Simulating spatial complexity in dry conifer forest restoration: implications for conservation prioritization and scenario evaluation

Abstract

Context Several initiatives seek to increase the pace and scale of dry forest restoration and fuels reduction to enhance forest resilience to wildfire and other stressors while improving the quality and reliability of key ecosystem services. Ecological effects models are increasingly used to prioritize these efforts at the landscape-scale based on simulated treatment outcomes. Objectives Treatments are often simulated using uniform post-treatment target conditions or proportional changes to baseline forest structure variables, but do not account for the common objective of restoration to mimic the complex forest structure that was present historically which is thought to provide an example of structural conditions that contributed to ecosystem diversity and resilience. Methods We simulate spatially homogenous fire hazard reduction treatments along with heterogeneous restoration treatments in dry conifer forests to investigate how spatial complexity affects ecological indicators of (1) forest structural heterogeneity, (2) forest and watershed vulnerability to high-severity fire, and (3) feasibility of future prescribed fire use. Results Our results suggest that spatially explicit restoration treatments should produce similar wildfire and prescribed fire outcomes as homogeneous fuels reduction treatments, but with greater forest structural heterogeneity. The lack of strong tradeoffs between ecological objectives suggests the primary benefit of spatially complex treatments is to increase forest structural heterogeneity which may promote biodiversity. Conclusions We show that landscape-scale prioritization to maximize ecological benefits can change when spatially complex restoration treatments are modeled. Coupling landscape-scale management simulations and ecological effects models offers flexible decision support for conservation assessment, prioritization, and planning.

Southwestern ponderosa pine forest patterns following wildland fires managed for resource benefit differ from reference landscapes

“…While we initially explored a large suite of landscape metrics, we concentrated on twelve common metrics which easily translated to forest management and silvicultural prescriptions in particular (Reynolds et al 2013;Churchill et al 2013;Rodman et al 2016), were parsimonious in their description of landscape pattern (Cushman et al 2008), and were useful in evaluation of restoration treatment success (Dickinson et al 2016;Ziegler et al 2017;Huffman et al 2017). Our approach to assessing differences across scale allowed us to examine those outcomes and provided some insight into the scales at which management would be necessary to address particular ecological processes and functions (Kerr and Ostrovsky 2003;Uuemaa et al 2013;Wan et al 2020) and could serve to help outline future restoration planning efforts (Churchill et al 2013;Dickinson et al 2016;Cannon et al 2020;Wan et al 2020).…”

“…This discrepancy presents a challenge for understanding reference landscapes of the Southwest at a range of scales and how those landscapes compare to contemporary landscape management. The paucity of potential reference landscapes con rms the urgent need for accelerated large-scale forest management (Stephens et al 2016;Cannon et al 2018), but also highlights the di culties in managing for ecologically functioning, resilient forest landscapes under threat of climate change and competing land uses (Bradford and D'Amato 2012;Stanturf et al 2014;Jacobs et al 2015;Gleason et al 2017;McCauley et al 2019;Cannon et al 2020). We hope to increase the geographic footprint of the study area in future research, but disparate landscapes necessitate accounting for increasingly large amounts of variability in both biotic and abiotic factors contributing to forest structure (Johnston et al 2016;Rodman et al 2017).…”

Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization

“…For example, in the alpine grasslands of the Qinghai–Tibetan Plateau, less than 30% of grassland biodiversity hot spots were found inside protected areas (Su et al, 2019 ). Landscape‐scale simulations can help guide and evaluate the placement of conservation areas with alternative planning scenarios (Cannon et al, 2020 ; Dorning et al, 2015 ). However, there have been few attempts at testing whether biodiversity metrics measured within existing conservation areas are distinct from the broader landscape (but see Thornton et al, 2016 for an example).…”

“…For example, in the alpine grasslands of the Qinghai-Tibetan Plateau, less than 30% of grassland biodiversity hot spots were found inside protected areas (Su et al, 2019). Landscape-scale simulations can help guide and evaluate the placement of conservation areas with alternative planning scenarios (Cannon et al, 2020;Dorning et al, 2015).…”

Causal Bayesian networks in assessments of wildfire risks: Opportunities for ecological risk assessment and management

“…B. Cannon et al, 2020). More complex models could also evaluate the near-term impacts of harvesting on habitat use (Ganey et al, 2017;Irwin et al, 2015) or could capture additional detail in spatial patterns of fuels and burned areas (Collins et al, 2017;Stevens et al, 2017).…”