# Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

Alexis Dzokom, Ezechiel Kodji

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Alexis Dzokom, Ezechiel Kodji. Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities. Sahel Nature Consulting Revue, In press, Heatwave Intensification in the Cameroonian Sahelian Zones, 08 (21), pp.1-37. hal-05429371

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Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

Scientific-Article Décembre 2025, Volume 08, Issue 21

Abstract

Recent extreme heatwaves in the Sahel, with maximum temperatures exceeding 45 °C, are exposing biodiversity, human populations, and agricultural systems to severe thermal stress. This article synthesizes recent climatic evidence demonstrating the intensification of heatwaves in the region. It proposes a methodological framework combining in situ observations, remote sensing, and modeling to estimate a Biodiversity Survival Index (BSI) under 35-45 °C temperature scenarios. The results indicate that operational vegetation cover standards for human-inhabited environments (defined through minimum thresholds and desirable target levels) can significantly reduce urban and peri-urban heat island effects and enhance overall resilience. Practical and policy-oriented recommendations are provided to strengthen community resilience, including agroforestry practices, water resource management, climate-sensitive urban planning, and early warning systems. The evidence further suggests that the intensity and frequency of recent heat extremes would not have occurred without anthropogenic climate forcing, and that substantial gains in local heat mitigation can be achieved through vegetation-based strategies and strengthened local governance.
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Sahel Nature Consulting Revue (ISSN : 1401-0006 March 2025)

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# Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

Alexis Dzokom¹, Ezechiel Kodji²

¹ Department of Geography, Faculty of Arts Letters and Social Sciences, University of Maroua, Cameroon

² Department of Environmental Sciences, National Advanced School of Engineering, University of Maroua, Cameroon

Email: dzokomalexis@gmail.com

## Abstract

Recent extreme heatwaves in the Sahel, with maximum temperatures exceeding 45 °C, are exposing biodiversity, human populations, and agricultural systems to severe thermal stress. This article synthesizes recent climatic evidence demonstrating the intensification of heatwaves in the region. It proposes a methodological framework combining in situ observations, remote sensing, and modeling to estimate a Biodiversity Survival Index (BSI) under 35–45 °C temperature scenarios. The results indicate that operational vegetation cover standards for human-inhabited environments (defined through minimum thresholds and desirable target levels) can significantly reduce urban and peri-urban heat island effects and enhance overall resilience. Practical and policy-oriented recommendations are provided to strengthen community resilience, including agroforestry practices, water resource management, climate-sensitive urban planning, and early warning systems. The evidence further suggests that the intensity and frequency of recent heat extremes would not have occurred without anthropogenic climate forcing, and that substantial gains in local heat mitigation can be achieved through vegetation-based strategies and strengthened local governance.

**Keywords:** Heatwaves; Cameroonian Sahel; biodiversity; survival index; vegetation cover; agroforestry; community resilience.

## Introduction

The Sahelian regions of Africa constitute a high-exposure frontier to thermal extremes due to their semi-arid climate, pronounced interannual rainfall variability, and the structural fragility of local socio-ecological systems, including rainfed agriculture, extensive livestock production, and weak infrastructure. In recent years, heatwave

episodes of increasing intensity and frequency have been recorded across the Sahel, with peak temperatures occasionally exceeding 45 °C and abnormally high nighttime temperatures that amplify cumulative heat stress on human populations, livestock, and wildlife. Attribution analyses conducted by international research teams indicate that these recent events (particularly the spring 2024 heatwaves) are extremely unlikely in
Heatwave Intensification in the Cameroonian Sahelian Zones:
Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

© October 2025, Volume 08, Issue 21 | https://sites.google.com/view/sahel-nature-consulting/accueil

the absence of anthropogenic climate
change forcing. These studies estimate that
human-induced warming has already
increased both the likelihood and intensity
of heatwave events in the region, rendering
certain present-day temperature conditions
virtually impossible without anthropogenic
emissions (World Weather Attribution,
2024; Dzokom, 2025a, b, c, d, e, f, g, h).

Rising temperature extremes have both
direct and indirect impacts on local
biodiversity. At the physiological scale,
each species is characterized by critical
thermal limits (CTmax, CTmin) and a thermal
safety margin, which together determine its
capacity to tolerate temperature increases
without losses in fitness, reproduction, or
survival. Syntheses in ecophysiology show
that the erosion of this margin under
increasing maximum temperatures elevates
the risk of physiological failure and local
population decline, particularly for
ectotherms and low-mobility species. When
aggregated at the community level, these
physiological responses translate into
differential vulnerability among biological
assemblages and potential reorganization of
local communities under 35-45 °C
temperature scenarios (Sunday et al., 2014;
Dzokom, 2024a, b, c).

Within this context, the extent and spatial
distribution of vegetation cover in human-
inhabited environments play a central role
as a local heat-mitigation factor and as a
foundation for ecological resilience. Trees,
wooded structures, and linear vegetation
elements alter the surface energy balance,
provide shading, enhance
evapotranspiration, and promote the
formation of cooler and more humid
microhabitats, thereby reducing land
surface and ambient air temperatures during
thermal extremes. Technical reviews and
best-practice guidelines (FAO, 2018;
Dzokom, 2022) indicate that increasing
urban and peri-urban tree cover is a cost-

effective and efficient measure for
mitigating urban heat island effects while
protecting human health and local
biodiversity. Empirical assessments further
suggest that substantial levels of tree
canopy cover (often cited in the range of
30-40% within urban matrices where
technically feasible) are associated with
measurable reductions in mean temperature
and heat-related mortality. However, these
targets must be carefully contextualized for
Sahelian environments, where water
availability, land-use competition, and land
tenure constraints limit implementation
(U.S. EPA, 2019; Vinagre et al., 2019).

Finally, community resilience capacity
strongly conditions the effectiveness of
vegetation-based solutions. Heat mitigation
and adaptation strategies—such as shade
tree planting, agroforestry systems, riparian
restoration, and the greening of public
spaces—are more successful when
supported by inclusive local governance
arrangements, secure land tenure, access to
finance, and participatory processes that
integrate local knowledge and gender
equity. Local initiatives in Cameroon
illustrate how women-led agroforestry can
simultaneously restore degraded land,
enhance food security, and strengthen
economic resilience, underscoring the
importance of integrated socio-ecological
approaches (Reuters, 2025).

Building on these insights, there is a clear
need to develop operational indicators for
assessing vulnerability and resilience, such
as a **Biodiversity Survival Index (BSI)**
that integrates thermal safety margins,
vegetation cover, water availability, and
habitat connectivity and to define
vegetation cover standards tailored to
inhabited Sahelian contexts. These
standards must reconcile thermal objectives
(heat island reduction), hydrological
constraints, and local socio-economic
realities, and be embedded within broader
Heatwave Intensification in the Cameroonian Sahelian Zones:
Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

© October 2025, Volume 08, Issue 21 | https://sites.google.com/view/sahel-nature-consulting/accueil

strategies for community capacity building
and territorial planning. The following
sections detail the scientific rationale for the
BSI, the proposed operational canopy
thresholds for inhabited environments, the
methodology for their assessment using
remote sensing and field data, and pathways
for institutional and community-level
integration in the Cameroonian Sahel.

# 1. Study Design and Study Area

The study was conducted in selected Sahelian localities of Cameroon (Diamaré, Mayo-Kani, Mayo-Tsanaga, and Bénoué), chosen to represent a gradient of land-use and settlement intensity, ranging from dense urban areas to peri-urban zones and rural/agro-pastoral systems.

Materials and Methods

<figure><img src="image_12.png" /><figcaption>**Figure 1.** *Study Area*</figcaption></figure>

The methodological framework integrates
the following components:

(A) climate analyses and thermal
scenarios;

(B) vegetation cover mapping using
remote sensing and in situ
measurements;

(C) biological inventories (birds, pollinators, small mammals, vegetation) and thermal tolerance assays for key species;

(D) assessment of habitat connectivity
and fragmentation;

(E) socio-economic surveys to evaluate
community resilience.

The following sections provide a detailed
description of each methodological
component.

## 2. Climate Data and Construction of 35–45 °C Scenarios

**2.1 Data Sources and Datasets**

Climate reanalyses and satellite-derived
products, such as ERA5 (ECMWF),
# Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

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provide hourly time series of land surface temperature and key atmospheric variables, including air temperature, humidity, and radiation. These datasets are validated using local observations from the Cameroon National Meteorological Service (Météo-Cameroun). Attribution studies conducted by the World Weather Attribution (WWA) network (2024) are used to characterize extreme events, including the 2024 Sahelian heatwave, with temperature ranges between 35 and 45 °C.

## 2.2 Climate Indices and Thermal Scenarios

### 2.2.1 Annual Number of Days Exceeding 35 °C — NDays35

$$ \text{NDays35}_{\text{Y}} = \sum_{d \in \text{year Y}} 1 \{ T_{\text{max,d}} > 35 \} \quad (\text{Eq. 1}) $$

This equation computes, for a given year Y, the total number of days on which the daily maximum temperature exceeds 35 °C. Each day exceeding the threshold is assigned a value of 1; otherwise, it is assigned 0. The annual sum yields the climatic index NDays35, which serves as an indicator of extreme heatwave occurrence and persistence.

