The research, led by Bhushan K. Shigwan and colleagues at the Agharkar Research Institute in Pune, set out to answer a deceptively simple question: do different kinds of forest protection actually produce different kinds of forests? The team compared four distinct protection regimes across the Northern Western Ghats and the adjacent Konkan coastal belt—state-run Protected Areas such as national parks and wildlife sanctuaries, government-managed Reserved Forests, community-protected Sacred Groves, and privately owned forests that include coffee plantations and silvicultural holdings. While earlier studies had examined individual regimes in isolation, no comprehensive survey had systematically compared tree diversity, community structure, and species composition across all four within this region as an integrated landscape.

The scale of the effort was considerable. Between 2017 and 2021, the researchers laid out forty transects—ten in each protection regime—stretching across an elevational range from 26 to 1,411 meters above sea level and a latitudinal span of nearly five degrees. Each transect ran 140 meters and contained four quadrats of 20 by 20 meters, spaced with 20-meter gaps to reduce spatial autocorrelation. Within every quadrat, the team measured all woody individuals with a girth at breast height of at least 30 centimeters, recording species identity, abundance, and trunk dimensions. Field identifications were verified against herbarium specimens at the Agharkar Herbarium of Maharashtra Association, and sampling was deliberately timed between December and May to avoid the monsoon, minimize phenological bias, and comply with access restrictions during wildlife breeding periods.

To quantify human pressure, the researchers scored eight categories of disturbance at each site—cutting, lopping, fire, grazing, construction, internal pathways, other human activities such as festivals and tourism, and proximity to roads—on an ordinal scale from zero to five. These scores were aggregated into a Combined Disturbance Index, a single number capturing the overall anthropogenic footprint of each forest. The results were striking. Sacred Groves recorded the highest average disturbance score at 47.75, followed by private forests at 34.5 and Reserved Forests at 31.5, while Protected Areas enjoyed the lowest at 17.5. The differences were statistically significant, confirming that the four regimes genuinely represent a gradient of human impact rather than merely different labels on similar land.

In total, the team recorded 3,360 woody plants representing 148 species, 118 genera, and 43 families. Reserved Forests emerged as the diversity champions, with the highest number of species at 89, along with the greatest richness of evergreen and endemic species. Protected Areas followed closely, while Sacred Groves matched Protected Areas in species count. Private forests told a different story: they hosted the highest richness and abundance of deciduous species and the lowest representation of evergreens, painting a picture of communities shifted toward disturbance-tolerant, drought-deciduous taxa. Shannon diversity values ranged from 1.13 to 2.98 across sites, and permutation-based analysis of variance revealed significant differences in species richness among regimes, though post-hoc tests suggested these arose from modest but consistent shifts rather than any single dramatic contrast.

Perhaps the most technically revealing results came from the structural analysis. Tree density differed strongly among regimes, with Reserved Forests and Protected Areas supporting the highest stem counts, dominated by individuals in the smallest girth class of 30 to 60 centimeters—a signature of active regeneration and younger, recovering stands. Sacred Groves, by contrast, showed lower densities but the highest mean basal area at 42.33 square meters per hectare, significantly exceeding both private forests and Reserved Forests. In the largest girth classes, above 150 centimeters, Sacred Groves held significantly more trees than any other regime. These giants, some potentially centuries old, represent legacy biomass that younger forests cannot replicate, and they position sacred groves as critical reservoirs of carbon and structural complexity in a fragmented landscape.

Community composition told a subtler story. Non-metric multidimensional scaling of site-level abundances showed that most sampling sites from Protected Areas, Sacred Groves, and Reserved Forests clustered tightly together, indicating broad overlap in species assemblages. Cluster analysis identified two main groupings—Sacred Groves with Protected Areas, and private forests with Reserved Forests—with overall similarity between regimes hovering around 38 percent, and roughly 50 to 54 percent within the paired groups. Only 26 species were shared across all four regimes, while each harbored 11 to 20 unique species. An analysis of similarities confirmed that compositional differences among regimes were weak and statistically non-significant, suggesting that chronic disturbance reshapes forest structure and functional traits without necessarily causing wholesale turnover in which species are present.

One species stood out as the great connector of these fragmented worlds. Memecylon umbellatum, a shade-intolerant, early-successional tree dispersed by birds and mammals, ranked among the dominant species in every protection regime, contributing disproportionately to total abundance everywhere the team looked. Its ubiquity likely reflects both its tolerance of canopy disturbance and the persistence of animal dispersal vectors across the landscape. Seed dispersal analysis added another layer: autochorous, self-dispersing species were significantly more common in private and Reserved Forests, where disturbed microhabitats favor them, while zoochorous, animal-dispersed species—typical of evergreen forests—remained well represented across all regimes, hinting that ecological relationships between trees and their dispersers persist even in degraded stands.

