The study covered the coastal zone from the Romanian border to Varna and divided it into three regions for analysis. Landslides affect approximately 55,000 decares across the full area, including about 2,042 decares of active landslides. In the section from the Romanian border to Cape Kaliakra, landslides occupy 1,338 decares, of which about 4 decares are active. Between Cape Kaliakra and Kranevo, the affected area is 12,560 decares, including about 1,168 active decares. The third section, from Kranevo to Varna, contains 41,097 decares affected by landslides, of which about 870 decares are active. Risk profiles were calculated specifically for the summer tourist season.

The methodology defines socio-ecological risk as a function of five components: hazard, exposure, vulnerability, adaptability and coping capacity. Its equation is R = (wH + wE + wV) − wA − wCC, with corresponding weights applied to each term. Data were converted to a unitless common scale, and the weighting coefficients were required to sum to 100. Indicators were assigned three levels—low, medium and high—and combined into risk categories ranging from very low to very high. This was a regional modelling and risk-assessment exercise rather than a direct measurement of landslide losses or an evaluation of a mitigation programme.

Geological conditions vary substantially among the three sections. Small landslides in the northern area affect an eroded loess complex. The step-shaped Taukliman and Yaylata landslides north of Cape Kaliakra probably developed after strong earthquakes; their geological setting includes Oligocene clays beneath 30–40 metres of limestone from the Odartsi Formation. In the central section, old landslide bodies commonly reach depths of 40–60 metres, and additional landslide packages extend below the present sea level. Along the Frangya Plateau in the southern section, deep-seated, circus-type landslide complexes are associated mainly with marine abrasion. More recent local activity occurs against the background of these older, conditionally stabilised landslides.

Protected areas and cultural heritage were incorporated alongside conventional social and infrastructure considerations. Around 15 protected areas in the study region are included in the Natura 2000 network. The Shabla Lake Complex covers 3,175 hectares and supports 260 bird species, 70 of which are listed in Bulgaria’s Red Book. The 687.5-hectare Kaliakra Reserve contains more than 400 plant species, including 47 described as extremely rare and endangered, and more than 310 bird species, 106 protected at European level. Archaeological features considered in the wider study included sites associated with Durankulak, Yaylata, Cape Kaliakra and the coastal landscape near Varna.

The results distinguish among the three sections rather than establishing a single coast-wide risk level. In Region 1, archaeological-site risk ranged from low through medium to high, biodiversity showed a similar range, and social risk varied from low to medium. Its total integrated risk was about 24–25%. Region 2 had the highest result, at approximately 85–86%. Archaeological sites were its most vulnerable component and were joined by high social risk, while biodiversity risk was assessed as medium. Region 3 occupied an intermediate position at 58%; archaeological-site risk ranged from high to low, social sites were considered vulnerable in connection with intensive population distribution, and biodiversity risk was predominantly medium.

The analysis also found that Regions 2 and 3 combined significant landslide susceptibility with coping capacity, while adaptive capacity was relatively moderate to low compared with coping ability. The authors state that coping capacity alone is insufficient to achieve the resilience needed to lower risk. They identify transformability—the capacity to shift development paths or create alternatives—as a major focus for socio-ecological risk management. Within the framework, reducing vulnerability and increasing adaptive capacity were identified as the most efficient strategies, especially where archaeological sites and protected natural areas occur close together.

The authors report that the acquired data were consistent with recorded landslide occurrences and empirical analysis, and they describe the methodology as flexible and adaptable. They also note that at high risk levels, a disaster could lead to biodiversity loss and ecosystem damage that could not be reversed. The framework can accommodate additional indicators, including climate-related parameters, but the reported regional percentages remain model estimates based on the selected data, scores and weighting assumptions rather than observed outcomes from a future landslide event.

A principal limitation identified by the authors is the absence of a comprehensive, detailed assessment of contemporary geological hazard and risk at archaeological sites along the northern Bulgarian Black Sea coast. Such work would require specialists in geology, engineering geology and the assessment of present-day hazards at heritage locations. The risk estimates also depend on indicator scores, weighting assumptions and the availability of data for hazards, exposure, vulnerability, adaptability and coping capacity. As a result, the study provides a comparative socio-ecological assessment of the three sections, with the central section between Cape Kaliakra and Kranevo identified as the riskiest under the proposed methodology.

