Compounding forest disturbances and livelihood insecurity: Mapping wildfire regimes, bio-invasions, and socio-ecological vulnerability in Jharkhand, India

Introduction

Forest ecosystems across the tropical world are undergoing profound transformations driven by the interactive forces of climate change, biological invasions, and anthropogenic disturbance regimes. In tropical dry and moist deciduous forest matrices, such as those spanning the Chota Nagpur Plateau in Eastern India, these disturbance vectors rarely operate in isolation. Instead, recurring surface forest fires and the aggressive proliferation of invasive alien species (𝐼𝐴𝑆) establish coupled ecological feedback loops that accelerate canopy thinning and structural degradation. While biophysical research frequently tracks the environmental dimensions of forest loss, there is growing recognition that these ecological perturbations fundamentally restructure human-environment interactions, altering local resource availability, traditional gathering practices, and rural socio-economic stability. In Jharkhand, a state characterized by rich tropical biodiversity and a substantial indigenous tribal population, forest landscapes serve as vital social and economic safety nets. Millions of forestfringe dwellers rely on common pool forest resources for Non-Timber Forest Produce (NTFP) harvesting, fuelwood, fodder, and seasonal grazing to supplement rainfed agriculture. However, multi-decadal inventories reveal an ongoing conversion of Moderately Dense Forests (MDF) into Open Forests (OF) and Scrub across several districts. This canopy degradation is closely tied to chronic seasonal fire occurrences and the widespread invasion of unpalatable woody shrubs, primarily Lantana camara, which displaces native floor flora, suppresses tree regeneration, and increases understory fuel loads.

Despite the acknowledged severity of these disturbance factors, regional forest governance frameworks-such as Joint Forest Management (JFM) institutions-have historically prioritized timber protection and conventional conservation over adaptive weed management and community-centered bio-economic restoration. Consequently, the compounding impacts of ecological degradation fall disproportionately on vulnerable forest-dependent households, increasing gathering labor burdens and heightening rural livelihood insecurity. Evaluating these dynamics requires a unified socio-ecological approach that bridges macro-level biophysical forest monitoring with microlevel community vulnerability assessment.

Research Gap and Objectives

While national forest inventories provide high-resolution spatial tracking of canopy density and disturbance trends, there remains a critical research gap in systematically linking these biophysical metrics to socio-economic vulnerability indices across distinct administrative and agro-climatic zones in Eastern India. Existing literature often analyzes forest fires, biological invasions, or socio-economic poverty in isolation, omitting their spatial convergence and combined effect on indigenous adaptive capacity.

Study Area and Regional Setting

The study focuses on the forest-dependent districts of Jharkhand, India, situated across the Chota Nagpur Plateau between 2158'N to 2518'N and 8322'E to 8757'E. Characterized by dry and moist deciduous Sal (Shorea robusta) forests, the region exhibits high seasonal vulnerability to anthropogenic and natural forest fires alongside aggressive biological invasions.

Data Harmonization and Secondary Data Sources

Construction of the Composite Socio-Ecological Vulnerability Index (CSVI)

To quantify socio-ecological risk at the district level, metrics were normalized, weighted, and aggregated using the Min-Max Normalization Method

Where 𝑋 𝑛𝑜𝑟𝑚 represents the standardized value bounded between 0 and 1. For inverse parameters (where higher values indicate lower vulnerability, such as Adaptive Capacity), normalization was computed as

Sub-Index Weighting and Mathematical Aggregation

Equal or Principal Component Analysis (PCA)-derived weights (𝑤 𝑖 ) were assigned to indicators across the three dimensions: a. Exposure Sub-Index (𝐸 𝑖 )

Final Index Formulation

The overall Composite Socio-Ecological Vulnerability Index (CSVI) for each district was constructed as:

Districts were subsequently classified into four vulnerability categories using natural break optimization (Jenks Classification): Low, Moderate, High, and Extreme Vulnerability.

Spatial & Econometric Analysis Techniques

To test the interaction between ecological disturbance and socioeconomic vulnerability, three statistical routines were performed:

a. Bivariate Spatial Autocorrelation (Moran's 𝐼)

Evaluated the spatial clustering between high forest disturbance zones (fire frequency and invasive species spread) [8,9] and districts with high socio-economic vulnerability metrics.

b. Ordinary Least Squares (OLS) & Geographically

Weighted Regression (GWR) Modeled the spatial impact of invasive species spread (Lantana) and fire occurrences on NTFP extraction loss and forest canopy degradation across districts.

c. Non-Parametric Trend Testing (Mann-Kendall):

Applied to historical ISFR datasets to identify multi-decadal monotonic trends in forest open-canopy transitions (MDF to OF/Scrub) across Jharkhand's mining vs. non-mining districts. [19] . The spatial overlap between high fire frequency and Lantana invasion establishes a self-reinforcing ecological disturbance loop: recurrent surface fires open the upper tree canopy, which accelerates Lantana seed germination, while Lantana's flammable, resinous woody biomass subsequently elevates understory fuel loads and fire severity (Ranjan, 2022) [15] . The descriptive statistics of biological and social economic variables across sample district is presented in table 1.

