Resilience indicates the capacity of a certain resource to resist and absorb the impact of fires (or to avoid being affected by them) and naturally recover the pre-fire value after it occurs [1]. Therefore, resilience is composed of two elements: coping capacity and recovery time. The former can be defined as the resistance of the system to the disturbance and its ability to reinitiate a recovery after the disturbance.
From a social point of view, the concept of resilience is related with the capability of a community to absorb, recover from, or adapt to hazards, including the capability to restore and eventually improve basic functions and structures [2]. A resilient community is one that suffers fewer losses and recovers more quickly after a risk takes place [3]. Resilience assessment comprises both quantitative, qualitative, and quanti-qualitative approaches. Both take into account the dynamic nature of the resilience and the unit of analysis (i.e., the community), as well as the inherent specificity of every community. They consider resilience as a relative feature [4] [5], socially constructed and temporally changing [6]. In the unavoidable absence of critical benchmarks or thresholds, resilience should be assessed in comparison with other similar units of analysis (e.g., neighbouring communities). This is the methodology applied in many studies [7] [8], and is often combined with self-assessment approaches that allow for end-users and stakeholders to contribute their local knowledge to the broader assessment and to interact with statistical data.
From an ecological point of view, resilience can be derived from few plant species strategies allowing a simplified classification in selected plant types [9]: (i) resistors, which include species able to survive the disturbance through their resistance strategies to high temperature, such as bark thickness and wood density or composition (as cork); (ii) tolerators at the individual level, which are highly affected by the disturbance (low resistance) but can easily regenerate by resprouting from canopy buds, or the rooting system (very short regeneration time); (iii) tolerators at the community level, which are highly affected by fire cannot resprout but could deliver resistant seeds in the soil or the canopy (serotiny of coniferous species) with a short regeneration time; and (iv) tolerators at the landscape level, which have none of the previous abilities to resist or regenerate, but developed efficient seed dispersal strategies to regenerate from the closest seeders outside of the disturbance, with a delayed regeneration time. The recovery time can be decomposed into the recovery rate (RCR) and regeneration time (RGT). The RCR is the standard rate of growth of species composing the ecosystem, assuming the presence of regenerative material on the site. It can be theoretically assessed from logistic curves along time [10] or through forest growth models. The RGT is intrinsically linked to regeneration strategies, depending on the size of the fire, which affects the ability of new seedlings to colonise the site [11]. Altogether, the recovery rate and the regeneration onset time will define the recovery time, as the time needed to recover and stabilise at a similar (resilient), lower (vulnerable) or higher (adaptative) state level.
Abiotic factors can affect resistance (RST), RGT and RCR, such as soil available water content (derived from soil texture, depth and rock fragment content) and fertility, or climate conditions (precipitation amount and intensity, temperatures, air humidity, etc.) locally modified by slope and aspects. In addition, time since last fire directly affects tree age and, in turn, RST [12]; and it also affects the viable seed bank availability based on the vital attribute model from Noble and Slatyer (1980)[13] stating that viable seeds are only available when mature individuals are present, or viable seeds could persist in the soil seed bank (seed longevity). In turn, time since last fire (a proxy for tree age for seeders) combined with age of maturity (either of saplings or resprouts) will affect RGT, so that cascading effects might apply with recurrent and short interval fires [14]. Fire size will drive the distance to unburned individuals providing seeds through dispersion (located outside the burn patch) and, in turn, the regeneration time for tolerators at the landscape level. Management strategies (through human-driven plantations after fires) will act as a similar process to widespread seed dispersal in accelerating the regeneration time, as provided in forest management maps [15].
Return to Conceptual Framework Diagram
Ingrisch, J., & Bahn, M. (2018). Towards a Comparable Quantification of Resilience. Trends In Ecology & Evolution, 33, 251-259. ↩︎
Cutter, S.L., Ash, K.D., & Emrich, C.T. (2014). The geographies of community disaster resilience. Global Environmental Change, 29, 65-77. ↩︎
Scherzer, S., Lujala, P., & Rød, J.K. (2019). A community resilience index for Norway: An adaptation of the Baseline Resilience Indicators for Communities (BRIC). International Journal of Disaster Risk Reduction, 36, 101-107. ↩︎
Cutter, S.L. (2016). Resilience to What? Resilience for Whom? The Geographical Journal, 182, 110-113. ↩︎
Fekete, A. (2019). Social Vulnerability (Re-)Assessment in Context to Natural Hazards: Review of the Usefulness of the Spatial Indicator Approach and Investigations of Validation Demands. International Journal of Disaster Risk Science, 10, 220-232. ↩︎
Cutter, S.L. (2018). Linkages between Vulnerability and Resilience. Vulnerability and Resilience to Natural Hazards (pp. 257-270): Cambridge University Press. ↩︎
Cutter, S.L., Boruff, B.J., & Shirley, W.L. (2003). Social Vulnerability to Environmental Hazard. Social Science Quarterly, 84, 242-261. ↩︎
Guillard-Gonçalves, C., Cutter, S.L., Emrich, C.T., & Zêzere, J.L. (2015). Application of Social Vulnerability Index (SoVI) and delineation of natural risk zones in Greater Lisbon, Portugal. Journal of Risk Research, 18, 651-674. ↩︎
Archibald, S., Hempson, G.P., & Lehmann, C. (2019). A unified framework for plant life‐history strategies shaped by fire and herbivory. New Phytologist, 224, 1490-1503. ↩︎
Amani, B.H., N’Guessan, A.E., Van der Meersch, V., Derroire, G., Piponiot, C., Elogne, A.G., Traoré, K., N’Dja, J.K., & Hérault, B. (2022). Lessons from a regional analysis of forest recovery trajectories in West Africa. Environmental Research Letters, 17, 115005. ↩︎
Moghli, A., Santana, V.M., Baeza, M.J., Pastor, E., & Soliveres, S. (2021). Fire recurrence and time since last fire interact to determine the supply of multiple ecosystem services by Mediterranean forests. Ecosystems, 1-13. ↩︎
Hood, S.M., Varner, J.M., Jain, T.B., & Kane, J.M. (2022). A framework for quantifying forest wildfire hazard and fuel treatment effectiveness from stands to landscapes. Fire Ecology, 18, 1-12. ↩︎
Noble, I.R., & Slatyer, R. (1980). The use of vital attributes to predict successional changes in plant communities subject to recurrent disturbances. Vegetatio, 43, 5-21. ↩︎
Ibanez, T., Platt, W.J., Bellingham, P.J., Vieilledent, G., Franklin, J., Martin, P.H., Menkes, C., Pérez-Salicrup, D.R., Russell-Smith, J., & Keppel, G. (2022). Altered cyclone–fire interactions are changing ecosystems, Trends in plant science. ↩︎
Nabuurs, G.-J., Verweij, P., Van Eupen, M., Pérez-Soba, M., Pülzl, H., & Hendriks, K. (2019). Next-generation information to support a sustainable course for European forests. Nature Sustainability, 2, 815-818. ↩︎