Subject of Research: Regional assessment of socio-ecological landslide risk across three sections of Bulgaria’s northern Black Sea coast

Article Title: Rethinking the landslide risk assessment for socio-ecological systems using the example of the northern Bulgarian Black Sea coast

Article References: Frantzova, A., Ivanov, P., Dobrev, N., Berov, B., Nankin, R., Krastanov, M., & Ranguelov, B. (2024). Rethinking the landslide risk assessment for socio-ecological systems using the example of the northern Bulgarian Black Sea coast. One Ecosystem, 9, Article e136166. https://doi.org/10.3897/oneeco.9.e136166

Image Credits: AI Generated

DOI: 10.3897/oneeco.9.e136166

Keywords: landslide risk, socio-ecological systems, Bulgarian Black Sea coast, Cape Kaliakra, Kranevo, Varna, biodiversity, archaeological heritage, adaptive capacity, risk modelling, Rethinking, landslide

Agricultural landscapes are quintessential socio-ecological systems, places where social and environmental factors co-exist and interact in inherently complex ways. When modellers try to reproduce these systems, farmers’ behaviour is of paramount importance. Crop choice, the application of certain agricultural practices and the adoption of innovations are actions that ripple through the economy, social sustainability and environmental sustainability of an agricultural landscape. Agent-based models, computational representations that focus on the atomistic parts of a system and describe their behaviour and interactions in a bottom-up approach, have become the tool of choice for capturing these dynamics. Yet while models involving farmers’ behaviour are now quite numerous, relatively few studies explicitly account for social interactions. GeSoN was designed precisely to fill this gap, recreating the farmers’ social context relevant to their decision-making process.

The theoretical heart of the model is the bounded rationality framework, first articulated by Herbert Simon. Bounded rationality states that, when making decisions, humans do not set out complex optimisation procedures; instead, they mediate between constraints regarding time, knowledge and cognitive abilities. Four distinct facets of human decision-making must be considered: humans encounter difficulties in evaluating and planning long behavioural sequences given their limited cognitive capacity and the inherent complexity of their environment; people tend to set aspirational levels related to specific goals; they work on goals sequentially rather than simultaneously; and their search strategy is aimed at satisfaction rather than optimisation. A vital aspect of the framework is ecological rationality, the insight that decisions are strongly influenced by the environment, both physical and social, relevant to agents’ goals and needs. This stands in deliberate contrast to the Expected Utility Theory, whose assumptions of selfishness, stable transitive preferences and unlimited cognitive capacity have been refuted by numerous empirical studies in different domains.

To model the interaction amongst farmers, the researchers turned to opinion dynamics, a branch of social network analysis that studies the evolution of individuals’ views as the result of interactions within a network. The approach assumes people influence each other, describes the interaction process from an individual point of view and produces results emergent from those interactions, pooled for the population. GeSoN belongs to the family of models in which opinions are continuous variables, usually ranging between 0 and 1, following the seminal work of Deffuant and colleagues and of Hegselmann and Krause. The major constraint applied is bounded confidence: influence during an interaction occurs only when a specific condition regarding the similarity between opinions is met. The underlying assumption is that opposite opinions have little or no influence on each other, a principle grounded in the observation that people with similar attitudes are more likely to interact and that there is a lack of understanding between persons with substantially different views.

What sets GeSoN apart is its multi-layer network structure, built on the concept of geographical specificity. The model generates connections between farmers using different criteria, forming a multi-criteria web of channels through which farmers interact. The first and major component is the Geographical Network, which represents the influence over the landscape of so-called nodal farmers, those that create the highest number of connections with others and hence significantly influence the agricultural landscape. This network uses a modified gravity model, an empirical economic model originally inspired by Newton’s law of gravitation and primarily applied to international trade studies. The two main assumptions are that closer farmers are more likely to interact and that larger farms, in terms of farm size, have more influence over their neighbours than smaller ones. The strength of the link connecting Farmer i with Farmer j is computed as the size of Farmer j divided by the physical distance between the two farms. After computing the force connecting them, all farmers hold a ranked list of the other farmers in the landscape, ordered by that force, and choose a predetermined number of neighbours from the top of the list when interactions take place.

Two further layers complete the structure. The Associative network simulates the presence, in a landscape, of groups of cooperating farmers, such as members of the same cooperative, a producer organisation or similar farmers in terms of agricultural production. This layer matters because farmers are strongly affected in their decision-making by being part of such groups. The Virtual network captures the non-agricultural connections between farmers, like friendship or parenthood, incorporating a determined level of stochasticity into the structure. Both of these layers are exogenous and predetermined at the beginning of a simulation. Together, the three layers evolve beyond the purely mathematical random connection rules of the seminal Watts-Strogatz and French-DeGroot models, forming a genuine socio-economical network in which economic and social criteria shape agents’ connections.

Once the links are formed, opinions travel through them. In GeSoN, opinions such as risk aversion and sustainability concerns are modelled as continuous variables ranging from 0 to 1, with 1 excluded. Crucially, every farmer carries not only a personal opinion but also a level of uncertainty, modelled as a continuous variable ranging from 0 to 0.6, with the farmer’s opinion sitting at the uncertainty’s central point. The minimum distance required to enable the influence of one opinion over another is represented by the uncertainty associated with the influenced agent’s opinion. The magnitude of influence is controlled by a mobility parameter, fixed for all agents and set at the beginning of the simulation. The governing formula is a weighted mean of the influenced farmer’s opinion and the central part of the influencing farmer’s shared opinion, with weights given by the mobility parameter and its inverse, both scaled by a factor indicating the difference in the farmers’ uncertainties. This factor makes the influence between farmers with unequal uncertainties asymmetrical: when two farmers with high and low uncertainty respectively interact, the effect is stronger on the farmer with high uncertainty. Time is discrete, and farmers upload their opinions only after every other farmer has interacted with their respective neighbours, ensuring that the starting order of farmers makes no difference to the results.