Results and Discussion

Canopy Degradation and Socio-Economic Sensitivity

Long-term forest cover change matrices reveal structural degradation across the state: while total forest cover has remained relatively stable, there is a consistent decadal transition of Moderately Dense Forest (MDF) into Open Forest (OF) and Scrub (Feder et al., 1985) [4] . This canopy thinning intersects directly with rural vulnerability. Secondary extraction statistics indicate that forest-fringe households harvest significant volumes of fuelwood, fodder, and Non-Timber Forest Produce (NTFP)such as Mahua (Madhuca longifolia), Sal leaves (Shorea robusta), and Kendu leaves (Diospyros melanoxylon)-to offset seasonal agricultural shortfalls (Adesina & Zinnah, 1993) [1] . The decadal canopy class transitions and parametric comparison across high versus low fire prone belts is presented in table 2. As invasive alien species displace native floor flora and regeneration saplings, indigenous communities experience an acute decline in NTFP collection yield per unit time (Tiwari et al., 2022) [19] . Furthermore, female gatherers bear a disproportionate labor burden, traveling significantly longer distances into deeper, highly fragmented forest patches to collect essential energy resources (Setty, 2021) [18] . The non-parametric correlation matrix between ecological disturbances and livelihood matrix is presented in table 3.

Composite Socio-Ecological Vulnerability Index (CSVI) Mapping

The Composite Socio-Ecological Vulnerability Index (𝐶𝑆𝑉𝐼), aggregated across normalized sub-indices of Exposure (𝐸), Sensitivity (𝑆), and Adaptive Capacity (𝐴𝐶), shows high spatial clustering of risk across administrative zones as shown in Table 4. [18] . [13,18] . Furthermore, aligning ecological restoration-such as native broad-leaved species enrichment-with Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) programs can strengthen local adaptive capacity while reversing canopy degradation.

Conclusion

This study highlights the compounding nature of biophysical forest disturbances and socio-economic insecurity across the tropical deciduous forest landscapes of Jharkhand. By integrating remote sensing-derived forest degradation metrics with household reliance and socio-economic vulnerability indicators, the findings confirm that forest fire occurrences and Lantana camara invasions do not act as isolated environmental stressors. Instead, they form a self-reinforcing ecological feedback loop: seasonal surface fires thin the forest canopy, creating light conditions that facilitate aggressive Lantana germination, which in turn increases understory fuel loads and exacerbates subsequent fire severity. This biophysical degradation directly translates into socioeconomic strain for fringe-dwelling indigenous communities. The transition of Moderately Dense Forests into fragmented Open Forests and Scrub reduces native biodiversity, severely suppressing NTFP collection yields such as Mahua, Sal, and Kendu leaves. Consequently, rural households-particularly female gatherers-face increased labor burdens and energy insecurity as they are forced to travel greater distances for basic forest resources. Spatial analysis via the Composite Socio-Ecological Vulnerability Index (CSVI) underscores significant regional disparities, with the western dry deciduous belt (Zone V) facing extreme vulnerability driven by a confluence of high fire hazards, heavy weed proliferation, elevated poverty headcount, and limited non-agricultural employment options. The policy implications of these results call for an overhaul of traditional forest governance in Eastern India. Traditional Joint Forest Management (JFM) approaches, which focus primarily on protection and timber conservation, are insufficient to address coupled bio-invasions and fire risks. Reversing canopy degradation and securing rural livelihoods requires transitioning toward Bioeconomic Restoration Frameworks. Institutionalizing community-led invasive weed management-where Lantana biomass is harvested and upcycled into bio-composites, handicrafts, and bio-energy-can simultaneously clear understory fire fuel, restore native floor flora, and generate offfarm income. Aligning these ecological restoration goals with national employment schemes like MGNREGA offers a viable path toward strengthening community adaptive capacity and building long-term socio-ecological resilience.

Disclaimer (Artificial Intelligence)

Author(s) hereby declare that NO generative AI technologies such as Large Language Models and text-to-image generators have been used in any part of this manuscript.

Competing Interests

Author have declared that no competing interests exist.

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