Landscape Model Research Articles

Genetic structure and landscape effects on gene flow in the Neotropical lizard Norops brasiliensis (Squamata: Dactyloidae)

One key research goal of evolutionary biology is to understand the origin and maintenance of genetic variation. In the Cerrado, the South American savanna located primarily in the Central Brazilian Plateau, many hypotheses have been proposed to explain how landscape features (e.g., geographic distance, river barriers, topographic compartmentalization, and historical climatic fluctuations) have promoted genetic structure by mediating gene flow. Here, we asked whether these landscape features have influenced the genetic structure and differentiation in the lizard species Norops brasiliensis (Squamata: Dactyloidae). To achieve our goal, we used a genetic clustering analysis and estimate an effective migration surface to assess genetic structure in the focal species. Optimized isolation-by-resistance models and a simulation-based approach combined with machine learning (convolutional neural network; CNN) were then used to infer current and historical effects on population genetic structure through 12 unique landscape models. We recovered five geographically distributed populations that are separated by regions of lower-than-expected gene flow. The results of the CNN showed that geographic distance is the sole predictor of genetic variation in N. brasiliensis, and that slope, rivers, and historical climate had no discernible influence on gene flow. Our novel CNN approach was accurate (89.5%) in differentiating each landscape model. CNN and other machine learning approaches are still largely unexplored in landscape genetics studies, representing promising avenues for future research with increasingly accessible genomic datasets.

How will climate change and forest harvesting influence the habitat quality of two culturally salient species?

Boreal landscapes face increasing disturbances which can affect cultural keystone species, i.e. culturally salient species that shape in a major way the cultural identity of a people. Given their importance, the fate of such species should be assessed to be able to act to ensure their perennity. We assessed how climate change and forest harvesting will affect the habitat quality of Rhododendron groenlandicum and Vaccinium angustifolium, two cultural keystone species for many Indigenous peoples in eastern Canada. We used the forest landscape model LANDIS-II in combination with species distribution models to simulate the habitat quality of these two species on the territories of three Indigenous communities according to different climate change and forest harvesting scenarios. Climate-sensitive parameters included wildfire regimes as well as tree growth. Moderate climate change scenarios were associated with an increased proportion of R. groenlandicum and V. angustifolium in the landscape, the latter species also responding positively to severe climate change scenarios. Harvesting had a minimal effect, but slightly decreased the probability of presence of both species where it occurred. According to the modeling results, neither species is at risk under moderate climate change scenarios. However, under severe climate change, R. groenlandicum could decline as the proportion of deciduous trees would increase in the landscape. Climate change mitigation strategies, such as prescribed fires, may be necessary to limit this increase. This would prevent the decrease of R. groenlandicum, as well as contribute to preserve biodiversity and harvestable volumes.

Evolutionary accessibility of random and structured fitness landscapes

Biological evolution can be conceptualized as a search process in the space of gene sequences guided by the fitness landscape, a mapping that assigns a measure of reproductive value to each genotype. Here, we discuss probabilistic models of fitness landscapes with a focus on their evolutionary accessibility, where a path in a fitness landscape is said to be accessible if the fitness values encountered along the path increase monotonically. For uncorrelated (random) landscapes with independent and identically distributed fitness values, the probability of existence of accessible paths between genotypes at a distance linear in the sequence length L becomes nonzero at a nontrivial threshold value of the fitness difference between the initial and final genotypes, which can be explicitly computed for large classes of genotype graphs. The behaviour of uncorrelated random landscapes is contrasted with landscape models that display additional, biologically motivated structural features. In particular, landscapes defined by a tradeoff between adaptation to environmental extremes have been found to display a combinatorially large number of accessible paths to all local fitness maxima. We show that this property is characteristic of a broad class of models that satisfy a certain global constraint, and provide further examples from this class.

Beyond Classroom Walls: High School Foreign Language Learning with Linguistic Landscape Activities

In addition to its widespread use in various disciplines, LL has garnered attention from researchers in education for its role in enhancing language learning and promoting linguistic diversity. However, its systematic integration into language teaching contexts, particularly in high school settings, remains limited. This paper aims to demonstrate how LL-centered activities were designed for integration into an 11th-grade English classroom in a public high school in southeastern Turkey. Over a 12-week period with 32 students, three LL-oriented tasks—Designing a job advertisement, Creating a T-shirt design, and Crafting a ‘Wanted’ sign—all aligned with the curriculum were implemented. Following the stages outlined by the pedagogical model of Linguistic Landscapes in Second Language Teaching and Learning (LLinL2TL), LL activities were systematically integrated. The objective is to provide a comprehensive illustration of the step-by-step integration of LL resources into high school English classrooms, supplemented by sample student works to offer a clear overview of the process. The study concludes by offering recommendations and implications for language education professionals.

The Ecological Design of Marine Urban Green Space Plant Landscaping Based on the Concept of Sustainability.

