
Fig. S9. Lost area, gained area, and intersection area in the geographic distribution of each species (within the semiarid region) in the four future climate change projections (period 2041-2060). The lost area (in red) and gained area (in green) are relative to the current projected distribution area of the species. The intersection area (in grey) represents the distribution area maintained between current and future scenarios. The projections range from a more optimistic to a more pessimistic scenario regarding greenhouse gas emissions (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively).
Fig. 3 Species richness and spatial beta diversity of wild food plants (hexagons) in the Brazilian semiarid. a Species richness in the cur- rent scenario (1970–2000) and in the four future climate change scenarios (2041–2060). b Variation of species richness in the four future climate change scenarios (2041–2060). c Spatial beta diversity in the current scenario (1970–2000) and in the four future climate change scenarios (2041–2060). Spatial beta diversity quantifies the difference in species composition between a focal cell and its surrounding cells (0.5° radius). d Variation of spatial beta diversity in the four future climate change scenarios (2041–2060). Boxplots show the median, quartiles, and standard error of the variation (
Species number == richness. Species identity == composition.
Beta diversity estimates (spatial and temporal) were calculated using the Sørensen index (ßSor), which measures the dissimilarity in species composition between biological communities. ßSor values range from 0 to 1, and the closer to 1, the greater the dissimilarity between communities [@silva2024].
Fig. 3 Species richness and spatial beta diversity of wild food plants (hexagons) in the Brazilian semiarid. a Species richness in the cur- rent scenario (1970–2000) and in the four future climate change scenarios (2041–2060). b Variation of species richness in the four future climate change scenarios (2041–2060). c Spatial beta diversity in the current scenario (1970–2000) and in the four future climate change scenarios (2041–2060). Spatial beta diversity quantifies the difference in species composition between a focal cell and its surrounding cells (0.5° radius). d Variation of spatial beta diversity in the four future climate change scenarios (2041–2060). Boxplots show the median, quartiles, and standard error of the variation (
Fig. 4 Temporal beta diversity of wild food plants (hexagons) in the Brazilian semiarid. a Temporal beta diversity in the four future climate change scenarios (2041–2060). Temporal beta diversity quantifies the difference in species composition of the same focal cell at two different times (present and future). Temporal beta diversity was partitioned into its components, namely b turnover and c nestedness [@silva2024].
The turnover component of Sørensen’s dissimilarity (ßsim) refers to the replacement of species from one location by different species from another location [@silva2024].
Fig. 4 Temporal beta diversity of wild food plants (hexagons) in the Brazilian semiarid. a Temporal beta diversity in the four future climate change scenarios (2041–2060). Temporal beta diversity quantifies the difference in species composition of the same focal cell at two different times (present and future). Temporal beta diversity was partitioned into its components, namely b turnover and c nestedness [@silva2024].
The nestedness component (ßnes) implies species loss or gain at only one of the sites, where the poorer site is considered a subset of the richer site [@silva2024].
Fig. 4 Temporal beta diversity of wild food plants (hexagons) in the Brazilian semiarid. a Temporal beta diversity in the four future climate change scenarios (2041–2060). Temporal beta diversity quantifies the difference in species composition of the same focal cell at two different times (present and future). Temporal beta diversity was partitioned into its components, namely b turnover and c nestedness [@silva2024].
Qual será a disponibilidade futura de espécies de plantas silvestres comestíveis, nutricional e economicamente importantes, no semiárido brasileiro?
Figure SPM.3: Projected risks and impacts of climate change on natural and human systems at different global warming levels (GWLs) relative to 1850-1900 levels. Projected risks and impacts shown on the maps are based on outputs from different subsets of Earth system and impact models that were used to project each impact indicator without additional adaptation. WGII provides further assessment of the impacts on human and natural systems using these projections and additional lines of evidence. (b) Risks to human health as indicated by the days per year of population exposure to hyperthermic conditions that pose a risk of mortality from surface air temperature and humidity conditions for historical period (1991–2005) and at GWLs of 1.7°C–2.3°C (mean = 1.9°C; 13 climate models), 2.4°C–3.1°C (2.7°C; 16 climate models) and 4.2°C–5.4°C (4.7°C; 15 climate models). Interquartile ranges of GWLs by 2081–2100 under RCP2.6, RCP4.5 and RCP8.5. The presented index is consistent with common features found in many indices included within WGI and WGII assessments.