### 2.2.2 Degree-Days Above 35 °C — CDD35 (Cooling Degree Days above 35 °C)

Often referred to as cumulative degree-days above a specified threshold, this index quantifies the cumulative thermal load by summing the positive differences between the daily maximum temperature and the 35 °C threshold over a given period.

$$ \text{CDD35}_{\text{Y}} = \sum_{d \in \text{year Y}} \max \{0, T_{\text{max,d}} - 35\} \quad (\text{Eq. 2}) $$

(Unit: °C·days); **Note:** hourly temperatures were aggregated by summing the positive values of

$\max(0, T_h - 35)$ over hours, providing a finer temporal resolution.

### 2.2.3 Daily Maximum Temperature — Tmax

Simply defined as: $T_{\text{max,d}} = \max_{h \in \text{night}} T_{h,d}$ (Eq. 3)

When only daily mean temperatures are available, the use of averages to count threshold exceedances should be avoided; daily maximum temperature data should be preferred.

### 2.2.4 Nighttime Minimum Temperature — Tmin_night

Operational definition: $T_{\text{min_night,d}} = \max_{h \in \text{night}} T_{h,d}$ (Eq. 4)

with “nighttime” defined, for example, as 00:00–06:00 local time (or 22:00–06:00 depending on the adopted convention). When only daily Tmax/Tmin values are available, the daily minimum temperature (Tmin,d) should be used, and its correspondence to the nocturnal period should be verified.

### 2.2.5 Combined Humidity-Temperature Indices: Humidex and Heat Index

#### 2.2.5.1 Dew Point Temperature (Td) Calculation from Air Temperature (T) and Relative Humidity (RH) — Magnus-Tetens Formula

This formulation is used as an intermediate step for the computation of humidex and vapor pressure.

* **Commonly used constants:** ($a = 17.27$, $b = 237.7$ °C).

$$ \alpha = \frac{aT}{b + T} + \ln\left(\frac{RH}{100}\right) \quad (\text{Eq. 5}); $$
Heatwave Intensification in the Cameroonian Sahelian Zones:
Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

© October 2025, Volume 08, Issue 21 | https://sites.google.com/view/sahel-nature-consulting/accueil

$T_d = \frac{ba}{a + \alpha}$ (Eq. 6); with T and Td expressed in °C.

2.2.5.2 Humidex (Canadian Method)

*   **Calculation of vapor pressure**, either from saturation vapor pressure or directly from the dew point temperature (Td):

$$e = 6.11 \times \exp \left( 5417.7530 \left( \frac{1}{273.16} - \frac{1}{T_d + 273.16} \right) \right) \quad (\text{Eq. 7}) \quad (\text{with e in hPa})$$

*   **Humidex**:

$$\text{Humidex} = T + 0.5555 \times (e - 10.0) \quad (\text{Eq. 8})$$

(T in °C; Humidex expressed in perceived °C-equivalent units)

**Note:** simplified versions exist that use an alternative Arrhenius-type formula for e (vapor pressure).

2.2.5.3 Heat Index (NOAA, requires T in °F)

*   **Convert air temperature to °F:**
    $T_F = 1.8 \times T_C + 32$ (Eq. 9)

*   **NOAA formula:** approximately valid for high T_F and moderate to high relative humidity (RH):

$$\begin{aligned} \text{HI} = & -42.379 + 2.04901523 \times T_F + \\ & 10.14333127 \times \text{RH} - 0.22475541 \times T_F \times \text{RH} \\ & - 0.00683783 \times T_F^2 - \\ & 0.05481717 \times \text{RH}^2 + 0.00122874 \times T_F^2 \times \text{RH} \\ & + 0.00085282 T_F \times \text{RH}^2 - \\ & 0.00000199 \times T_F^2 \times \text{RH}^2 \end{aligned} \quad (\text{Eq. 10})$$

**Note:** RH in % and T_F in °F. Empirical adjustments are available for low RH or very high RH conditions (see NOAA) and should be applied as appropriate.

2.2.6 Construction of Thermal Scenarios
(Baseline 1991–2020 and Perturbed
+1/+2/+3 °C)

2.2.6.1 Reference “Static” Scenario
(Historical 1991–2020)

*   Extract daily time series of Tmax,d, Tmin,d, and RHd for the period 1991–2020.

*   Compute climate indices (NDays35, CDD35, etc.) for each year and derive climatological statistics: 1991–2020 mean, percentiles (p50, p90, p99), and interannual variability.

2.2.6.2 Perturbed Scenarios (Simple “Delta Change” Method)

*   For each day (d) in the reference climate, generate perturbed versions as follows:

$$T'_{\text{max,d}} = T_{\text{max,d}} + \Delta T \quad (\text{Eq. 11}); \quad \Delta T \in \{+1, +2, +3\}^{\circ\text{C}};$$

$$T'_{\text{min,d}} = T_{\text{min,d}} + \Delta T \quad (\text{Eq. 12})$$

*   Keep relative humidity (RH) unchanged, or adjust specific humidity if absolute moisture preservation is desired (see variants below).

2.2.7 Extraction of Extreme Subsets
(Thresholds 35, 40, 45 °C) — Frequency
and Duration

2.2.7.1 Frequency (per year)

$$\text{Freq } y, \text{th} = \sum_{d \in y} 1\{T_{\text{max,d}} > \text{th}\} \quad (\text{Eq. 13}). \text{ where th } \in \{35, 40, 45\}.$$
# Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

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## 2.2.7.2 Duration of Heatwave Events (Consecutive Days)

is applied to distinguish major land-use and land-cover classes.

* Identify consecutive sequences of days where $T_{max,d} > threshold (th)$.

* For each event i in year y, compute the event duration $L_i$ (in days). Example statistics:

* $L_{max,y} = max_i L_i$ (annual maximum duration)

* $L_{avg,y} = mean_i L_i$ (average duration)

* Distribution of $L_i$ values

## 2.3 Validation and Uncertainty

Reanalysis data are validated against local observational series (bias tests, RMSE, correlation) and general confidence intervals are calculated for extreme indices. Seasonal biases and spatial resolution are considered in the interpretation (O'Connor et al., 2019).

## 3. Vegetation Cover Mapping — Remote Sensing and In Situ Measurements

### 3.1 Data Products and Preprocessing

Sentinel-2 and Landsat 8/9 imagery are used to compute vegetation indices (NDVI, EVI) and the Fractional Vegetation Cover (FVC). After atmospheric preprocessing (Sen2Cor for Sentinel-2, LaSRC for Landsat), indices are extracted using Google Earth Engine or QGIS. LiDAR or radar data complement the assessment of canopy structure and cooling potential.

### 3.2 Derived Products and Cover Classes

Vegetation indices, including NDVI and FVC computed via the scaled NDVI method, are used to produce a binary canopy map (≥10 %, target 30–40 %). According to FAO (2020), this canopy mitigates urban heat island effects. A supervised classification (Random Forest)

#### (a) Vegetation Indices

#### NDVI (Normalized Difference Vegetation Index)

Classic formula (Rouse et al., 1974):

$$NDVI = \frac{\rho_{NIR} - \rho_{RED}}{\rho_{NIR} + \rho_{RED}} \quad (Eq. 14)$$

* $\rho_{NIR}$ = near-infrared reflectance

* $\rho_{RED}$ = red band reflectance

**Interpretation:** values close to +1 indicate dense vegetation; values near 0 indicate bare soil or built-up surfaces; negative values correspond to water bodies or clouds.

#### Fractional Vegetation Cover (FVC) using the “Scaled NDVI” Method

Proportional method (Carlson & Ripley, 1997; Gutman & Ignatov, 1998):

$$FVC = \left( \frac{NDVI - NDVI_{soil}}{NDVI_{veg} - NDVI_{soil}} \right)^2 \quad (Eq. 15)$$

* $NDVI_{soil}$: minimum NDVI value (bare soil)

* $NDVI_{veg}$: maximum NDVI value (dense vegetation)

* The exponent (2) accounts for the nonlinear relationship between cover and spectral index (Gutman & Ignatov, 1998). This approach is also referred to as the “scaled NDVI” method.

* $FVC = 0 \rightarrow$ absence of vegetation

* $FVC = 1 \rightarrow$ full vegetation cover

#### Binary Canopy Map

* Convert FVC or NDVI into a binary map (presence/absence) according to thresholds:
# Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

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* ≥10 % → presence of functional forest or tree cover (FAO threshold)

* 30–40 % → operational target for bioclimatic effectiveness in urban environments (urban heat island mitigation) (Konijnendijk et al., 2021; Wang et al., 2024; Kabisch et al., 2016)

## Simple binary formula:

$$ \text{Canopy}(x, y) = \begin{cases} 1 & \text{if FVC}(x, y) \ge 0.10 \\ 0 & \text{if FVC}(x, y) < 0.10 \end{cases} \quad (\text{Eq. 16}) $$

## (b) Thematic Land-Cover Classification

**Method:** Supervised classification using Random Forest (Breiman, 2001)

### Step 1: Training Dataset Construction

* Samples are extracted from high-resolution imagery (Sentinel-2, SPOT, or drone data) combined with field survey data.

### Step 2: Selection of Predictor Variables

* Spectral bands (red, NIR, SWIR)

* Derived indices (NDVI, EVI, moisture indices)

* Textural metrics (GLCM) or contextual variables (elevation, slope if available)

### Step 3: Training and Classification

* Random Forest = ensemble of N decision trees (N ≈ 500); each tree is trained on a bootstrapped subsample and a subset of predictor variables.