The researchers demonstrated the model’s behavioural capabilities with a prototype implementation in Python, running simulations over both real and simulated landscapes. In a scenario based on the real Danish landscape of Himmerland, with 190 farmers whose locations and sizes were extracted from the EU’s Land Parcel Identification System database, initial opinions drawn from a uniform distribution evolved over 100 time steps into a double peak distribution, with the more prominent peak slowly forming from around the sixtieth time step. In a striking comparison, two simulations with identical input values but different network structures produced substantially different results: applying all three networks showed polarisation of opinions around two values, roughly 0.3 and 0.7, while applying just the Geographical Network showed consensus around the mean opinion value of about 0.5. This indicates that a single simple network would give erroneous results if the other networks were active in the real world. A second experiment on a simulated landscape of 214 farmers revealed further emergent behaviour, with two peaks forming rapidly and the larger peak drifting towards higher values, driven by the peculiar initial spatial configuration in which clusters of small farms remained uninfluential and uninfluenced.

The model’s peculiarity lies in the conjunction of bounded confidence opinion dynamics with the bounded rationality behavioural framework and geographical specificity, together with a multifactorial asymmetrical interaction function unique to this model. In the seminal Deffuant-Weisbuch model, by contrast, the formula governing the interaction is symmetrical and uncertainty is not taken directly into consideration. The asymmetrical function has a double consequence: farmer interactions are independent, so exchanges are not guaranteed to be mutual, and the influence one farmer has on another may differ in magnitude. This allows for the formation of different roles during a simulation, with farmers holding the most connections and low uncertainty playing the role of leaders or nodal farmers. The prototype results, characterised by consensus, polarisation and fragmentation, display non-linearities indicating the emergence of interesting, complex dynamics.

GeSoN has clear limitations at this stage of development. Farmers are the only type of agent represented, although agricultural advisors, food processing industries and institutions may mediate interactions in real scenarios and are planned for future developments. Farmers’ emotions were intentionally left outside the system boundaries, as were the agricultural land market and other sources of influence such as economic and environmental shocks. The continuous representation of opinions also ignores the intricacies related to one’s personal views. Nonetheless, the model presents flexibility and adaptability to be incorporated into more complex simulation systems, and the team plans to integrate it as a module of the ALMaSS agent-based modelling platform, where farmers’ decision-making is handled through the CONSUMAT conceptual framework. The practical payoff could be considerable: ex-ante policy analysis could benefit from the model in predicting farmers’ responses to specific measures, and predicting the adoption rate of voluntary enrolment measures in the EU agricultural policy framework could lead to higher efficiency and effectiveness, avoiding sub-optimal budget allocation when adoption cannot be precisely foreseen. As the model matures within richer simulations, the different configurations of inputs, diverse populations of farmers and varied agricultural landscapes are expected to generate complex emergent properties that can inform the real world.

Subject of Research: Socio-ecological simulation

Article Title: GeSoN: A Geo-Social Network model applying bounded rationality to farmers in socio-ecological simulations

Article References: Paparella, A., Cembalo, L., & Topping, C. (2023). GeSoN: A Geo-Social Network model applying bounded rationality to farmers in socio-ecological simulations. Food and Ecological Systems Modelling Journal, 4, Article e100714. https://doi.org/10.3897/fmj.4.100714

Image Credits: AI Generated

DOI: 10.3897/fmj.4.100714

Keywords: agent-based modelling, bounded rationality, opinion dynamics, socio-ecological systems, agricultural networks, farmer behaviour, gravity model, bounded confidence, GeSoN, Geo-Social, Network, model

The study, published in Humanities and Social Sciences Communications, was led by Tingyu Xiao and colleagues at Southwest Forestry University in Kunming. The team set out to answer a question that has long frustrated mountain ecologists: when a region’s vegetation cover improves over decades, is that improvement stable and durable, or is it a fragile surface trend masking hidden volatility? Their answer, drawn from remote-sensing records spanning 2000 to 2023, is that in this alpine canyon region the two conditions coexist—and that the mismatch between them carries substantial risk.

The researchers focused on fractional vegetation cover, or FVC, a core indicator of ecological health retrieved from a 250-meter normalized difference vegetation index dataset for China. FVC expresses the proportion of the ground, projected vertically, that is covered by leaves, stems, and branches. Using the pixel dichotomy model, each satellite pixel was treated as a mixture of fully vegetated and bare-soil endmembers, with the 2nd and 98th percentiles of each image’s NDVI distribution serving as the soil and vegetation reference values. The result is a continuous map of vegetation density, classified into five levels from very low to very high cover.

On the surface, the news is encouraging. Throughout the 24-year period, areas classified as high or very high vegetation cover together accounted for more than 65 percent of the region. The proportion of very high cover rose markedly, from 27.91 percent in 2000 to 34.50 percent in 2023, and transitions between cover classes occurred mainly between adjacent categories—a gradual expansion of high-quality vegetation rather than wholesale transformation. Spatially, the pattern remained remarkably stable, with extensive contiguous high-value coverage interspersed with localized low-value patches along deeply incised river valleys and near high-altitude margins.

