Climate change and the availability of wild food plants in the semiarid

A. Magalhães & D. Vartanian

University of São Paulo

2024-10-23

Hi there! 👋

In this presentation, we will delve into the insightful article by Silva et al. (2024) titled: Climate change may alter the availability of wild food plants in the Brazilian semiarid. This research opens up important discussions about the future of essential resources under the pressures of climate change.

We will cover the following topics:

1. Introduction
2. Methods
3. Results & Discussion
4. Conclusion

1. Introduction
2. Methods
3. Results
4. Discussion

URL: https://link.springer.com/article/10.1007/s10113-024-02250-3

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

The authors

Amanda S. S. da Silva

• Lattes
• UFPB, UFRN, UFRPE
• LECEB/CECA/UFAL
• PPGEtno

Xavier Arnan Viadiu

• Lattes
• UPE
• LECEB/CECA/UFAL
• PPGEtno

Patrícia M. de Medeiros

• Lattes
• UFAL
• LECEB/CECA/UFAL
• PPGEtno

Amanda Stefanie Sérgio da Silva

Xavier Arnan Viadiu

Patrícia Muniz de Medeiros

Introduction

Question

What will be the future availability of nutritionally and economically important wild food plants species in the Brazilian semiarid?

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 (Silva et al., 2024).

The effects of climate change on productive activities may lead to reductions in global agricultural production and, consequently, food availability (Zhu et al. 2022) (Silva et al., 2024).

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) (Silva et al., 2024).

Wild food plants

Wild food plants (WFPs) are defined as plant species that grow spontaneously in natural or seminatural ecosystems, have self-sustaining populations, and are collected for human consumption as food or drink in rural and urban environments (Heywood, 1999; Reyes-García et al., 2015; Shackleton & de Vos, 2022).

A Goiaba-do-morro (Psidium guineense).

B Jenipapo (American genipa).

C Taioba/Costela-de-adão/Orelha-de-elefante (Xanthosoma sagittifolium).

D Cará-roxo (Dioscorea trifida).

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. (Medeiros et al., 2021).

The Brazilian Semiarid

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 (Silva et al., 2024).

Shared Socioeconomic Pathways

The SSPs are based on five narratives describing alternative socio-economic developments. Their main purpose is to provide an internally consistent logic of the main causal relationships, including a description of trends that are traditionally difficult to capture by models (Riahi et al., 2017).

SSP1: Sustainability – Taking the Green Road

Low challenges to mitigation and adaptation.

SSP2: Middle of the Road

Medium challenges to mitigation and adaptation.

SSP3: Regional Rivalry – A Rocky Road

High challenges to mitigation and adaptation.

SSP4: Inequality – A Road Divided

Low challenges to mitigation, high challenges to adaptation.

SSP5: Fossil-fueled Development ― Taking the Highway

High challenges to mitigation, low challenges to adaptation.

Caminhos socioeconômicos compartilhados.

• Color palette 1: Viridis Inferno (8 categories).

• Color palette 2: Our World in Data.

Shared Socioeconomic Pathways

See also Intergovernmental Panel on Climate Change (2021, p. 571, fig. 4.2).

Shared Socioeconomic Pathways

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. (Nazarenko et al., 2022).

Methods

Selection and identification of species

Sources:

1. IBGE Agricultural Census: “plant extraction”, “semiarid”, and “family farming” (Instituto Brasileiro de Geografia e Estatística, 2022).
2. Plants with potential for human consumption as food (Jacob et al., 2020) (systematic review).

All species that were cited by three or more articles present in Jacob et al. (2020). 11 species in the first database and 17 species in the second.

All 27 selected species are native to Brazil, 11 are endemic to Brazil, and 3 are endemic to Brazil and restricted to Caatinga.

Selection of occurrence records

Occurrence records of the species in the Global Biodiversity Information Facility (GBIF) database.

Selected records with geographical coordinates and dates from 1970 to 2021 (collected in January 2022), to coincide with the period of the bioclimatic variables (Silva et al., 2024).

Distribution of the wild food plant (WFP) species studied in the Brazilian semiarid region (Silva et al., 2024).

Selection of env. predictor variables

Climate data from the WorldClim 2.1 database, which compiles interpolated data from weather stations worldwide (Fick & Hijmans, 2017; Harris et al., 2020).

Future projections are informed by multiple climate models and various SSPs, providing data at several spatial resolutions for a detailed representation of climate variables.

Projected monthly average maximum temperatures (°C) for June 2021–2040, based on the global climate model (GCM) ACCESS-CM2.

Dataseries:

1. Historical climate data (1970–2000).
2. Historical monthly weather data (1960-2018): Downscaled data from CRU-TS-4.06.
3. 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.

Selection of env. predictor variables

Soil data from the SoilGrids database, which is based on a global compilation of soil profile data (WoSIS) and environmental layers.

It takes as inputs soil observations from about 240,000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology (Poggio et al., 2021).

Soil total nitrogen concentration (in cg/kg) at a depth of 0-5 cm for each 5000 m cell, based on the latest release of the SoilGrids database (May 2020).

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

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 (WorldClim, n.d.).

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

Habitat suitability modeling

Response variable (Occurrences of plant species) \(+\) Predictor variables (Bioclimatic and soil variables) \(=\) Suitability map

80% training/20% testing

Habitat suitability modeling

Maxent (Maximum entropy) is an application for modelling species geographic distributions.

Data on occurrence \(+\) Environmental variables \(+\) Random background points \(=\) Predict where a species is likely to be found

Result: A probability map, where each location on the map is assigned a value that indicates the likelihood of the species living there.