* Majority voting determines the assigned class:

$$ \hat{C}(x) = \arg \max_{c} \sum_{i=1}^{N} 1\{h_i(x) = c\} \quad (\text{Eq. 17}) \text{ where } h_i(x) \text{ is the prediction of tree } i \text{ for pixel } x. $$

## Proposed thematic classes (Sahel-adapted):

* **Built-up:** urban areas, villages, and bare/mineral surfaces

* **Cropland:** cultivated fields, irrigated crops

* **Shrubs / Hedgerows:** low, scattered woody vegetation

* **Isolated Trees / Woodlots:** tree alignments, orchards, reforestation areas

* **Riparian Vegetation:** riverine vegetation, often dense, playing a critical role in local microclimate

**Validation:** confusion matrix analysis (Congalton, 1991).

* **Overall Accuracy (OA);** $OA = \frac{\sum_{i=1}^{k} n_{ii}}{N}$ (Eq. 18)

* **Kappa ;** $k = \frac{OA - P_e}{1 - P_e}$, $P_e = \frac{\sum_{i=1}^{k} (n_i + n_{i+1})}{N^2}$ (Eq. 19)

## 3.3 Field Sampling for Validation

Using a stratified design (urban, peri-urban, rural), 100–200 plots of 20 × 20 m are established to measure canopy cover, tree density, diameter at breast height (DBH), height, dominant species, presence of hedgerows, linear woody strips, herbaceous vegetation, and litter cover. These data are used to validate and calibrate FVC estimates and canopy maps.

### 3.3.1.) Stratified Sampling Design

* **Stratification:** three main strata (urban, peri-urban, rural) to ensure spatial and contextual
# Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

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representativeness (Cochran, 1977; Särndal et al., 1992).

* **Number of plots:** 100–200, standardized size of 20 × 20 m (400 m²), following FAO recommendations for rapid forest surveys.

## Total sampled area:

$$A_{tot} = n \times a_{plots} \quad (\text{Eq. 20}); \text{ where } n = \text{number of plots}; a_{plot} = 400 \text{ m}^2.$$

### 3.3.2.) Measured Variables and Calculations

#### Canopy Cover Percentage (%)

Measured using a densitometer or zenithal photographic survey method (Jennings et al., 1999):

$$CC = \frac{\sum_{i=1}^{m} A_{c,i}}{A_{plots}} \times 100 \quad (\text{Eq. 21})$$

* $A_{(c,i)}$ = projected canopy area of tree i within the plot

* $A_{plot}$ = plot area (400 m²)

#### Tree Density (individuals per hectare)

$$D = \frac{N}{A_{plots}} \times 10 000 \quad (\text{Eq. 22})$$

* N = number of trees recorded in the plot

* Conversion to hectares (1 ha = 10,000 m²)

#### Diameter at Breast Height (DBH)

Measured at 1.3 m above ground for trees (FAO, 2004), at 0.85 m for shrubs, and at 0.3 m for herbaceous plants in Sahelian areas (adopted by Sahel Nature Consulting, 2020).

If circumference (C) is measured:

$$DAP = \frac{C}{\pi} \quad (\text{Eq. 23})$$

#### Tree Height (H)

* Measured using a clinometer, relascope, or laser rangefinder.

* If the angle (θ) is measured:

$$H = d \times \tan(\theta_{top}) - d \times \tan(\theta_{base}) \quad (\text{Eq. 24})$$

* **d** = horizontal distance from observer to tree

* **θtop, θbase** = angles to the top and base of the tree

#### Dominant Species and Diversity

**Shannon Diversity Index** (Shannon & Weaver, 1949):

$$H' = - \sum_{i=1}^{S} p_i \ln p_i \quad (\text{Eq. 25})$$

* **S** = total number of species

* **p** = proportion of individuals of species i

#### Presence of Hedgerows, Linear Woody Strips, and Herbaceous Vegetation

Binary qualitative variables (presence/absence) coded as:

$$V_j = \begin{cases} 1 & \text{If the presence of element j is observed:} \\ 0 & \text{Otherwise:} \end{cases} \quad (\text{Eq. 26})$$

This allows correlation with the Random Forest classification.

#### Ground/Litter Cover (litter, plant debris)

Percentage visually estimated or using quadrats (Daubenmire, 1959):

$$CM = \frac{\sum_{i=1}^{q} A_{cm,i}}{A_{plot}} \times 100 \quad (\text{Eq. 27})$$
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Human-Settled Environments, and Community Resilience Capacities

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Acm,i = area covered by litter/plant debris in
quadrat i.

3.3.3.) Validation and Calibration with
Remote Sensing

* Field vs. FVC/NDVI comparison:

○ Simple linear regression:

$$
FVC_{sat} = \alpha + \beta \times FVC_{Field} + \varepsilon \quad (\text{Eq. 28})
$$

*   **Coefficient of determination (R²)**
    to assess goodness of fit

*   **Bias and RMSE** calculated to
    quantify errors (Willmott, 1981;
    O'Connor et al., 2019)

$$
\text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\text{FVC}_{\text{sat},i} - \text{FVC}_{\text{Field},i})^2} \quad (\text{Eq. 29})
$$

4. Biological Inventories and Biodiversity
Indicators

4.1 Objectives and Target Groups

The studied groups include birds,
pollinators, terrestrial arthropods, small
mammals, and vegetation, assessed using
abundance indices, species richness, and
floristic cover. These taxa, exhibiting
contrasting ecophysiological strategies
under extreme heat, provide an integrated
view of ecosystem responses according to
standardized ecological protocols (Bibby et
al., 2000).

4.2 Methods by Taxon — Practical Protocols

*   **Birds** are surveyed via point counts and morning transects (stations of 250–300 m, 10 min each) with dual observers following Bibby et al. (2000).

* Pollinators are sampled using pan traps and visual transects following Westphal et al. (2020) and O'Connor et al. (2019).

*   **Soil arthropods** are collected with weekly pitfall traps, **small mammals** with Sherman traps, and **vegetation** described on plots (10 × 10 m for trees; 1 × 1 m for herbaceous layer).

4.3 Sampling Effort and Replication

In at least three pilot municipalities, 30–50 inventory points are established per municipality according to land-use stratification. Inventories are repeated during dry and wet seasons to capture seasonal variability in ecological and biological parameters.

4.4 Physiological Data and CTmax / Thermal
Safety Margin Assays

Secondary sources compile CTmax values or thermal thresholds from the literature for local or related species. These data, following Sunday et al. (2014) and Vinagre et al. (2018), are used to estimate thermal safety margins. Experimental assays follow the ramping method (Lutterschmidt & Hutchison, 1997) after acclimation, on 20–30 individuals per taxon, to calculate the **Thermal Safety Margin (TSM):**

$$
\text{TSM} = \{ \text{CT}_{\text{max}} - \text{T}_{\text{max, observed}} \} \quad (\text{Eq. 29}) \\
\text{using local climate series.}
$$

5. Construction of the Biodiversity
Survival Index (BSI / ISB)

5.1 Ratio and Components

The ISB is a composite index evaluating a
site's capacity to support biodiversity under
heatwave conditions (35-45 °C). It
combines:

1.  **Thermal Margin (TM):** mean
Thermal Safety Margin (CT<sub>max</sub> –
T<sub>max_local</sub>) for indicator species
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2. Structured Vegetation Cover (SVC): canopy/shade fraction (FVC or % canopy)

3. Local Water Availability (LWA): humidity, water bodies, retention capacity

4. Habitat Connectivity (CON): patch metrics, corridors, isolation computed via FRAGSTATS (Sunday et al., 2014; Terblanche et al., 2007; Dzokom, 2023a,b)

## 5.2 Normalization and Weighting

* Each component is normalized to a 0–1 scale (min–max scaling) across the study area.

* The ISB is then calculated as a weighted sum:

$$ \text{ISB} = w_{\text{MT}} \times \text{MT}_{\text{norm}} + w_{\text{CVS}} \text{CVS}_{\text{norm}} + w_{\text{DHL}} \text{DHL}_{\text{norm}} + w_{\text{CON}} \text{CON}_{\text{norm}} \quad (\text{Eq. 30}) $$

## 5.2 Weighting of Components

Weights ($w_i$) can initially be set equal (0.25) and subsequently adjusted via:

1. Sensitivity analysis,

2. Empirical correlations between components and abundance/persistence indicators, or

3. Delphi method with local experts (Vinagre et al., 2019).

## 5.3 Operational Thresholds (Examples)

* ISB > 0.7 indicates a high probability of maintaining biological communities under thermal stress.

* Values between 0.4–0.7 indicate moderate risk.

* ISB < 0.4 identifies priority areas for ecological restoration, reforestation, or landscape connectivity enhancement.

These thresholds serve as operational guidelines, adaptable to local and bioclimatic conditions (Gbode et al., 2023; Dzokom et al., 2024a,b,c).

# 6. Landscape Structure and Connectivity Analysis

## 6.1 Tools and Metrics

Functional connectivity is assessed using landscape metrics, patch analysis, and corridor modeling. ISB thresholds help prioritize areas for intervention: high (>0.7), moderate (0.4–0.7), low (<0.4).

## 6.2 Modeling Functional Corridors

Dispersal corridors are modeled using **least-cost path approaches** and **circuit theory (Circuitscape)**, based on resistance maps derived from land cover, hydrological flows, and anthropogenic barriers.

* Parameters are set according to the ecology of focal species and empirically calibrated.

* Outputs (optimal pathways, connectivity fluxes, movement probabilities) are used to evaluate functional connectivity and identify critical conservation areas, contributing to the **CON component** of landscape assessment (Bibby et al., 2000).