Yet the vertical dimension of the landscape tells a sharper story. Because the region spans an elevation range exceeding 4000 meters, vegetation is strongly constrained by vertical zonation. The belt between 1500 and 3500 meters emerged as the core ecologically active zone: at 2500 to 3500 meters, the very high cover class dominated, accounting for more than half the area. Below 1500 meters, in the dry-hot canyon floors where the foehn effect on leeward slopes generates arid conditions, low and moderate cover increased and the distribution became more heterogeneous. Above 3500 meters, high-grade cover fell away sharply, and above 4500 meters the decline became abrupt—very low cover rose to roughly 90 percent of the area, becoming the dominant type in this extreme alpine zone, where cold and frequent freeze–thaw cycles exceed the tolerance thresholds of most vascular plants.

To capture the temporal character of these changes, the team combined two analytical approaches. The Theil–Sen median slope estimator, paired with the non-parametric Mann–Kendall test, provided the conventional linear trend assessment. But linear models struggle when ecosystems approach or cross ecological thresholds and undergo structural breaks, so the researchers also applied the BFAST01 model—Breaks For Additive Season and Trend—which decomposes each vegetation time series into trend, seasonal, and residual components, fits the seasonal signal with a third-order harmonic model matched to the 16-day MODIS sampling frequency, and then locates significant breakpoints in the trend using an ordinary least squares residuals-based moving sum stability test. The model automatically selects the optimal evolution scheme with the Bayesian information criterion and refines breakpoint trends into ten categories, from monotonic increases to interrupted and reversal patterns.

The comparison between the two methods proved revealing. In identifying stable vegetation areas, the linear and nonlinear approaches agreed exceptionally well, with a 92.65 percent overlap—strong evidence that the underlying analysis is robust. But among the areas that the linear method flagged as significantly increasing, only 18.59 percent represented purely monotonic growth. Roughly 80 percent of the significantly increasing areas actually displayed more complex nonlinear trajectories, such as interrupted increases or reversal-to-increase patterns. In other words, the apparent greening of northwestern Yunnan is substantially unstable: the study estimates a potential degradation risk of 73.3 percent of the study area when combining areas projected to degrade in the future (44.4 percent) with those already in continuous deterioration (28.9 percent), based on coupling the Hurst exponent with Sen’s slope. The Hurst analysis, which quantifies persistence in time series, revealed anti-persistent behavior in much of the region—meaning that areas currently stable or improving may reverse course.

The nonlinear model also recovered signals the linear approach missed. In 46.24 percent of pixels previously classified as significantly declining, BFAST01 detected positive breaks or reversal-to-increase patterns, indicating recent ecological recovery that linear trend analysis responds to only with a lag. The timing of abrupt changes showed a pronounced outbreak between 2009 and 2011, with 2011 recording the highest proportion of affected area at roughly 16 percent—a pattern the authors link to the lagged response of regional vegetation to the extreme drought of 2009–2010. The timing also varied systematically with elevation: high-altitude zones above 3500 meters experienced their dominant breakpoints early, with 2003 as the critical year, consistent with the greater sensitivity of alpine ecosystems to warming signals, whereas low-elevation valley areas below 1500 meters broke late and in concentrated fashion, with 2015 dominant—likely reflecting the cumulative effects of human activities such as slope farmland management and hydropower construction.

Attribution analysis added the study’s most striking quantitative finding. Using the optimal parameters-based geographical detector, or OPGD, which systematically searches five discretisation algorithms across classification intervals of 3 to 15 categories to maximize each factor’s explanatory q value, the team assessed natural and socio-economic drivers over the 2000–2020 window. Net primary productivity displayed the strongest individual explanatory power, with q values consistently exceeding 0.6, followed by temperature, elevation, and potential evapotranspiration. But the explanatory power of night-time light intensity—a satellite proxy for physical development activity such as hydropower construction, road networks, and expanding tourist towns—increased by 87.9 percent over the two decades, while the influence of GDP declined by 22.6 percent. Although socio-economic factors remained individually weak at the macro scale, their interactions with core natural drivers produced pronounced nonlinear enhancement: the interaction between temperature and NPP was the strongest pair, with q values stable between 0.77 and 0.79, and GDP combined with NPP reached 0.73 to 0.74. Human development, the authors conclude, tends to be nested within areas of optimal hydrothermal conditions, where it exerts a disproportionately strong restructuring influence on local vegetation patterns.

The findings carry direct implications for governance. The authors argue that a one-size-fits-all model risks both constraining appropriate development in valley areas and providing insufficient protection for high-altitude sensitive zones, and they propose elevation-differentiated strategies: strengthened development controls and ecological redlines below 1500 meters, a shift from incremental green expansion toward qualitative improvement of existing vegetation stands in the 1500–3500 meter core zone, and nature-based solutions with long-term monitoring above 3500 meters. They caution that the analysis identifies spatial associations rather than direct causality, and that terrain shadow effects and resampling uncertainty remain challenges in the Hengduan Mountains. Still, the central message stands: beneath the surface greening of this alpine canyon region lies a system of high volatility, where rising human pressure interacting nonlinearly with a natural hydrothermal baseline may be quietly pushing vegetation dynamics toward thresholds that conventional monitoring has yet to detect.