The conservation implications are profound. The authors argue that no single protection regime can represent the full biodiversity and structural value of the Northern Western Ghats; instead, the region’s ecological integrity rests on the complementarity of its management mosaic. Sacred Groves, sustained for generations by socio-cultural norms rather than enforcement, act as refugia for mature trees and endemic species, yet they are increasingly threatened by urbanization and the erosion of traditional practices, with significant losses reported over the past five decades. Protected Areas and Reserved Forests, meanwhile, are the engines of regeneration, teeming with young stems that promise future canopy. Private forests, though the most degraded, offer the greatest restoration opportunity: reintroducing evergreen and endemic species at lower elevations could recover biomass, carbon storage, and habitat connectivity.

For a biodiversity hotspot where primary forest loss and human pressure continue to mount, the message is clear and urgent. Formal legal protection matters—it suppresses disturbance and nurtures the next generation of forest. But cultural protection matters too, in ways that law cannot replicate, preserving the ancient giants that anchor forest ecosystems. The researchers call for regime-specific conservation strategies that pair disturbance regulation and restoration in private and community forests with renewed support for the community stewardship that keeps sacred groves standing. In the Northern Western Ghats, saving biodiversity will require not one fortress of conservation, but an entire alliance of them—legal, cultural, and private—each playing an irreplaceable role in sustaining the forest of the future.

Subject of Research: Comparative assessment of tree diversity and forest structure across four protection regimes in the Northern Western Ghats, India

Article Title: Assessing the impact of differential protection regimes on tree diversity and composition in the Western Ghats: insights for conservation strategies

Article References: Shigwan, B. K., Kulkarni, A., Smirthy, V., Page, N. V., Shetti, R., & Datar, M. N. (2026). Assessing the impact of differential protection regimes on tree diversity and composition in the Western Ghats: insights for conservation strategies. Discover Ecology, 2(1), Article 2. https://doi.org/10.1007/s44396-025-00016-7

Image Credits: AI Generated

DOI: 10.1007/s44396-025-00016-7

Keywords: Western Ghats, sacred groves, tree diversity, forest conservation, protected areas, basal area, endemic species, anthropogenic disturbance, seed dispersal, biodiversity hotspot, community conservation, forest structure

Known as Kheraidaphni among the Bodo community and Matiajam more broadly in Assam, P. herbacea is harvested as a leafy vegetable and used in traditional medicine. Young shoots, leaves and ripe fruits are consumed, and earlier nutritional studies have reported approximately 15.38 percent protein and 41.75 percent carbohydrates, along with micronutrients including zinc, molybdenum, copper, manganese, iron and magnesium. The plant also contains reported phytochemicals such as phenolics, flavonoids, terpenoids and saponins. Traditional systems including Ayurveda, Siddha and Unani have associated the species with treatments for diabetes, jaundice, fever and sleeping sickness, while laboratory research has begun investigating possible antioxidant, antidiabetic and liver-related effects. Those uses do not by themselves establish clinical efficacy, but they illustrate why the plant is important to local communities. Its recognition as a Geographical Indication and its status as a non-timber forest product add economic and cultural value, while harvesting pressure creates a practical conservation challenge: the same plant can be both a livelihood resource and a vulnerable component of a shrinking habitat.

To estimate where the herb could occur, the researchers used Maximum Entropy, or MaxEnt, version 3.4.4, a species-distribution algorithm designed to work with presence-only observations. Such models do not require researchers to document every place where a species is absent. Instead, they compare known occurrence locations with environmental conditions across a study region and estimate how suitable other locations may be. The team assembled 75 georeferenced records from field surveys, published studies, herbarium material and ethnobotanical databases. Because clusters of records can make a model learn the geography of surveying rather than the ecology of a species, the researchers applied spatial thinning. They removed records located within one kilometre of one another and ensured that no two observations occupied the same 30-arcsecond grid cell, leaving 65 records for modelling. This procedure reduced the effects of spatial autocorrelation and helped limit overfitting, although it could not eliminate geographic sampling bias entirely.

The analysis covered Assam, a state of about 78,438 square kilometres extending from the Eastern Himalaya toward the Indo-Burman ranges. The region includes the Brahmaputra and Barak river valleys, alluvial floodplains, wetlands, riparian woodland, moist deciduous forest and sub-Himalayan grasslands. Elevation ranges from roughly 15 metres above sea level in the floodplains to more than 1,800 metres in foothills and uplands. Assam’s subtropical monsoonal climate delivers about 1,807 millimetres of annual rainfall, with more than 70 percent arriving during the June-to-September monsoon. From 19 bioclimatic variables, elevation-related layers, soil information and land-use data, the researchers retained nine predictors after correlation and variance-inflation screening. The final variables represented temperature patterns, annual and seasonal precipitation, elevation, soil type and land use or land cover. Pairwise correlations above 0.80 were removed, and the remaining variables were screened using a variance inflation factor threshold of three, reducing the risk that overlapping predictors would distort the model.