# 7. Socio-Economic Surveys and Community Resilience Index

## 7.1 Survey Design and Indicators

* Stratified sampling based on geographic zones and household activity types, with 200–400 households per municipality for ±5–7% precision.

* Surveys combine semi-structured questionnaires, focus group
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discussions, and key informant interviews.

* Indicators include water access, income, credit, land tenure, agroforestry, health, and early warning systems, forming a **Community Resilience Index (CRI)** adapted from the BRIC model (Cutter et al., 2014).

## 7.2 Construction of a Resilience Score

* Thematic sub-indices (social, economic, institutional, infrastructure, environmental) are normalized for consistent comparison.

* Sub-indices are aggregated via weighted sum or **Principal Component Analysis (PCA)** to produce a composite community resilience score (0–1).

* This score is cross-referenced with the **Biodiversity Survival Index (ISB)** and canopy fraction (% FVC) to identify areas requiring combined social, institutional, and ecological interventions.

This integrated approach links ecological and adaptive resilience, enhancing the effectiveness of management and territorial planning strategies under thermal stress.

### (a) Sub-indices to Consider

1. **Social (S):** community cohesion, local participation, social capital

2. **Economic (E):** income access, economic diversification, food security

3. **Institutional (I):** local governance, risk management plans, access to information

4. **Infrastructure (INF):** access to water, energy, roads, climate refuges

5. **Environmental (ENV):** access to green spaces, natural resource management

Each sub-index can be measured using questionnaires, statistical indicators, or spatial data (Cutter et al., 2008).

### (b) Normalization of Sub-indices

To make indicators comparable:

$$X_{\text{norm}} = \frac{X - X_{\text{min}}}{X_{\text{max}} - X_{\text{min}}} \quad (\text{Eq. 31})$$

where:

* **X** = observed value of the sub-index

* **Xmin**, **Xmax** = minimum and maximum possible or observed values

* **Xnorm** = normalized sub-index scaled between 0 and 1

### (c) Aggregation into a Composite Score

Two main approaches:

#### 7.2.1 Weighted Sum (Simple Additive Index)

$$ \begin{aligned} R_{\text{composite}} &= w_S \times S_{\text{norm}} + w_E \times \\ E_{\text{norm}} &= w_I \times I_{\text{norm}} + w_{\text{INF}} \times \\ \text{INF}_{\text{norm}} &= w_{\text{ENV}} \times \text{ENV}_{\text{norm}} \end{aligned} \quad (\text{Eq. 32}) $$

* **w**<sub>x</sub> = relative weights of sub-indices (often w<sub>x</sub> = 0.2 for equal weighting)

* **R**<sub>composite</sub> is normalized between 0 and 1 after calculation

#### 7.2.2 Principal Component Analysis (PCA)

PCA is applied to the matrix of normalized sub-indices:

$$Z = X \times V \quad (\text{Eq. 33})$$

**X** = matrix of normalized sub-indices by site
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* V = eigenvectors (loadings) from PCA

* The first principal component (PC1) is often used as the composite resilience score

## (d) Cross-referencing with ISB and Vegetation Cover

Priority areas are identified as: $Z_{priority}$= Sites where $ISB < ISB_{threshold}$ and $R_{composite} < R_{threshold}$ and $CVS < CVS_{target}$ (Eq. 34)

ISB = Biodiversity Survival Index

* $R_{composite}$ = normalized community resilience score

* CVS = Structured Vegetation Cover (% canopy)

These sites indicate where social, institutional, or infrastructure reinforcement should accompany ecological interventions.

## 8. Microclimate Mitigation Experiments (Pilot Plots)

### 8.1 Experimental Design

Pilot plots are established (n = 3 replications per intervention type) including:

* Shade tree plantings (locally adapted species)

* Hedgerows / linear woody strips

* Reflective and vegetated roofs (where feasible)

* Control plots without intervention

Plots are standardized (e.g., 0.25 ha).

### 8.2 Microclimatic Measurements

Automated sensors record air temperature ($T_{air}$), surface temperature ($T_{surface}$), and relative humidity (RH) every 10–15 minutes. Sensors are placed under canopy (1.5 m) and in open areas (2–5 m) to capture

microclimatic gradients. FDR sensors measure soil moisture and evapotranspiration, while pyranometers assess radiative fluxes. These integrated measurements allow detailed analysis of vegetation cover and landscape interventions on local thermal and hydric regulation.

### 8.3 Statistical Analyses

Effects of vegetation cover and interventions are analyzed using **generalized linear mixed models (GLMMs)** with site and replication as random effects. Dependent variables include $\Delta T_{air}$, $\Delta T_{surface}$, and RH variations. Tukey post-hoc tests compare modalities, while shade effects are assessed along distance gradients to quantify microclimatic changes, providing robust estimates of local impacts.

## 9. Data Processing, Validation, and Reproducibility

### 9.1 Software and Pipelines

* Spatial analyses use **Google Earth Engine** for image preprocessing and large-scale index computation.

* **QGIS** or **ArcGIS** are used for local mapping and visualization.

* Statistical analyses are performed in **R** (packages: raster, sf, landscapemetrics, lme4), with landscape metrics via **FRAGSTATS**.

* Physiological and field data are stored in **PostgreSQL/PostGIS**, exportable as CSV, with **ISO 19115** metadata ensuring traceability, reproducibility, and full documentation.

### 9.2 Statistical Analysis

* Descriptive statistics (means, medians, distributions) and
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multivariate analyses (PCA, CCA)
explore relationships between
biological communities and thermal
/ vegetation gradients.

* Linear mixed models estimate fixed effects (habitat type, % canopy, Tmax) and random effects (site, season).

* Composite indices are validated via bootstrap and cross-validation to ensure reliability and reproducibility for ecological applications and adaptive management.

10. Ethical Considerations and Community Engagement

Fieldwork and non-destructive sampling
are conducted with prefectural and
municipal permits, adhering to ethical
standards for animal handling (capture-
release, stress minimization).

A participatory approach engages local committees in **co-designing plots**, **sharing results**, and **training activities**, enhancing local ownership, environmental awareness, and intervention sustainability while integrating local knowledge into ecosystem management.

11. Anticipated Methodological Limitations

* Heterogeneity of local meteorological data may affect reanalysis calibration.

* Methodological variability in CTmax measurements (heating rate, endpoint criteria) limits comparability across studies, emphasizing standardized protocols

and rigorous documentation
(Terblanche et al., 2007).

* Logistical constraints may restrict access to LiDAR or high-resolution imagery; in such cases, **FVC** can be estimated from Sentinel-2 or Landsat, with field calibrations to ensure accuracy and spatial representativeness (Bibby et al., 2000).

**Results and Discussion**

1. Characterization of Heatwave Increases (35–45 °C Scenarios)

Recent attribution analyses indicate that the Sahelian heatwaves (spring 2024), with local maxima exceeding 45 °C and exceptionally warm nights, would have been “virtually impossible” without anthropogenic forcing.

Studies combining observations and reanalyses indicate that current warming has substantially increased the likelihood and intensity of these extremes (World Weather Attribution, 2024).

The daily duration of temperatures exceeding 35 °C rises with heatwave intensity, while relative humidity decreases, exacerbating water stress for plants and perceived heat for wildlife and humans, as evaluated by indices such as **WBGT** (Wet Bulb Globe Temperature). These trends confirm the heightened vulnerability of Sahelian ecological systems and communities to increases in both frequency and intensity of thermal extremes (Sunday et al., 2014; Terblanche et al., 2007; Dzokom et al., 2025).
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<table><caption>**Table 1: Meteorological Characteristics of Heatwave Episodes (35–45 °C Scenarios)**</caption><thead><tr><th>Variable</th><th>Scenario 35-<br>37 °C (n=30)</th><th>Scenario 38-<br>40 °C (n=30)</th><th>Scenario 41-<br>43 °C (n=20)</th><th>Scenario 44-<br>45 °C (n=10)</th></tr></thead><tbody><tr><td>Daily maximum<br>temperature (°C)</td><td>36.1 ± 0.6</td><td>39.0 ± 0.7</td><td>42.0 ± 0.9</td><td>44.5 ± 0.4</td></tr><tr><td>Nighttime minimum<br>temperature (°C)</td><td>25.3 ± 1.2</td><td>26.8 ± 1.3</td><td>28.2 ± 1.1</td><td>29.1 ± 0.9</td></tr><tr><td>Daily duration >35 °C<br>(h)</td><td>4.2 ± 1.3</td><td>7.6 ± 1.5</td><td>11.8 ± 2.1</td><td>14.5 ± 1.0</td></tr><tr><td>Relative humidity (%)</td><td>28.5 ± 6.8</td><td>22.0 ± 5.5</td><td>18.2 ± 4.9</td><td>15.0 ± 4.1</td></tr><tr><td>Soil surface<br>temperature (°C, 1 cm)</td><td>46.2 ± 2.5</td><td>53.0 ± 3.1</td><td>59.8 ± 3.6</td><td>64.0 ± 4.0</td></tr><tr><td>Wet-Bulb Globe<br>Temperature (WBGT,<br>°C)</td><td>28.5 ± 1.2</td><td>31.8 ± 1.5</td><td>34.7 ± 1.8</td><td>36.6 ± 1.3</td></tr></tbody></table>

Vegetation cover is a **key driver of resilience**, as trees reduce temperatures through shading and evapotranspiration. Meta-analyses and operational guidelines recommend approximately **30 % canopy cover per neighborhood** for significant benefits to thermal comfort and public health. Effectiveness depends on **drought-adapted species, hydraulic resilience, strategic tree placement, canopy structure (LAI)**, and local urban characteristics, requiring integrated spatial and ecological planning (Sunday et al., 2014; Terblanche et al., 2007).

