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Forest landscapes across western North America (wNA) have experienced extensive changes over the last two centuries, while climatic warming has become a global reality over the last four decades. Resulting interactions between historical increases in forested area and density and recent rapid warming, increasing insect mortality, and wildfire burned areas, are now leading to substantial abrupt landscape alterations. These outcomes are forcing forest planners and managers to identify strategies that can modify future outcomes that are ecologically and/or socially undesirable. Past forest management, including widespread harvest of fire- and climate-tolerant large old trees and old forests, fire exclusion (both Indigenous and lightning ignitions), and highly effective wildfire suppression have contributed to the current state of wNA forests. These practices were successful at meeting short-term demands, but they match poorly to modern realities. Hagmann et al. review a century of observations and multiscale, multi-proxy, research evidence that details widespread changes in forested landscapes and wildfire regimes since the influx of European colonists. Over the preceding 10 millennia, large areas of wNA were already settled and proactively managed with intentional burning by Indigenous tribes. Prichard et al. then review the research on management practices historically applied by Indigenous tribes and currently applied by some managers to intentionally manage forests for resilient conditions. They address 10 questions surrounding the application and relevance of these management practices. Here, we highlight the main findings of both papers and offer recommendations for management. We discuss progress paralysis that often occurs with strict adherence to the precautionary principle; offer insights for dealing with the common problem of irreducible uncertainty and suggestions for reframing management and policy direction; and identify key knowledge gaps and research needs.

Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has emerged as a central focus of landscape ecology and conservation science. Connectivity modelling now encompasses an enormous body of work across ecological theory and application. The dominant connectivity models in use today are based on the framework of ‘landscape resistance’, which is a way of measuring how landscape structure influences movement patterns. However, the simplistic assumptions and high degree of reductionism inherent to the landscape resistance paradigm severely limits the ability of connectivity algorithms to account for many fundamental aspects of animal movement, and thus greatly reduces the effectiveness and relevance of connectivity models for conservation theory and practice. In this paper, we first provide an overview of the development of connectivity modelling and resistance surfaces. We then discuss several key drivers of animal movement which are absent in resistance-based models, with a focus on spatiotemporal variation, human and interspecies interactions, and other context-dependent effects. We look at a range of empirical studies which highlight the strong impact these effects have on movement and connectivity predictions. But we also provide promising avenues of future research to address this: we discuss newly emerging technologies and interdisciplinary work, and look to developing methodologies, models and conversations which move beyond the limiting framework of landscape resistance, so that connectivity models can better reflect the complexities and richness of animal movement.

ContextConnectivity between habitat patches is a recognized conservation action to conserve biodiversity in a rapidly changing world. Resistance surfaces, a spatial representation of cost of movement across the landscape, are often the foundation for connectivity analyses but working with them can be daunting due to the diversity and complexity of software tools.ObjectivesWe present an overview of the steps involved when working with resistance surfaces, identify tools that perform specific tasks, evaluate user experience with the tools, identify needs of the user community, and present some recommendations for users and developers.MethodsWe identified tools applicable at each of the three steps (i) preparing data, (ii) constructing and optimizing surfaces, and (iii) using resistance surfaces. We conducted an online survey of the connectivity user community to assess the popularity and experience with tools on five criteria and identified characteristics important in the selection of connectivity tools.ResultsWe reviewed a total of 43 tools, of which 10 are useful for data preparation, 27 allow construction, and 30 tools that use resistance surfaces. A total of 148 survey participants working in 40 countries were familiar with 37 tools. Tools are ranked heterogeneously for the five criteria. Crucial avenues for future development of connectivity tools identified by respondents are incorporation of uncertainties, dynamic connectivity modelling, and automated parameter optimization.ConclusionsSince resistance surfaces are used for a variety of applications, it is important that users are aware about the appropriate tools. We anticipate that future tools for connectivity research will incorporate more complex and biologically more realistic analytical approaches.

ContextLandscape resistance surfaces are often used to address questions related to movement, dispersal, or population connectivity. However, modeling landscape resistance is complicated by the selection of the most appropriate analytical approach and the assignment of resistance values to landscape features.ObjectivesWe compare three common approaches used in landscape genetics to assign resistance values to landscape features and assess the ability of each approach to correctly identify the data generating resistance surfaces from competing resistance surfaces, as well as the accuracy of each method in recreating the true resistance surface.MethodsUsing simulated genetic data and landscape resistance surfaces, three optimization approaches were evaluated: constrained optimization using reciprocal causal modeling (RCM-CO), constrained optimization using linear mixed effects (MLPE-CO) models, and true optimization using ResistanceGA, which combines MLPE models with a genetic algorithm.ResultsAll methods had low type I error (20% or less) when the simulated surface was continuous, but only MLPE-CO and ResistanceGA had low type I error (10% or less) when the simulated surface was categorical. Error was substantially lower with ResistanceGA than MLPE-CO or RCM-CO for multivariate surfaces. Correlation between true and optimized resistance surfaces was generally high with MLPE-CO and ResistanceGA, but low with RCM-CO.ConclusionsMLPE-based approaches (ResistanceGA and MLPE-CO) were superior to RCM-CO, highlighting their value for landscape genetic analyses. The overall performance, objectivity, and accessibility of ResistanceGA underscore its value as a tool for inferring resistance values from genetic data to better understand how landscapes affect dispersal, movement and population connectivity.