The resulting model showed exceptionally strong discrimination across its evaluation measures. Ten bootstrap replicates produced a mean area under the receiver operating characteristic curve of 0.995, with a standard deviation of 0.003. The true skill statistic was 0.87 and Cohen’s kappa was 0.84. AUC measures how effectively a model ranks suitable sites above unsuitable ones, while TSS and kappa assess classification performance using sensitivity and specificity, with kappa also correcting for agreement expected by chance. The researchers optimized model complexity with the ENMeval package, testing regularization settings and feature combinations through spatially partitioned cross-validation. The selected configuration used a regularization multiplier of 1.5 and linear-plus-hinge features, with 10,000 background points and 10 bootstrap runs. The prediction was expressed in cloglog format, producing suitability values from zero to one. Strong metrics indicate a well-performing model for the available data, but they do not mean that every predicted site contains the plant or that the species’ future range is guaranteed.

Of the estimated 1,331 square kilometres of suitable habitat, 383 square kilometres, or 28.79 percent, was classified as highly suitable. Another 199 square kilometres, or 14.95 percent, fell into the moderate category, while 749 square kilometres, or 56.26 percent, was designated low suitability. The classifications were based on the maximum training sensitivity plus specificity threshold, a method intended to balance missed presences against false-positive predictions in presence-only modelling. The strongest concentration appeared across the northern sub-Himalayan grassland belt, especially in and around Manas and Orang National Parks. Within those protected areas, the model identified 810 square kilometres of suitable habitat: 354 square kilometres of high suitability, 89 square kilometres of moderate suitability and 367 square kilometres of low suitability. High-suitability habitat therefore represented 43.70 percent of suitable area inside the parks, compared with 28.79 percent across Assam as a whole. The result highlights the parks as important refuges, while also showing that a substantial portion of the potential range lies outside their core boundaries.

Land use and land cover emerged as the most influential predictor, contributing 59.0 percent to the model and accounting for 53.9 percent of permutation importance. Suitability was highest in grassland classes and declined sharply in agricultural, forest and built-up areas. Precipitation seasonality, represented by the bioclimatic variable BIO15, contributed 21.7 percent and had the same value for permutation importance, showing that the timing and variability of rainfall are central to the plant’s distribution. Assam’s monsoon cycle influences grassland growth, soil moisture and the timing of conditions suitable for establishment. Soil type was also identified as a major driver, likely because substrate properties affect water retention and nutrient availability. Elevation contributed 5.3 percent, yet its permutation importance reached 12.8 percent, suggesting that small topographic differences may influence local moisture and soil conditions. Jackknife tests supported the importance of land cover and precipitation seasonality: each produced high model gain when used alone and caused the greatest reduction in gain when omitted.

These patterns place the plant’s conservation within the broader crisis facing tropical and subtropical grasslands. Such habitats are increasingly altered by woody encroachment, invasive alien plants, changes in fire regimes, agriculture and other human pressures. The loss or conversion of open grassland can remove suitable conditions for P. herbacea even when the surrounding landscape remains green. The species’ predicted range also overlaps habitats used by the pygmy hog, greater one-horned rhinoceros and Bengal florican, making management decisions relevant to several conservation priorities at once. Harvesting inside protected areas may create additional disturbance, while poorly regulated collection could reduce local plant populations. At the same time, excluding communities from management would overlook the knowledge, food value and income linked to the species. Because suitable areas were predicted in buffer and peripheral zones, the researchers propose ecological restoration and community-based or co-managed harvesting as possible strategies. Such measures could include protecting grassland structure, monitoring populations, regulating collection and linking conservation rules with equitable local benefits.

The study is a first spatial assessment rather than a final range map. Its occurrence records were concentrated in Manas and Orang, where survey effort has been comparatively strong, so the model may overrepresent protected-area conditions. The analysis also describes suitability under current environmental conditions and does not project how future climate change could alter rainfall seasonality, temperature or habitat availability. Independent field surveys across high-, moderate- and low-suitability areas would provide a stronger test of the predictions and could reveal undocumented populations. Even with those uncertainties, the map supplies a practical framework for deciding where surveys, restoration and harvest monitoring should begin. The central message is that a culturally valued edible herb depends on a specialized and threatened grassland landscape. Protecting P. herbacea will therefore require coordinated action among communities, forest managers and conservation planners, combining habitat protection with sustainable use. By connecting local food traditions to quantitative habitat modelling, the research gives Assam a more precise basis for conserving both the plant and the grasslands that sustain it.

Subject of Research: Potential habitat and conservation needs of Premna herbacea in Assam

Article Title: Predicting the distribution of Premna herbacea Roxb., a wild edible plant from the plains of Assam

Article References: Goswami, S., Gogoi, M., Kalita, J., & Choudhury, M. (2026). Predicting the distribution of Premna herbacea Roxb., a wild edible plant from the plains of Assam. Discover Conservation, 3(1), Article 36. https://doi.org/10.1007/s44353-026-00105-y

Image Credits: AI Generated

DOI: 10.1007/s44353-026-00105-y

Keywords: Premna herbacea, Assam, MaxEnt modeling, species distribution, grassland conservation, wild edible plants, non-timber forest products, community conservation, Predicting, distribution, Premna, herbacea