Habitat suitability modeling

The Maxent algorithm compares the environmental conditions at presence points with background points and tries to identify a pattern.

Habitat suitability modeling

As a result, a map is obtained showing the probabilities of that species occurring in that location.

With the model ready, the test 20% can be used to evaluate the model.

This evaluation allows for the selection of the best models.

Habitat suitability map for Annona crassiflora (Marolo/Pinha-do-cerrado). Suitability surfaces are presented within the known extent of occurrence (EOO) and clipped by the Semiarid region. Blank areas represent locations where species have no known occurrence (extend beyond the EOO).

Habitat suitability modeling

After the model was generated, it was projected into the future under different scenarios.

Projections of the predictor variables for the future.

Habitat suitability map for Annona crassiflora (Marolo/Pinha-do-cerrado). Suitability surfaces are presented within the known extent of occurrence (EOO) and clipped by the Semiarid region. Blank areas represent locations where species have no known occurrence (extend beyond the EOO).

Geographical distribution

Based on the optimal sensitivity and specificity ― Threshold that best defines the ‘presence’ area (Binarization).

After that, the relative loss (RG) and relative gain (RL) of potential area were calculated.

Species richness and composition

A hexagonal grid of 0.5 degrees (in red, ~55.6 km) is used for delineating communities.

The gray map represents the spatial extent of the Brazilian Semiarid region. The blue dots represent the centroids of the hexagons.

Species number == Richness.

Species identity == Composition.

Species composition per grid.

Species richness and composition

The hex grid was superimposed on the geographical distribution polygons of each species, and species richness was estimated by the sum of the number of species found in each hexagon for each climate scenario (current and future).

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).

Species richness and composition

The hex grid was superimposed on the geographical distribution polygons of each species, and species richness was estimated by the sum of the number of species found in each hexagon for each climate scenario (current and future).

Species richness and composition

The hex grid was superimposed on the geographical distribution polygons of each species, and species richness was estimated by the sum of the number of species found in each hexagon for each climate scenario (current and future).

Species richness and composition

Spatial Beta Diversity quantifies the difference in species composition between a focal cell and the surrounding cells.

Temporal Beta Diversity quantifies the difference in species composition between a focal cell and the same cell in a future scenario.

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).

Bioclimatic variation

Projections of the predictor variables for the future.

Annual precipitation in the Brazilian Semiarid region for the current scenario (represented on a yellow-green scale) and the same but across four future climate change projections (represented on a red-white-blue scale). The current scenario considers the historical climate of the period 1970-2000. The projected climate changes are for the period 2041-2060

Results & Discussion

Variations in geographical dist.

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 (\(n = 27\)) in each future scenario; points represent outliers (Dipteryx alata Vogel). Nega- tive values indicate a reduction in distribution area, and positive values indicate an increase (Silva et al., 2024).

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.

Variations in geographical dist.

Lost area, gained area, and intersection area in the geographic distribution of Acrocomia aculeata (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.

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).

Variations in geographical dist.

Lost area, gained area, and intersection area in the geographic distribution of Dipteryx alata (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.

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).

Variations in species richness

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 (\(n = 430\)) in each future scenario. Negative values indicate reductions (red) and positive values indicate increases (green) (Silva et al., 2024).

Species number == richness. Species identity == composition.

Variations in species 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 (Silva et al., 2024).

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 (\(n = 430\)) in each future scenario. Negative values indicate reductions (red) and positive values indicate increases (green) (Silva et al., 2024).

Variations in species composition

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 (Silva et al., 2024).

Variations in species composition

The turnover component of Sørensen’s dissimilarity (ßsim) refers to the replacement of species from one location by different species from another location (Silva et al., 2024).

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 (Silva et al., 2024).

Variations in species composition

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 (Silva et al., 2024).

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 (Silva et al., 2024).

Conclusion

Question

What will be the future availability of nutritionally and economically important wild food plants species in the Brazilian semiarid?

Qual será a disponibilidade futura de espécies de plantas silvestres comestíveis, nutricional e economicamente importantes, no semiárido brasileiro?

Conclusion

The species with the highest projected losses in climatically suitable areas were those that contribute substantially to the income of local populations.

Some species will expand their range, being more tolerant to global changes, and can be used to replace this loss of food species.

In addition to climate change, other threats can contribute to reductions in the geographical distribution of species, such as changes in land use and unsustainable extraction.

Closing remarks

This presentation was created using the Quarto Publishing System. Code and materials are available on GitHub.

We would like to express our gratitude to Amanda Silva for her guidance and the opportunity to present this insightful work.

References

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Thank you!

Appendices

(AP) Run to the poles!

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.

(AP) Run to the poles!

(AP) Average temprature anomaly

(AP) Robustness

The order in complex systems is said to be robust because, being distributed and not centrally produced, it is stable under perturbations of the system (Ladyman et al., 2013).

(AP) Equilibrium states

Systems in a highly stable state (deep valley) have low potential energy, and considerable energy is required to move them out of this stable state. Systems in an unstable state (top of a hill) have high potential energy, and they require only a little additional energy to push them off the hill.

(AP) Leverage points

There are places within a complex system where a small shift in one thing can produce big changes in everything (D. Meadows, 1999).

Leverage points are points of power (D. H. Meadows, 2008).

Possible states, even if unlikely: by modelling such interactions, it can suggest that properties exist in the system that had not been noticed in the real world situation (Dodig-Crnkovic & Giovagnoli, 2013).