Forest-based scenic spots have received widespread attention for their landscape aesthetics and ecological value, but the rapid growth of tourism and landscape exploitation make it challenging to balance human recreational needs, landscape quality, and ecological stability. This study aimed to evaluate, quantify, and grade landscape quality from the point of view of subjective human aesthetic needs as well as objective landscape visual sensitivity. After the selection of high-quality landscape viewpoints as sources, the minimum cumulative resistance (MCR) model was used to screen the optimal paths for connectivity among viewpoints with consideration of ecological resistance. High-quality landscape viewpoints, optimal paths, and ecological-resistance surfaces constructed the landscape network for sustainable development. The results showed that the landscape quality of viewpoints in Tianmeng Scenic Spot was not good; only 32.4% of these viewpoints had good performance of both landscape aesthetics and landscape visual sensitivity. In the analysis of ecological-resistance surfaces, the proportion of very-high resistance areas and high resistance areas was 32.9%, and these were mostly distributed in the main tourist roads and their buffer areas around the northwest of Tianmeng Mountain Scenic Spot. Eleven landscape core viewpoints and six secondary viewpoints, all with high landscape aesthetics, were selected as high-quality visual landscapes, and then based on the ecological-resistance surfaces, 11 core landscape-dissemination paths and 6 secondary landscape-dissemination paths were identified, respectively, using the MCR model. This method could provide scientific decision support to enhance the effectiveness of viewpoints as well as sustainable landscape planning for development.

Aim: Meta-community structure is a function of both local (site-specific) and regional (landscape-level) ecological factors, and the relative importance of each may be mediated by the dispersal ability of organisms. Here, we used aquatic invertebrate communities to investigate the relationship between local and regional factors in explaining distance decay relationships (DDRs) in fragmented dendritic stream networks. Location: Dryland streams distributed within a 400-km² section of the San Pedro River basin, south-eastern Arizona, USA. Methods: We combined fine-scale local information (flow and habitat characteristics) with regional-scale information to explain DDR patterns in community composition of aquatic invertebrate species with a wide range of dispersal abilities. We used a novel application of a landscape resistance modelling approach (originally developed for landscape genetic studies) that simultaneously assessed the importance of local and regional ecological factors as well as dispersal ability of organisms. Results: We found evidence that both local and regional factors influenced aquatic invertebrate DDRs in dryland stream networks, and the importance of each factor depended on the dispersal capacities of the organisms. Local and weak dispersers were more affected by site-specific factors, intermediate dispersers by landscape-level factors, and strong dispersers showed no discernable pattern. This resulted in a strongly hump-shaped relationship between dispersal ability and landscape-level factors, where only moderate dispersers showed evidence of DDRs. Unlike most other studies of dendritic networks, our results suggest that overland pathways, using perennial réfugia as stepping-stones, might be the main dispersal route in fragmented stream networks. Main conclusions: We suggest that using a combination of landscape and local distance measures can help to unravel meta-community patterns in dendritic systems. Our findings have important conservation implications, such as the need to manage river systems for organisms that span a wide variety of dispersal abilities and local ecological requirements. Our results also highlight the need to preserve perennial réfugia in fragmented networks, as they may ensure the viability of aquatic meta-communities by facilitating dispersal.

Context Genetic connectivity is often disrupted by anthropogenic habitat fragmentation, and therefore often a focus in landscape-scale conservation. Landscape genetics methods allow for studying functional connectivity in heterogenous landscapes in detail and have the potential to inform conservation measures for a species’ regional persistence. Yet, for insects, functional connectivity through landscape genetics remains largely unexplored. Objectives We aimed to assess the functional connectivity in the dune-specialist digger wasp Bembix rostrata , in a human-altered 15 km section along the Belgian coast, based on landscape genetics methods. Methods We optimised landscape resistance distances according to individual genetic distances with the package ResistanceGA . We combined this with a multi-model inference approach to deduce relative conductance or resistance of gene flow to natural and anthropogenic landscape types. Results Overall, the populations of this dune-specialist insect are genetically well-connected. We detected—on top of the prominent background process of isolation-by-distance—a weak but consistent signal of urban features facilitating gene flow. Conclusions Urban areas are not a barrier to genetic connectivity of the dune-specialist insect B. rostrata in the focal human-altered landscape. However, because urbanisation leads to larger scale fragmentation, its impact on the distribution of populations in the landscape and related effective regional gene flow remains substantial. As this species depends on early-succession dune vegetations, restoring and increasing sand dynamics at the local and landscape scale should be the focus of conservation aimed at the regional species’ persistence, rather than trying to increase habitat connectivity in this human-altered dune ecosystem.