Maintaining or restoring **vegetation patches** (corridors, shade trees, wetlands) increases **microclimatic heterogeneity** and provides **thermal refuges for wildlife**. Using poorly adapted species with high evapotranspiration without sufficient water can exacerbate hydric stress; therefore, **native or xerophytic species** with high albedo and thick leaves are preferred to optimize microclimate and ecological resilience while minimizing impacts on water and communities (O'Connor et al., 2019).

<figure><img src="image_13.png" /></figure>

Aggregated Measures by Biological Group (mean ± SD, n = 15 sites per group)

<caption>**Figure 2. Biodiversity Response Indicators**</caption>
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Operational **community resilience indices**
may combine:

a. maintenance of emergency
water reserves,

b. tree corridors ≥30 % canopy,

c. heat alert panels and public
shaded areas, and

d. monitoring of pollinators
and birds for early detection
of declines.

<table><caption>**Table 2.** Vegetation Cover Performance (Thermal Mitigation Standards; Mean Values, *n* = 25 Plots)</caption><thead><tr><th>Indicator / Cover</th><th>Basic Plots<br>(≤10 % canopy)</th><th>Canopy<br>15–25 %</th><th>Canopy 30–<br>40 % (target)</th><th>Canopy<br>>50 %</th></tr></thead><tbody><tr><td>Mean surface T° reduction<br>under canopy (°C)</td><td>0.6 ± 0.4</td><td>1.6 ± 0.6</td><td>3.4 ± 0.8</td><td>4.8 ± 1.0</td></tr><tr><td>Air T° reduction at 1.5 m<br>(°C)</td><td>0.4 ± 0.3</td><td>1.2 ± 0.5</td><td>2.8 ± 0.6</td><td>3.6 ± 0.9</td></tr><tr><td>Local RH increase (%)</td><td>1.8 ± 1.2</td><td>3.5 ± 1.3</td><td>5.6 ± 1.8</td><td>7.2 ± 2.1</td></tr><tr><td>ET (mm·day<sup>-1</sup>) under<br>canopy</td><td>0.9 ± 0.3</td><td>1.5 ± 0.4</td><td>2.2 ± 0.5</td><td>2.6 ± 0.6</td></tr><tr><td>WBGT perceived reduction<br>(°C)</td><td>0.5 ± 0.4</td><td>1.4 ± 0.6</td><td>2.7 ± 0.8</td><td>3.8 ± 1.1</td></tr><tr><td>Relative efficiency per %<br>canopy (ΔT° / % canopy)</td><td>0.06 ± 0.02</td><td>0.07 ± 0.02</td><td>0.09 ± 0.02</td><td>0.08 ± 0.02</td></tr></tbody></table>

Integrated actions **enhance adaptive capacity** to heatwaves, in line with international recommendations for urban and rural adaptation to thermal extremes (Vinagre et al., 2019; U.S. EPA, 2024).

## 2. Impact on Organism Thermal Safety Margins and Implications for the ISB

Ectotherms and some tropical endotherms have **narrow thermal safety margins**; during episodes >35–40 °C, reductions in the **Thermal Safety Margin (TSM)** increase the risk of **physiological failure and mortality**.

The **Biothermic Stability Index (ISB)** shows that areas with **high canopy and water features** maintain better thermal stability, whereas isolated urban zones remain highly vulnerable. Multivariate analyses confirm that **thermal margin determines exposure to extremes**, while

**vegetation cover modulates thermal stress** through shading and evapotranspiration, explaining the variable resilience of species and highlighting the importance of integrated **biothermic and landscape-based approaches**.

Pollinating insects exhibit the **lowest operational TSM**, with CTmax ≈ 40–42 °C. In simulations, increasing Tmax from 36 to 39 °C **reduces TSM by ~60 %** and significantly lowers the ISB (≈0.98 → 0.82). More extreme temperatures (42–44.5 °C) lead to **very low ISB values** (0.35 → 0.12), indicating a high risk of **ecological dysfunction**, particularly through **loss of local pollination services**. These results illustrate the **critical vulnerability of pollinators** to heatwaves in tropical and Sahelian areas, emphasizing the importance of preserving vegetation cover and thermal refuges to maintain ecological resilience (Sunday et al., 2014; Terblanche et al., 2007).
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Birds and mature trees have **initially wider thermal margins**, benefiting from **thermal inertia** and **behavioral or physiological compensation**. However, repeated extreme

events and elevated nighttime minima **reduce recovery capacity**, leading to progressive ISB decline under repeated 41–45 °C scenarios

<figure><img src="image_14.png" /></figure>

TSM Positive = margin before failure; TSM Negative = field conditions exceed mean CTmax (high probability of acute effects). (n = 20 samples per group; scenarios defined by mean habitat Tmax: 36.0, 39.0, 42.0, 44.5 °C)

<caption>**Figure 3. Thermal Safety Margins (TSM) and ISB by Biological Group**</caption>

This dynamic highlights that, **despite initially higher tolerance**, these organisms remain vulnerable to **cumulative heat stress**, and medium-term resilience strongly depends on **the frequency and duration of heatwaves** (Konijnendijk, 2023; Pörtner & Peck, 2017; Sunday et al., 2014).

**1. Low TSM in tropical/hot regions increases ectotherm vulnerability**

Multiple studies indicate that many ectotherms (insects, reptiles) live with **narrow thermal safety margins**; even moderate increases in Tmax can substantially reduce TSM, triggering **physiological or behavioral failures** (displacement, loss of diurnal activity), thereby reducing local ISB. This

mechanism explains simulations in which **pollinators exhibit the lowest ISB under scenarios ≥42 °C** (Sunday et al., 2014).

**2. Behavioral thermoregulation versus physiological limits**

Air temperature-based TSM estimates often **overestimate actual thermal safety**, as organisms exploit **microhabitats** (shade, cooler substrates) for thermoregulation (Sunday et al., 2014; Deutsch et al., 2008). In our simulations, variability (SD) is included to reflect this **micro-scale heterogeneity**. Nevertheless, in **Sahelian landscapes with sparse canopy cover**, behavioral thermoregulation is limited, increasing organism vulnerability (Sunday et al., 2014; Terblanche et al., 2007).
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<figure><img src="image_15.png" /></figure>

ISB Computation Using a Simple Logistic Function: $ISB \approx 1 / (1 + e^{(-k \times (TSM - \theta))})$ with $k = 0.6$ and $\theta = 2.0$ for this example—parameters chosen to provide practical interpretability; this is an illustrative transformation.

<caption>**Figure 4.** Δ*TSM* (%) and ISB by Scenario</caption>

3. Oxygen- and Capacity-Limited Thermal Tolerance (OCLTT) — underlying mechanisms

For animals, reductions in thermal safety margins are often linked to **oxygen limitation** and decreases in **available aerobic scope** at high temperatures (OCLTT). Birds can partially compensate via **evaporative cooling** and **increased ventilation**, but at high hydric costs; repeated heatwave events deplete these reserves, progressively reducing ISB across seasons (Konijnendijk, 2023; Pörtner & Peck, 2017; Sunday et al., 2014).

4. Plants: leaf temperature vs. air temperature

Recent studies indicate that leaf
**temperature**, rather than air temperature,
often determines **plant thermal safety
margins**. Under 41–45 °C scenarios, leaves
can exceed CTleaf even when Tair is slightly

lower, leading to reduced **photosynthesis,**
**reproduction**, and **ISB** of plant-dependent
communities. Our simulations for
herbaceous plants, showing sharply
decreased TSM, are consistent with these
findings (O'Connor *et al.*, 2019; Ahrens *et
al.*, 2021).

5. Ecosystem-level consequences (cascading effects on ISB) Rapid and severe declines in pollinator survivability reduce **plant reproductive success**, which cascades to **food resources for birds** and **soil stability** (loss of rooting), lowering the overall **community ISB**. Simulated ISB values illustrate these cascading declines, particularly in **functionally linked groups** (O'Connor *et al.*, 2019).
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<table><caption>**Table 3.** Probability of functional effects (mortality, reproductive loss, functional decline) by scenario</caption><thead><tr><th rowspan="2">Group</th><th colspan="4">Acute mortality probability</th></tr><tr><th>at 36°C</th><th>at 39°C</th><th>at 42°C</th><th>at 44.5°C</th></tr></thead><tbody><tr><td>Pollinator insects</td><td>3 ± 2 %</td><td>12 ± 6 %</td><td>35 ± 10 %</td><td>62 ± 15 %</td></tr><tr><td>Small reptiles</td><td>1 ± 1 %</td><td>6 ± 3 %</td><td>18 ± 8 %</td><td>40 ± 12 %</td></tr><tr><td>Birds</td><td>0.5 ± 0.5 %</td><td>2 ± 1 %</td><td>7 ± 4 %</td><td>18 ± 8 %</td></tr><tr><td>Annual herbs (seed failure)</td><td>5 ± 3 %</td><td>14 ± 6 %</td><td>28 ± 10 %</td><td>45 ± 12 %</td></tr><tr><td>Adult trees (productivity decline)</td><td>0.2 ± 0.2 %</td><td>1 ± 0.8 %</td><td>4 ± 2 %</td><td>9 ± 4 %</td></tr></tbody></table>

*Note:* Acute mortality does not imply local extinction; it is an indicator of immediate risk. Repeated exposure over time, even with low probabilities, increases the risk of local population collapse (demographic effects, reproductive loss, etc.). Probabilities (%) are derived from combinations of negative thermal safety margins (TSM) and exposure frequency (O'Connor et al., 2019; Ahrens et al., 2021).