Subject of Research: Nonlinear fractional vegetation cover dynamics and their natural and socio-economic drivers in the alpine canyon region of northwestern Yunnan, China

Article Title: Nonlinear vegetation dynamics driven by socio-ecological coupling in the alpine canyon region of northwestern Yunnan, China

Article References: Xiao, T., Ma, Y., Chen, Y., Gui, Y., Zhou, C., & Yi, X. (2026). Nonlinear vegetation dynamics driven by socio-ecological coupling in the alpine canyon region of northwestern Yunnan, China. Humanities and Social Sciences Communications, 13(1), 1783. https://doi.org/10.1057/s41599-026-08776-7

Image Credits: AI Generated

DOI: 10.1057/s41599-026-08776-7

Keywords: vegetation dynamics, remote sensing, alpine ecology, socio-ecological systems, Yunnan, BFAST01, geographical detector, ecosystem resilience, Nonlinear, vegetation, dynamics, driven

The study rests on 22 semi-structured interviews with municipal officials, philanthropic funders, non-governmental organization leaders, Indigenous representatives, and community organizers across Nova Scotia and Newfoundland, supplemented by participant observation at a regional climate adaptation event. Participants were recruited through purposive and snowball sampling, with data collection continuing until thematic saturation was reached. All interviews were recorded, transcribed verbatim, anonymized, and analyzed through an inductive thematic approach, allowing themes such as funding access, policy support, and barriers to collaboration to emerge from the data rather than being imposed in advance. The work received ethical approval from the Grenfell Campus Research Ethics Board and forms part of a larger transdisciplinary project on collaborative governance in financing climate adaptation and mitigation.

Conceptually, the research is anchored in the Socio-Ecological Systems framework, which treats communities not as isolated units of adaptation but as nodes embedded within interconnected social, institutional, and ecological systems. From this perspective, the most effective adaptation strategies emerge from feedback loops between community experience, institutional support, and environmental change. The author also frames the translation of local knowledge as a three-stage process: recognition, in which local knowledge is acknowledged as a valid source of environmental information; valuing, in which it gains real influence over the priorities and design of adaptation plans; and use, in which it is applied alongside scientific tools to concrete planning decisions, such as combining elders’ memories of past shoreline positions with geospatial mapping.

The findings are striking in their consistency: local knowledge is central to building climate resilience, not peripheral to it. Participants described a rich tapestry of knowledge forms, from fishers’ observations of shifting marine species to the Traditional Ecological Knowledge held by First Nations, Inuit, and Métis Peoples, which reflects generations of observation of seasonal change, animal behaviour, and land management. The study also highlights the concept of Two-Eyed Seeing, introduced by Mi’kmaq Elder Albert Marshall, which calls for weaving Indigenous and Western ways of knowing together rather than treating them as competing systems. One participant captured the grassroots ethos succinctly, explaining that the work is not about telling communities what to do but about communities receiving support and encouragement for what they are already doing and want to do.

In practice, this knowledge is being mobilized through four broad categories of adaptation activity. Education and outreach initiatives, including arts communication projects and museum preservation efforts responding to shoreline erosion, rely heavily on cultural knowledge and build public engagement. Community and skills-building efforts, such as public education sessions and community-garden knowledge-sharing, transmit expertise effectively at small scale. Natural infrastructure projects, including living shorelines and regenerative agriculture, showed the deepest integration of traditional ecological knowledge, drawing directly on longstanding local practices. Community planning initiatives, particularly municipal climate advisory committees with formal community representation, demonstrated the strongest institutional uptake. In one municipality, eight community consultation sessions on climate adaptation directly informed local plans, while in another, five of twelve council seats were held in ways that fostered trust and shared responsibility.

Yet the study documents a persistent gap between recognition and substantive use. Systemic barriers, including rigid, short-term funding programs, top-down policy frameworks, and heavy bureaucratic requirements, continue to limit the consistent and equitable application of local knowledge. One participant from a community NGO criticized federal funding structures as formulaic, with everything prescribed in advance and little room for genuine collaboration. A municipal official noted the absence of internal resources to adequately cover climate programs, and another organization described the lack of continuity between short grants as very harmful to progress. Smaller and rural communities face particular disadvantages, forced to compete against large municipalities with dedicated administrative staff for funding opportunities they often lack the capacity to pursue.

Equity concerns surfaced repeatedly. A community foundation representative pointed to Nova Scotia’s history of environmental racism and suggested it extends to adaptation efforts, underscoring the need to embed climate justice principles in planning. Volunteer burnout emerged as another strain on smaller organizations. At the same time, participants identified clear enablers: strong local leadership, grassroots engagement that lets communities define their own priorities, collaborative governance, and flexible funding arrangements. Multi-year funding commitments were described as a game changer, allowing organizations to plan and sustain projects rather than lurching from one grant cycle to the next.