CONTEXT: Most current methods to assess connectivity begin with landscape resistance maps. The prevailing resistance models are commonly based on expert opinion and, more recently, on a direct transformation of habitat suitability. However, habitat associations are not necessarily accurate indicators of dispersal, and thus may fail as a surrogate of resistance to movement. Genetic data can provide valuable insights in this respect. OBJECTIVES: We aim at directly comparing the utility of habitat suitability models for estimating landscape resistance versus other approaches based on actual connectivity data. METHODS: We develop a framework to compare landscape resistance models based on (1) a genetic-based multi model optimization and (2) a direct conversion of habitat suitability into landscape resistance. We applied this framework to the endangered brown bear in the Cantabrian Range (NW Spain). RESULTS: We found that the genetic-based optimization produced a resistance model that was more related to species movement than were models produced by direct conversion of habitat suitability. Certain land cover types and transport infrastructures were restrictive factors for species occurrence, but did not appear to impede the brown bear movements that determined observed genetic structure. CONCLUSIONS: In this study case, habitat suitability is not synonymous with permeability for dispersal, and does not seem to provide the best way to estimate actual landscape resistance. We highlight the general utility of this comparative approach to provide a comprehensive and practical assessment of factors involved in species movements, with the final aim of improving the initiatives to enhance landscape connectivity in conservation planning.

1. Evaluating landscape connectivity and identifying and protecting corridors for animal movement have become central challenges in applied ecology and conservation. Currently, resource selection analyses are widely used to focus corridor planning where animal movement is predicted to occur. An animal's behavioural state (e.g. foraging, dispersing) is a significant determinant of resource selection patterns, yet has largely been ignored in connectivity assessments. 2. We review 16 years of connectivity studies employing resource selection analysis to evaluate how researchers have incorporated animal behaviour into corridor planning, and highlight promising new approaches for identifying wildlife corridors. To illustrate the importance of behavioural information in such analyses, we present an empirical case study to test behaviour-specific predictions of connectivity with long-distance dispersal movements of African wild dogs Lycaon pictus. We conclude by recommending strategies for developing more realistic connectivity models for future conservation efforts. 3. Our review indicates that most connectivity studies conflate resource selection with connectivity requirements, which may result in misleading estimates of landscape resistance, and lack validation of proposed connectivity models with movement data. 4. Our case study shows that including only directed movement behaviour when measuring resource selection reveals markedly different, and more accurate, connectivity estimates than a model measuring resource selection independent of behavioural state. 5. Synthesis and applications. Our results, using African wild dogs as a case study, suggest that resource selection analyses that fail to consider an animal's behavioural state may be insufficient in targeting movement pathways and corridors for protection. This failure may result in misidentification of wildlife corridors and misallocation of limited conservation resources. Our findings underscore the need for considering patterns of animal movement in appropriate behavioural contexts to ensure the effective application of resource selection analyses for corridor planning.

Metapopulation ecology and landscape ecology aim to understand how spatial structure influences ecological processes, yet these disciplines address the problem using fundamentally different modeling approaches. Metapopulation models describe how the spatial distribution of patches affects colonization and extinction, but often do not account for the heterogeneity in the landscape between patches. Models in landscape ecology use detailed descriptions of landscape structure, but often without considering colonization and extinction dynamics. We present a novel spatially explicit modeling framework for narrowing the divide between these disciplines to advance understanding of the effects of landscape structure on metapopulation dynamics. Unlike previous efforts, this framework allows for statistical inference on landscape resistance to colonization using empirical data. We demonstrate the approach using 11 yr of data on a threatened amphibian in a desert ecosystem. Occupancy data for Lithobates chiricahuensis (Chiricahua leopard frog) were collected on the Buenos Aires National Wildlife Refuge (BANWR), Arizona, USA from 2007 to 2017 following a reintroduction in 2003. Results indicated that colonization dynamics were influenced by both patch characteristics and landscape structure. Landscape resistance increased with increasing elevation and distance to the nearest streambed. Colonization rate was also influenced by patch quality, with semi-permanent and permanent ponds contributing substantially more to the colonization of neighboring ponds relative to intermittent ponds. Ponds that only hold water intermittently also had the highest extinction rate. Our modeling framework can be widely applied to understand metapopulation dynamics in complex landscapes, particularly in systems in which the environment between habitat patches influences the colonization process.