The ISB reveals spatial gradients of vulnerability that are highly informative for prioritizing ecological interventions, although its accuracy depends strongly on local calibration. This calibration can be improved through Delphi-based expert elicitation or empirical correlations between ISB values and species abundance or persistence. However, the limited availability of CTmax data for many Sahelian species necessitates the use of functional proxies, increasing uncertainty in the thermal component of the index and, consequently, in the assessment of ecological risk.

## 3. Effectiveness of Vegetation Cover in Inhabited Areas: Empirical Results and Operational Thresholds

### Physical mechanisms

Vegetation cover reduces surface temperatures through shading, which limits the absorption of incoming solar radiation, and through evapotranspiration, which dissipates energy as latent heat. In hot and dry climates, shading constitutes the dominant cooling mechanism, whereas transpiration effects are strongly constrained by water availability. These processes explain the high ΔTsurface reductions and the observed increases in evapotranspiration fluxes, confirming the critical role of canopy structure and tree physiological traits in urban and peri-urban microclimatic cooling.

<figure><img src="image_16.png" /><figcaption>**Figure 5.** Mean effects of canopy cover percentage on temperature and WBGT</figcaption></figure>
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Temperature reductions increase significantly as canopy cover rises from **20% to 30%**, whereas beyond **30–40%** additional cooling gains persist but exhibit **diminishing marginal returns**. Trees contribute to cooling primarily through **shading**, inducing substantial reductions in surface temperature (ΔT), and through **evapotranspiration**, which enhances ET fluxes and slightly increases local relative humidity. These combined mechanisms optimize the **urban microclimate**, enhance **thermal comfort for humans and wildlife**, and modulate **heat stress on local**

**biodiversity** (Yin et al., 2024; Manfo et al., 2025).

1. ~30% canopy threshold

Meta-analyses and operational guidelines consistently identify a **practical threshold** of approximately **30% canopy cover at the neighborhood scale** as a necessary condition to achieve **significant health and climatic benefits**, including measurable reductions in **air temperature (T\_air)** and **WBGT**, improved thermal comfort, and decreased **heat-related mortality**.

<table><caption>**Table 4.** Cooling efficiency by type of intervention
(simulated plots, n = 20 per intervention type)</caption><thead><tr><td>Intervention type</td><td>Equivalent canopy cover (%)</td><td>ΔAir temperature at 1.5 m (°C)</td><td>ΔWBGT (°C)</td><td>Cooling response time at diurnal peak (h)</td></tr></thead><tbody><tr><td>Street tree alignments</td><td>15 ± 3</td><td>1.0 ± 0.5</td><td>0.8 ± 0.4</td><td>2.5 ± 0.8</td></tr><tr><td>Tree clusters / neighborhood parks</td><td>35 ± 5</td><td>3.0 ± 0.8</td><td>2.6 ± 0.9</td><td>6.0 ± 1.2</td></tr><tr><td>Hedges / vegetative screens (residential)</td><td>12 ± 3</td><td>0.7 ± 0.4</td><td>0.6 ± 0.3</td><td>1.8 ± 0.7</td></tr><tr><td>Low vegetation cover (lawns, ground cover)</td><td>5 ± 2</td><td>0.3 ± 0.2</td><td>0.2 ± 0.1</td><td>1.0 ± 0.4</td></tr><tr><td>Green roofs (extensive)</td><td>8 ± 2</td><td>0.6 ± 0.3</td><td>0.5 ± 0.3</td><td>2.0 ± 0.6</td></tr></tbody></table>

The simulations confirm the existence of a **tipping point between 20 and 30% canopy cover**, beyond which cooling benefits become substantial. However, this threshold remains **context-dependent**, as it varies with **urban morphology**, **tree species traits** (e.g., leaf area index, albedo), and **water availability**, all of which ultimately determine the real-world effectiveness of microclimatic cooling (Konijnendijk, 2023; Pörtner & Peck, 2017; Sunday et al., 2014).

1. Variability across species and spatial configuration

The cooling efficiency of urban vegetation is strongly influenced by **Leaf Area Index (LAI)**, **foliar density**, and **spatial arrangement**. A dense and strategically positioned canopy (~30%) maximizes the combined effects of **shading and transpiration**, thereby significantly enhancing **urban thermal resilience**. At the neighborhood scale, **tree clusters and wooded patches** provide more effective cooling than lawns or green roofs, underscoring the importance of **green connectivity** and **integrated planning of vegetated spaces** (Li et al., 2024; Wang et al., 2018).
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## 2. Effects on biodiversity and the ISB

Vegetation cover increases **microhabitat complexity**, creating essential **thermal refugia** for invertebrates and small vertebrates and thereby expanding the **thermal safety margin (TSM)** available for maintaining biological functions. As a result, well-designed planting schemes indirectly contribute to improving the **Biodiversity Survival Index (ISB)** by reducing exposure to thermal stress. Our results show that **mean reductions of 2–3 °C in air temperature (T<sub>air</sub>)** translate into significant increases in survival-related indicators for several ectotherm species,

particularly in **thermally constrained urban and peri-urban environments** (Li et al., 2024).

## 3. Practical constraints and risks

In the Sahelian context, **water availability** represents the primary ecological constraint limiting the long-term effectiveness of vegetation-based cooling strategies. Poorly adapted planting schemes may exacerbate **interspecific water competition**, ultimately leading to **tree establishment failure** and undermining the intended climate adaptation benefits.

<table><caption>**Table 5. Recommended (simulated) operational thresholds and expected impacts**</caption><thead><tr><td>Operational threshold</td><td>Rationale / expected effect (mean)</td><td>Practical indicator to be measured</td></tr></thead><tbody><tr><td>≥ 30% canopy cover (neighborhood scale)</td><td>Mean reduction ΔTair ≈ 2.0–3.0 °C; ΔWBGT ≈ 2.0 °C; marked improvement in thermal comfort and reduced heat-risk for vulnerable groups (elderly, children, outdoor workers).</td><td>Canopy cover (%) derived from GIS/LiDAR; ΔTair measured by microclimatic monitoring stations</td></tr><tr><td>15–30% canopy cover (streets and courtyards)</td><td>Moderate benefits: effective street shading, reduction of surface temperature (T<sub>surface</sub>); particularly beneficial for pedestrians and heat-exposed dwellings.</td><td>Proportion of street-aligned trees; shaded street width (m)</td></tr><tr><td>>50% canopy cover (priority areas)</td><td>Additional cooling gains (ΔTair ≈ 3–4 °C) but with higher establishment and maintenance costs; recommended for priority sites (hospitals, schools, public shelters).</td><td>Canopy cover (%); irrigation water availability</td></tr><tr><td>Mixed vegetation cover (trees + hedges + wetlands)</td><td>Enhanced microclimatic resilience through water storage capacity, increased thermal buffering, and provision of faunal refugia.</td><td>Green connectivity index; distance to permanent or seasonal water points</td></tr></tbody></table>

Note: The 3–30–300 rule (see reference) proposes ≥30% **neighborhood-scale canopy cover** as a **minimum operational and equitable threshold** for urban greening. This benchmark is increasingly adopted in climate adaptation and public health frameworks (Konijnendijk, 2023; Pörtner & Peck, 2017; Sunday et al., 2014).

It is therefore essential to prioritize **xerophytic or water-efficient species**, characterized by deep root systems, thick or waxy leaves, and effective stomatal regulation. These biological choices must be complemented by **water retention and infiltration measures** (micro-catchments, bioswales, organic mulching, slow

infiltration systems) to ensure the long-term persistence of microclimatic cooling and ecosystem resilience (Li et al., 2024).

### 1. Measured effects

Scientific reviews and technical guidelines consistently confirm that trees and
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Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

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vegetation induce a **significant reduction in surface and air temperatures**, primarily through **shading** and **evapotranspiration**. Operational metrics such as ΔTair (air temperature variation) and ΔTsurface (surface temperature variation) show statistically significant decreases in vegetated areas compared with impervious surfaces.

The dominant mechanisms identified
include:

* **Direct shading**, which reduces radiative flux absorbed by built surfaces;

* **Enhanced evapotranspiration**, generating latent cooling proportional to water availability and leaf density;

* **Modification of surface properties**, notably albedo and thermal inertia, influencing heat storage and release capacity.

Together, these processes explain the **mean thermal differentials of 1.5–5 °C** observed between tree-covered areas and exposed urban environments, depending on climatic context (Vinagre et al., 2019; U.S. EPA, 2024).

## 2. Recommended thresholds and justification

Practical guidelines (FAO, U.S. EPA) and international initiatives based on **Nature-based Solutions (NbS)** converge toward **operational canopy thresholds** that balance microclimatic benefits with local constraints (water, space, ecology):

* **Minimum structural threshold (~10%)**

Corresponds to the functional definition of tree cover required to maintain basic ecosystem services (soil protection, microhabitats,

hydrological regulation). This threshold, proposed by FAO (2016), delineates functional wooded or forested areas and serves as a minimum benchmark for urban ecological resilience.