The role of partnerships proved double-edged. Multi-stakeholder platforms connecting municipalities, Indigenous groups, and NGOs were praised for pooling resources and building legitimacy, with one program coordinator noting that such partnerships ensure local knowledge shapes coastal resilience projects. But power dynamics matter enormously. One community organizer observed that input is often sought while final decisions ignore community priorities, such as protecting cultural sites. This distinction between being consulted and having actual influence echoes the scholarly argument that meaningful knowledge co-production requires redistributing decision-making authority, not simply inviting participation. The clearest cases of effective collaboration in the study were precisely those where such redistribution occurred.

Federal policy frameworks received a mixed assessment. Canada’s National Adaptation Strategy was seen as supportive in principle but weak in practice, with one provincial program planner observing that it encourages local knowledge while lacking clear guidelines for implementation. Large funding programs, such as the 41-million-dollar Climate-Resilient Coastal Communities initiative running from 2023 to 2028, provided essential resources, enabling one Nova Scotia municipality to hire local experts to map erosion risks based on community input. But access inequities persist, and rigid use-case restrictions can leave communities unable to pay for what they actually need most: staff and people. By contrast, municipal-level policies establishing climate advisory committees were praised for creating goodwill by giving community members a formal voice in decision-making.

The study concludes that resilience cannot be delivered from the top down; it must be built through collaboration and continuous learning across the system. The author calls for policy shifts to institutionalize long-term, locally controlled funding, formalize community participation in decision-making, and embed equity-centered, co-productive approaches into adaptation frameworks. Without these enabling conditions, the role of local knowledge risks being reduced to a symbolic gesture rather than serving as a core driver of adaptation. The findings carry weight well beyond Atlantic Canada, offering a template for any jurisdiction grappling with how to move beyond token acknowledgment of community expertise toward climate action that is adaptive, context-specific, and grounded in the lived realities of the people who know their places best. As the author notes, the study’s qualitative design offers transferable insight into similar rural and coastal contexts rather than statistically generalizable conclusions, and future research probing how local knowledge shapes the most contentious decisions, such as managed retreat, would be a valuable extension.

Subject of Research: Integration of local and Indigenous knowledge into community climate change adaptation planning in Atlantic Canada

Article Title: Place-based climate adaptation: How Atlantic Canadian communities translate local knowledge into action

Article References: Yirenkyi, E. (2026). Place-based climate adaptation: How Atlantic Canadian communities translate local knowledge into action. PLOS Sustainability and Transformation, 5(10), e0000291. https://doi.org/10.1371/journal.pstr.0000291

Image Credits: AI Generated

DOI: 10.1371/journal.pstr.0000291

Keywords: climate adaptation, local knowledge, Indigenous knowledge, Atlantic Canada, socio-ecological systems, co-production, climate governance, funding, coastal communities, participatory planning, Nova Scotia, Newfoundland

The methodological rigour of the review underpins its findings. By applying PRISMA 2020 guidelines, the researchers ensured that study selection, screening, and analysis followed transparent and reproducible procedures, reducing the risk of bias in a field where evidence has historically been scattered across disciplines and regions. The 59 included studies were examined through thematic synthesis, a qualitative technique that identifies recurring patterns and constructs broader explanatory themes from individual case reports. This approach allowed the team to move beyond cataloguing practices in isolation and instead reveal the structural connections between what Indigenous communities do on the ground and the adaptation outcomes those actions produce. The result is one of the most coherent pictures to date of how traditional forest stewardship translates into measurable climate resilience.

The synthesis identified four dominant forest management strategies employed by Indigenous peoples worldwide. The first centres on Traditional Ecological Knowledge, often abbreviated as TEK, and its associated seasonal practices. TEK encompasses cumulative bodies of knowledge, practice, and belief about the relationships between living beings and their environment, refined over long timeframes through direct observation and experience. Seasonal practices guided by this knowledge allow communities to time harvesting, planting, and other interventions to ecological rhythms, adjusting to variability in ways that static management plans often cannot. The review found that this temporal responsiveness is a key mechanism through which TEK buffers forest systems against climate-driven uncertainty, enabling communities to detect and respond to environmental shifts long before formal monitoring systems register them.

The second strategy is community-based forest management, in which local communities hold collective responsibility for governing and using forest resources. The third is Indigenous fire management and controlled burning, a practice that has attracted global attention in recent years as catastrophic wildfires intensify. Indigenous burning regimes apply fire at carefully chosen times and intensities to reduce fuel loads, maintain habitat heterogeneity, and prevent the destructive conflagrations that unchecked fire suppression policies have sometimes enabled. The fourth strategy comprises agroforestry and sustainable harvesting systems, which integrate trees and crops in ways that diversify production, maintain canopy cover, and preserve soil and water functions. Together, these four strategies demonstrate the breadth of Indigenous practice, spanning fire regimes, tenure arrangements, knowledge systems, and production landscapes.