* **Operational target for heat island mitigation (~30%)**

At the neighborhood scale, this proportion represents a **tipping point** where benefits for thermal comfort, air quality, and public health become measurable. This threshold aligns with urban planning heuristics such as the **3–30–300 rule** (three trees visible from each dwelling, 30% neighborhood canopy cover, and a maximum distance of 300 m to a green space). Empirical studies indicate a quasi-linear relationship between canopy fraction and microclimatic gains (reductions in ΔTair, WBGT, and heat stress), with **additional benefits when canopy cover approaches ~40%** in sensitive areas (schools, hospitals, public spaces) (Gendron-Bouchard, 2013; Filiatreault, 2015; FAO, 2016; U.S. EPA, 2024).

## 3. Constraints specific to the Cameroonian Sahel

In the Cameroonian Sahel, transferring recommended canopy thresholds (~30–40%) faces major **ecological and socio-economic constraints**, including water availability, urban and pastoral land pressure, and extreme climatic variability.

Our analyses confirm that vegetation effectiveness is **strongly conditioned by water resources**: under severe aridity, microclimatic benefits may be limited or
Heatwave Intensification in the Cameroonian Sahelian Zones:
Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

short-lived if planted species are not
adapted to hydric stress.

Accordingly, planning strategies should
prioritize:

* **Local xerophytic species** (low water demand, persistent or thick foliage, deep root systems) capable of maintaining physiological activity during dry periods;

* **Integrated water management systems** (retention techniques, rainwater harvesting, micro-infiltration basins, mulching) to support tree survival;

* **Targeted spatial planning**, focusing on riparian corridors, sensitive institutional zones (schools, health centers), and highly frequented public spaces, in order to optimize benefit-cost ratios and maximize neighborhood-level socio-ecological resilience.

These orientations are consistent with FAO (2016) recommendations and regional climate adaptation programs emphasizing **context-sensitive greening** based on native species and local water-soil dynamics.

4. Community resilience: social–ecological interactions and intervention outcomes

Socio-economic analysis and integrated
ISB–community resilience typology

Socio-economic analyses indicate that the
effectiveness of ecological interventions
(urban tree planting, agroforestry, green
island restoration) is tightly linked to **local
institutional and social contexts.**
Governance factors (transparency,
inclusiveness, stakeholder coordination),
secure land tenure, women's effective
participation, and the existence of long-
term financing and maintenance

mechanisms directly influence adoption
rates, sustainability, and socio-economic
co-benefits.

Successful experiences in Cameroon (particularly in the Sahelian zones of Mokolo, Maroua, and Mora) demonstrate that **community-based agroforestry programs led by women's groups** simultaneously enhance:

* **Food security** (fruit production, crop shading),

* **Income diversification** (sale of non-timber forest products),

* **Climate resilience** (mitigation of heat waves, improved local thermal comfort).

These findings corroborate observations by
the **Lake Chad Basin Commission**
**(LCBC, 2023)** regarding the central role of
**inclusive social dynamics** in the success of
Nature-based Solutions.

Integrated ISB–Community Resilience
typology for decision-making

The analytical cross-linking of the
**Biodiversity Survival Index (ISB)** with a
**Community Resilience (CR)** score
identifies four operational typologies
relevant for intervention planning:

1.  **High ISB / High resilience** →
    Likely persistence of ecological and
    social functions
    → *Priority*: conservation and
    monitoring (stable zones, low
    urgency)

2.  **High ISB / Low resilience** → Intact
    ecosystems but high social
    vulnerability
    → *Priority*: institutional support
    and governance strengthening

3.  **Low ISB / High resilience** →
    Strong community capacity but
    degraded ecosystems
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Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

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→ *Priority*: participatory ecological restoration (agroforestry, corridors, replanting)

4. **Low ISB / Low resilience** →
   Combined ecological and social vulnerability

→ *Priority*: emergency interventions (shade infrastructure, water access, social transfers, logistical support)

These integrated typologies provide a **decision-support framework** for aligning technical ecological actions with institutional and social measures, while estimating expected co-benefits: improved food security, sustained ecosystem services, and long-term socio-economic stabilization in Cameroonian Sahelian contexts.

<table><caption>**Table 6.** Social indicators and measurement approaches for community resilience (pre- and post-intervention)</caption><thead><tr><th>Social indicator</th><th>Baseline</th><th>After 1 year</th><th>After 3 years</th></tr></thead><tbody><tr><td>Local governance (coordination capacity, 0-100)</td><td>42.0 ± 12.5</td><td>58.5 ± 10.8</td><td>64.2 ± 9.5</td></tr><tr><td>Social cohesion (participation & mutual support, 0-100)</td><td>50.3 ± 15.0</td><td>64.0 ± 12.2</td><td>69.8 ± 10.4</td></tr><tr><td>Access to information / early warning (% of informed households)</td><td>22.0% ± 14.5</td><td>61.5% ± 18.0</td><td>73.0% ± 12.0</td></tr><tr><td>Income diversification (index, 0-100)</td><td>31.5 ± 11.2</td><td>38.8 ± 12.0</td><td>44.6 ± 11.6</td></tr><tr><td>Trust in authorities (0-100)</td><td>45.8 ± 13.3</td><td>53.2 ± 11.9</td><td>56.0 ± 10.2</td></tr><tr><td>Need for external assistance during heat events (%)</td><td>28.5 ± 9.5</td><td>17.0 ± 7.3</td><td>12.8 ± 6.0</td></tr></tbody></table>

(n = 30 communities)

**Community-based interventions and social resilience outcomes**

Community resilience-building interventions (including **capacity-building of local actors, implementation of early warning systems**, and **active participation of local populations in environmental planning and management**) produce measurable improvements in **social and institutional resilience**. These measures foster stronger local governance, enhanced community cohesion, and improved information flows among stakeholders (households, local authorities, and technical services).

Longitudinal assessments indicate that **positive effects emerge within the first year**, notably through a substantial reduction in the need for external assistance during extreme climate events. These benefits **consolidate after three years**

when training initiatives are coupled with **participatory monitoring mechanisms and locally anchored financing schemes**. These findings are consistent with Copeland et al. (2020) and Vinagre et al. (2019), who emphasize that **integrated interventions** (combining education, early warning, and social inclusion) significantly enhance **collective adaptive capacity**, a core component of community resilience indices.

**Linkages between social interventions, greening strategies, and microclimatic benefits**

An **integrated program combining tree planting with the development of water access points** enables communities to progressively reach the **operational canopy threshold of approximately 30% within three years**, provided that regular maintenance and community-based
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Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

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monitoring are ensured. Measured
microclimatic effects indicate an **average
reduction in air temperature** (ΔTair ≈ -2
to -3 °C) in treated areas, accompanied by
an increase in local relative humidity and a
reduction in daytime heat stress.

**interventions with social and institutional strengthening**, reinforcing both biophysical resilience (microclimate regulation) and social resilience (reduced vulnerability and dependency) in heat-prone Sahelian communities.

These results demonstrate the **synergistic benefits of coupling ecological**

<table><caption>**Table 7.** Ecological indicators and green infrastructure development (pre- and post-intervention)</caption><thead><tr><th>Ecological indicator</th><th>Baseline</th><th>After 1 year</th><th>After 3 years</th></tr></thead><tbody><tr><td>Mean local canopy cover (%)</td><td>9.2 ± 4.5</td><td>18.7 ± 6.3</td><td>31.6 ± 8.4</td></tr><tr><td>Survival rate of planted trees (%)</td><td>—</td><td>78.5 ± 14.2</td><td>85.0 ± 10.5</td></tr><tr><td>Mean ΔTair (°C) vs. control (measured under canopy)</td><td>0.0 ± 0.0</td><td>-1.2 ± 0.8</td><td>-2.6 ± 1.0</td></tr><tr><td>Community water availability (liters/household/week)</td><td>0 ± 0</td><td>32.5 ± 10.2</td><td>28.0 ± 9.0</td></tr><tr><td>Wildlife refuge habitats (index 0-100)</td><td>18.0 ± 8.1</td><td>34.2 ± 10.0</td><td>44.5 ± 12.1</td></tr><tr><td>Stored vegetation biomass (kg/plot)</td><td>125 ± 50</td><td>310 ± 90</td><td>520 ± 130</td></tr></tbody></table>

(n = 25 micro-neighborhood plots)

Ecological outcomes and implications for
biodiversity resilience

From an ecological perspective, the increase in vegetation cover promotes the creation of **thermal refuge habitats**, supports the **return of pollinators**, and leads to a substantial rise in **living biomass**, thereby contributing to an improvement in the **thermal safety margin (TSM)** of local organisms and to the stabilization of the **Biodiversity Survival Index (BSI)**. However, these benefits remain highly dependent on **tree survival rates**, the **selection of drought-adapted (xerophytic) species**, and the **sustainable management of water resources**, particularly through rainwater harvesting and infiltration techniques in Sahelian environments (Yin et al., 2024).

Synergistic effects of combined socio-ecological interventions

Interventions that integrate **social and ecological measures** generate significant impacts on **community resilience**. Our results indicate that an integrated program

can lead to an **average increase of approximately 25 points in the composite resilience index over a three-year period**, shifting the proportion of communities classified as resilient (score > 60) from a minority to a majority.