Perhaps the most consequential finding of the review is that these practices consistently contribute to four key adaptation outcomes. The first is enhanced ecosystem resilience, meaning forests managed under Indigenous systems are better able to absorb disturbances such as drought, pests, and storms without collapsing into degraded states. The second is reduced climate-related risk, as practices like controlled burning directly lower the probability of extreme fire events while seasonal management reduces exposure to crop and resource failures. The third is improved livelihood sustainability, because diversified agroforestry and regulated harvesting provide reliable food, medicine, and income even as climatic conditions shift. The fourth is biodiversity conservation, with Indigenous-managed forests frequently supporting richer assemblages of species than intensively managed or abandoned landscapes. The convergence of these outcomes across independent studies from different regions and ecosystems lends considerable weight to the review’s argument.

The researchers frame their central insight around the concept of a socio-ecological system, a framework in ecology and sustainability science that treats human societies and their environments as a single, tightly coupled entity rather than separate domains. Within this framing, the adaptive capacity of Indigenous forest management does not reside in any single technique. Instead, it emerges from interactions among ecological processes, the cultural values that motivate stewardship, the customary institutions that regulate resource use, and the governance arrangements that grant communities legitimate authority over their territories. This integrated structure explains why removing one component, for example by severing a community’s land rights or disrupting knowledge transmission, can degrade the whole system’s ability to adapt, even if the underlying ecological knowledge remains intact.

The review is also explicit about the conditions that determine whether these systems succeed or falter. Effectiveness, the authors found, is influenced by ecological thresholds, meaning there are limits beyond which climate stress can overwhelm even well-managed systems. Continuity of knowledge transmission matters profoundly, since TEK depends on intergenerational learning between elders and younger community members, a chain that colonisation, urbanisation, and cultural disruption have broken in many places. Secure land tenure emerged as another decisive factor, because communities facing uncertain or contested claims to their forests have weakened incentives and diminished authority to manage them for the long term. Recognition of Indigenous governance authority by state institutions completes the picture, ensuring that community decisions about burning, harvesting, and conservation carry legal weight rather than being overridden by external agencies.

Collaboration between Indigenous knowledge systems and scientific or policy frameworks offers a promising path forward, but the review delivers a cautionary note on how such integration is pursued. When done well, through genuine co-production of knowledge and shared decision-making, collaboration can strengthen adaptation outcomes, combining the fine-grained, place-based sensitivity of TEK with the analytical reach of formal science and the implementation capacity of policy institutions. When done badly, however, tokenistic integration may actively undermine Indigenous institutions and erode the very adaptive capacity it claims to harness. Extractive consultation processes that take knowledge without granting authority, or blanket policies imposed without regard to local governance, can leave communities worse off. The distinction between equitable partnership and superficial inclusion is, in the authors’ assessment, central to the future of forest-based climate adaptation.

The significance of the review extends well beyond academic debate. Indigenous peoples manage extensive forest landscapes worldwide, and the fate of vast carbon stores and biodiversity hotspots is bound up in how those lands are governed. As climate change accelerates, the review positions Indigenous forest management as a place-based, relational, and multi-scalar approach to adaptation, one that operates simultaneously at the level of individual practices, community institutions, and broader governance frameworks. The authors argue that strengthening Indigenous rights, supporting governance autonomy, and promoting equitable knowledge integration are essential prerequisites for effective, just, and sustainable forest-based adaptation. For policymakers searching for scalable climate solutions, the message is that the blueprint may already exist, refined over generations in the world’s forests, and that the task now is to protect, empower, and learn from the communities who hold it.

Subject of Research: Indigenous peoples' forest management practices for climate change adaptation

Article Title: Indigenous peoples’ forest management practices for climate change adaptation: a systematic literature review

Article References: Abas, A., Rahman, A. H. A., Juhari, M. L., Echoh, D. U., & Jamru, L. R. (2026). Indigenous peoples’ forest management practices for climate change adaptation: a systematic literature review. Discover Sustainability. https://doi.org/10.1007/s43621-026-04950-8

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04950-8

Keywords: Indigenous forest management, climate change adaptation, Traditional Ecological Knowledge, community-based forest management, controlled burning, agroforestry, biodiversity conservation, socio-ecological systems, land tenure, forest resilience, PRISMA, sustainable harvesting

The core argument is deceptively simple. Conventional evaluation frameworks treat urban nature-based solutions as collections of measurable assets—a square meter of green roof, a cubic meter of stormwater retention, a degree Celsius of cooling—and then ask whether these assets deliver expected ecosystem services. This asset-based logic fits neatly into cost-benefit analyses, engineering performance metrics, and municipal reporting cycles. But it systematically obscures the relational dimensions of adaptation: who participates in designing and maintaining green infrastructure, how communities attach meaning to restored landscapes, how knowledge flows between residents and planners, and how institutional trust shapes whether interventions are sustained beyond pilot phases. Relationality, in this framing, is not a soft supplement to hard metrics but a constitutive property of urban socio-ecological systems.