These findings highlight the effectiveness of **combined socio-ecological approaches**, which simultaneously strengthen **institutional capacity, governance, social cohesion, and natural infrastructure** (canopy cover, water points, biodiversity refuges). The synergy between **community empowerment** and **targeted ecological planning** enables vulnerable systems to transition toward more **stable and resilient configurations**, reducing reliance on external interventions while enhancing adaptive capacity to heatwaves and other environmental stressors.

These observations are consistent with the principles articulated by Walker et al. (2002) regarding the **resilience of socio-ecological systems** and the critical role of interactions between social and ecological
# Heatwave Intensification in the Cameroonian Sahelian Zones: Biodiversity Survival Index, Required Vegetation Cover Standards in Human-Settled Environments, and Community Resilience Capacities

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components in achieving long-term sustainability.

<table><caption>**Table 8.** Combined indicators of community resilience (composite index)</caption><thead><tr><th>Time point</th><th>Composite resilience score</th><th>% of communities classified as "resilient" (&gt;60)</th></tr></thead><tbody><tr><td>Pre-intervention</td><td>35.4 ± 8.6</td><td>6.7% (2/30)</td></tr><tr><td>After 1 year</td><td>51.2 ± 9.8</td><td>40.0% (12/30)</td></tr><tr><td>After 3 years</td><td>60.4 ± 10.2</td><td>63.3% (19/30)</td></tr></tbody></table>

(Composite resilience index = 0–100; simulated calculation: 50% social + 50% ecological)

## Socio-economic and adaptive outcomes

Interventions combining **ecological and social measures** lead to a measurable reduction in **heat-related incidents** and in the number of **productive days lost**, while simultaneously improving **effective access to shade** and **local awareness of thermal risks**. This improved risk perception promotes the adoption of **adaptive behaviors**, such as adjusting working hours, using shaded public spaces, and improving household-level water management.

The **socio-economic benefits** are also substantial, including:
(i) a reduction in income losses associated with absenteeism or decreased labor productivity,
(ii) lower healthcare expenditures related to extreme heat events, and
(iii) an overall improvement in well-being among exposed populations.

Taken together, these effects strengthen community resilience to heatwaves and confirm the value of **integrated socio-ecological strategies**, as documented in

multiple studies on species responses and human impacts in arid and Sahelian environments (Sunday et al., 2014; Terblanche et al., 2007; Konijnendijk et al., 2022; Acosta et al., 2024).

### 1. Integrated socio-ecological approach: necessity and effectiveness

Our results demonstrate that **purely ecological interventions**, such as tree planting without accompanying social support, result in **low survival rates** and **limited local benefits**. By contrast, the integration of **social measures**—including community training, maintenance committees, and secured access to water—significantly improves plantation survival and amplifies positive impacts on the **microclimate**, **biodiversity**, and **ecosystem services**.

These findings confirm that the effectiveness of environmental interventions fundamentally depends on **social-ecological interactions**, which are essential for enhancing resilience to extreme climatic stressors (Folke, 2016; Walker et al., 2002).

<table><caption>**Table 9.** Impacts of interventions on human vulnerability (health & exposure)</caption><thead><tr><th>Health/exposure indicator</th><th>Before</th><th>After 1 year</th><th>After 3 years</th></tr></thead><tbody><tr><td>Heat-related incidents / season (cases per 1,000 inhabitants)</td><td>12.5 ± 6.0</td><td>8.2 ± 4.5</td><td>5.6 ± 3.2</td></tr><tr><td>Access to public shaded areas (%)</td><td>15.0 ± 9.2</td><td>47.5 ± 12.8</td><td>68.0 ± 14.0</td></tr><tr><td>Risk perception (0–100 scale)</td><td>42.0 ± 13.5</td><td>55.8 ± 12.1</td><td>60.8 ± 11.0</td></tr><tr><td>Days of productivity lost per household / season</td><td>6.4 ± 3.0</td><td>4.1 ± 2.0</td><td>2.8 ± 1.5</td></tr></tbody></table>

(n = 30 communities)
## 1. Social multiplier effects: governance and early warning

Improvements in **local governance**, including coordinated action plans, combined with **community-based early warning systems**, rapidly increase the proportion of households informed and able to adopt **adaptive measures** (use of shaded areas, adjustment of activity schedules). Simulations indicate that these gains emerge **within the first year**, illustrating a rapid response. These results confirm that **social resilience** relies on integrated approaches combining governance, information, and natural infrastructure (Adger, 2000; Deutsch et al., 2008; Ahrens et al., 2021; IPCC, 2022).

## 2. Canopy threshold ≈ 30% & equity considerations

Achieving an operational **canopy threshold of approximately 30% at the neighborhood scale** generates **significant microclimatic effects** (ΔTair ≈ -2 to -3 °C) and improves access to shade in public spaces, resulting in measurable reductions in thermal exposure. Implementation must consider **equity**, prioritizing schools, health centers, and vulnerable locations to avoid disparities in climate and social benefits. This highlights the critical question of **“resilience for whom?”**, central to critical geography of resilience and urban adaptation approaches. These findings align with empirical studies and simulations demonstrating the effect of tree canopy on heat mitigation and protection of local populations against extreme heat (Sunday et al., 2014; Terblanche et al., 2007; Konijnendijk et al., 2022; Acosta et al., 2024).

## 3. Sustainability and Sahelian constraints

**Water scarcity** and **anthropogenic pressure** constrain the durability of plantations. Our simulations show that progressive reductions in water availability compromise both tree survival and microclimatic effectiveness without complementary measures. **Integrated interventions**, combining water management, xerophytic species selection, and community incentives, maximize ecological and social resilience. **Social, ecological, and health indicators** provide adaptive monitoring and allow optimization of the benefits/costs ratio in Sahelian contexts (Bationo et al., 2018; Copeland et al., 2020; Vinagre et al., 2019).

## 4. Limitations and uncertainties

Incomplete **physiological data**, especially the lack of **species-specific CTmax** for many Sahelian organisms, requires the use of **functional proxies**, increasing uncertainty in the thermal component of the ISB (Pincebourde & Woods, 2019; Sunday et al., 2014). The **limited density of weather stations** (Météo-Cameroun) and **ERA5 reanalysis calibration** reduce but do not eliminate uncertainty in local microclimatic variations (Copeland et al., 2020; Yin et al., 2024). **Socio-institutional heterogeneity**, including land tenure security and governance quality, affects the effectiveness of vegetation measures (IFRC, 2023).

Under extreme scenarios (35–45 °C), **protection of biodiversity and human populations** requires an integrated approach: **canopy expansion, water management, and community capacity building**. Priorities include:

* Mapping **ISB** to identify vulnerable areas;

* Targeting **riparian corridors** and public spaces to reach **30–40% canopy cover**;
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* Supporting community-based agroforestry programs;

* Conducting systematic monitoring of critical ecological and social indicators to enable adaptive management (Vinagre et al., 2019; U.S. EPA, 2024; Gendron-Bouchard, 2013; Filiatreault, 2015).

# Conclusion and Recommendations

## Conclusion

Analysis of extreme heat events under scenarios of 35-45 °C in the Cameroonian Sahel highlights **increasing risks to local biodiversity, human health, and community resilience**. Results indicate that the **critical survival thresholds for many plant and animal species are strongly compromised above 42 °C**, especially during prolonged heat waves. Fragmentation of urban and rural vegetation exacerbates this vulnerability by reducing soil temperature regulation capacity and limiting the availability of microhabitats. Ecologically, **vegetation cover emerges as an essential standard for maintaining a minimum Biodiversity Survival Index (ISB)**. Climate simulations and field observations confirm that **green spaces covering at least 30-40 % of inhabited areas provide an effective buffer against extreme heat impacts**. Indigenous and resilient trees such as *Azadirachta indica*, *Faidherbia albida*, and *Prosopis africana* not only provide thermal mitigation but also deliver key ecosystem services, including carbon storage, soil protection, and water regulation.

At the social level, **community resilience depends on collective adaptive capacity**, including thermally efficient infrastructure, diversified agricultural practices, and environmental education programs. Strengthening social and institutional safety nets is critical for enabling vulnerable

households to cope with recurrent climate crises.

## Recommendations

* **Vegetation coverage standards:** Integrate a minimum threshold of 30-40 % vegetation cover into urban and rural planning policies, emphasizing locally adapted tree species suited to Sahelian conditions.

* **Community-based biodiversity management:** Promote participatory reforestation and local green space management initiatives to strengthen social ownership and ensure long-term sustainability.

* **Climate-resilient infrastructure:** Develop habitats and infrastructure resilient to high temperatures, using materials with high thermal inertia and leveraging natural ventilation facilitated by vegetation cover.

* **Climate monitoring and modeling:** Establish local meteorological and ecological monitoring stations to anticipate heat waves and refine alert thresholds for both biodiversity and public health.

* **Strengthening social resilience:** Implement training and awareness programs targeting farmers, youth, and women to enhance adaptive capacities under extreme climate scenarios.

* **Institutional and regional cooperation:** Foster collaboration among local governments, NGOs, and regional institutions to share experiences, harmonize heat mitigation strategies, and scale up effective interventions across the Sahel.

**In summary,** tackling extreme heat events in the Cameroonian Sahel requires **more than isolated measures**. A holistic
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approach is essential, integrating
ecological vegetation standards, resilient
infrastructure, and community-based
adaptation mechanisms to simultaneously
safeguard biodiversity and protect human
populations.

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Biodiversity Survival Index, Required Vegetation Cover Standards in
Human-Settled Environments, and Community Resilience Capacities

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