Technically, the paper draws on insights from relational ontology and socio-ecological systems theory, fields that view entities not as discrete objects with fixed attributes but as nodes defined by their connections and interactions. In a relational view, a bioswale is not merely soil, vegetation, and drainage capacity; it is an artifact produced through municipal procurement rules, community stewardship agreements, hydrological dynamics, and the ecological history of the site. Evaluating only the physical performance of the bioswale captures a fraction of its adaptive significance. The relational lens asks instead how the intervention reconfigures relationships—between the city and its water, between neighbors who co-maintain a rain garden, between planners and the communities whose neighborhoods are transformed.

This perspective has profound implications for how success is defined. Traditional indicators of nature-based solution performance—runoff reduction, biodiversity indices, temperature moderation—remain important, but they are incomplete. An evaluation framework centered on relationality would additionally assess the quality of participation in planning processes, the distribution of decision-making power, the strength of stewardship networks, and the degree to which interventions strengthen or erode place attachment and social cohesion. These relational outcomes are not incidental benefits; the authors argue they are often the mechanisms through which adaptive capacity is actually built. A city that greens a riverbank without engaging the surrounding community may achieve hydrological targets while leaving its adaptive capacity unchanged or even diminished.

The urgency of this argument reflects real-world patterns in urban adaptation practice. Across many cities, nature-based solutions have been criticized as vehicles of green gentrification, in which environmental improvements raise property values and displace the very residents who are most vulnerable to climate hazards. An asset-based evaluation would record a successful intervention: trees planted, heat island effect reduced, amenity value created. A relational evaluation would ask different questions: whose relationships to the neighborhood were disrupted, which communities gained or lost access to decision-making, and how institutional relationships between city agencies and low-income residents evolved through the process. Only the second framing reveals the equity dynamics that increasingly determine whether adaptation is just.

Relational evaluation also changes the temporal scope of assessment. Conventional metrics are typically measured at project completion or over short monitoring windows, reflecting funding cycles and political timelines. But relational processes—trust building, learning networks, the maturation of stewardship arrangements—unfold over years and decades. Urban forests, for example, accrue their cooling and carbon benefits slowly, and the social institutions that maintain them often take even longer to consolidate. The paper suggests that evaluation must become longitudinal and iterative, treating evaluation not as a terminal audit but as an ongoing dialogue that feeds learning back into governance. In this sense, relationality connects directly to adaptive management: the capacity of a city to learn from its interventions is itself a relational achievement.

Methodologically, the shift toward relationality demands a pluralistic toolkit. Quantitative indicators of social network structure, participation rates, and longitudinal wellbeing surveys can be combined with qualitative approaches—ethnography, participatory mapping, and community-based monitoring—that capture the texture of human-nature relationships. Co-production of evaluation criteria with affected communities is central: rather than importing evaluation frameworks designed by distant experts, relational evaluation begins with the question of whose values and knowledge count. This does not mean abandoning rigor; it means redefining rigor to include validity across multiple ways of knowing, from hydrological modeling to Indigenous and local ecological knowledge.

The implications extend to policy and finance. Global frameworks such as the Sendai Framework for Disaster Risk Reduction, the Paris Agreement’s adaptation goals, and the emerging biodiversity agenda all call for robust adaptation evaluation, yet most national reporting remains anchored in asset-based metrics. If relational outcomes were incorporated into adaptation finance criteria, funders could reward projects that build durable stewardship institutions and equitable governance, not merely those that deliver the cheapest cubic meter of retention. Municipal audit offices, development banks, and philanthropic funders all have leverage to institutionalize relational indicators, and the paper’s argument suggests that failing to do so creates a systematic blind spot in global adaptation accounting.

Ultimately, the argument is a call to recentrate the human and ecological bonds that make adaptation possible. Climate change is often described as a crisis of physics and chemistry, but its impacts and remedies are mediated through relationships: between cities and their watersheds, between institutions and residents, between present decisions and future generations. Urban nature-based solutions offer a rare opportunity to strengthen those relationships while delivering concrete climate benefits. Whether that opportunity is realized depends on whether evaluation evolves from a technical scorecard into a relational practice—one that sees green infrastructure not as a product to be verified, but as a living set of connections to be understood, nurtured, and sustained.

Subject of Research: Relational approaches to evaluating climate adaptation through urban nature-based solutions

Article Title: Relationality must be at the heart of climate adaptation evaluation: insights from urban nature-based solutions

Article References: Goodwin, S., Alda-Vidal, C., Amorim-Maia, A. T., Lewis, W., Loroño, M., & Olazabal, M. (2026). Relationality must be at the heart of climate adaptation evaluation: insights from urban nature-based solutions. npj Urban Sustainability. https://doi.org/10.1038/s42949-026-00460-8

Image Credits: AI Generated

DOI: 10.1038/s42949-026-00460-8

Keywords: climate adaptation, nature-based solutions, urban sustainability, relationality, evaluation frameworks, green infrastructure, socio-ecological systems, climate resilience, urban planning, adaptive governance, green gentrification, community stewardship