With global climate change and accelerating urbanization, marine cities face unique environmental challenges. Ecological landscape creation is a form of design planning guided by the disciplines of landscape ecology and ecological aesthetics in the process of urban planning and construction. It seeks a design that can maintain the virtuous cycle of the ecosystem and at the same time maintain the spatial equilibrium of the dynamic development of urban landscapes, so as to make them have good ecological functions and corridor functions. The aim of this study is to explore the ecological design methods of plant landscaping in marine urban green spaces under the concept of sustainability. We first reviewed the concept of sustainable development and its application to urban green space design, especially the special requirements in the marine urban environment. This research focuses on how to select plant species that are adapted to the marine climate and how to promote biodiversity, enhance ecosystem services, and improve the quality of life of urban residents through eco-design approaches. Through the analysis of a number of domestic and international cases of green spaces in marine cities, we found that effective eco-design is not only about choosing the right plant species but also includes the rational management of water resources, soil protection, and ecosystem restoration, among other aspects. This study also points out that public participation and interdisciplinary cooperation play a crucial role in the ecological design process. Finally, this paper carries out a specific analysis of the landscape model landscape evaluation system for the ecological design of plant landscaping in marine urban green spaces and experimentally verifies that, compared with other styles, the experience of the European-style landscape is good overall. However, the view openness rating of the European style landscape is only about 0.42, and the best plant landscaping is the mixed mode of alkali poncho and salt poncho. This study aims to provide a practical reference and guidance for urban planners, landscape architects, and environmentalists.

Urban landscape modeling and algorithms under machine learning and remote sensing data

Urban landscape modeling and algorithms under machine learning and remote sensing data

Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (Dzud) in Mongolian Nomadic Pasturelands

In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level soum districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers retrievable from multi-sensor satellite data. Three alternative methods (principal component analysis, PCA; stepwise multiple regression, SMR; and random forest machine learning model, RF) were used to determine the key drivers for livestock changes and Dzud outbreaks. The countrywide Dzud in 2010 was well-characterized by the PCA as cold with a snowy winter and low summer foraging biomass. The RF estimated the annual livestock change with high accuracy (R2 > 0.9 in most soums). The SMR was less accurate but provided better intuitive insights on the regionality of the key factors and its relationships with local climate and Dzud characteristics. Summer and winter variables appeared to be almost equally important in both models. The primary factors of livestock change and Dzud showed regional patterns: dryness in the south, temperature in the north, and foraging resource in the central and western regions. This study demonstrates a synergistic potential of models and satellite data to understand climate–vegetation–livestock interactions in Mongolian nomadic pastures.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study.

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Ground-penetrating radar investigation of regolith thickness on a periglacial alpine summit flat, Uinta Mountains, Utah, USA

ABSTRACT Summit flats are low-relief, gently sloping landforms common in periglacial mountain environments. Apart from at their edges where summit flats are truncated by glacial headwalls and at their crests where isolated tors are occasionally present, bedrock is typically mantled on a summit flat by a continuous layer of regolith. This study applied ground-penetrating radar (GPR) to survey the thickness of regolith on a summit flat in the Uinta Mountains (Utah, USA). More than 500 m of GPR data were collected along transects extending from the edge of the summit flat to the crest, as well as adjacent to a deep soil pit. Results indicate that the regolith thickness is quite variable, with a mean of 91 ± 38 cm when calculated with an appropriate radar velocity. Because the ground surface of the summit flat is notably smooth, the variability in thickness is a consequence of irregularities in the bedrock surface at depth, which is significantly rougher. Recognition that regolith thickness can vary considerably beneath an alpine summit flat has implications for soil formation, carbon storage, and the transmission and storage of shallow groundwater, as well as evolutionary models for periglacial mountain landscapes.

Modelling foraging strategies of honey bees as agents in a dynamic landscape representation

Introduction: Both intrinsic colony mechanisms and external environmental variables affect the honey bee colony development rates and response and a key aspect of this is the use of resources within the landscape by honey bees. Although several models have been developed to explore the foraging behaviour of bees, none fully considered the spatial and temporal dynamics of landscape resources and the role of the colony in the process. Methodology: Here, we introduce a new honey bee foraging model being developed as a part of the ApisRAM honey bee colony model. Based on agent-based modelling, we used a dynamic ALMaSS landscape model enhanced with floral resource modelling to assess the impacts of weather conditions and resource availability on the possible foraging behaviour of honey bees. Several possible mechanisms (defined, based on honey bee traits) for scouting and foraging were investigated, separately for nectar and pollen collection, including prioritising foraging polygons for nectar foraging according to their distance to the colony, the quality or the energetic efficiency and, for pollen foraging, according to their distance to the colony and pollen quantity. Results: If model foraging bees prioritised the polygons, based on their distance from the colony, the number of unsuccessful flights increased compared to other tested strategies and the total amount of sugar collected showed a high variability. Contrary to expectations, the energetic efficiency strategy did not provide the colony with the highest amount of sugar. Overall, the tested strategies provide different outcomes on the collection of resources, the number of performed flights and their success rate, evidencing that the model's outcome at the colony level arises from the individual types of behaviour. Conclusions and Relevance: Variability in the mass of collected nectar and pollen was found mostly when scout bees applied the distance strategy. This higher variability in sugar collection means that model bees were not able to find the most profitable foraging sites at the landscape level, but only at the local level. Other strategies showed less dependence on the surrounding landscape (i.e. quality or random), but it comes at a cost (i.e. lower production for both nectar and pollen collection). These outputs help us evaluate which strategies could be used for future model development and confirm the models' ability to create dynamic responses. These responses at the colony level were only possible thanks to the implementation of a dynamic landscape model and dynamic spatiotemporal resource model, as well as implementing a social communication mechanism for bees to share information about the resources. Plant nectar production and quality information is essential to predict honey bee foraging distribution. In future model development, the implementation of pollen quality should also be explored to evaluate if it influences the overall pollen collection.

Increased unpredictability in spruce budworm outbreaks following habitat loss and landscape fragmentation

The spruce budworm (SBW) is the insect defoliator responsible for the greatest tree mortality in North America. Fragmentation and loss of habitat are expected to affect the movement of SBW through forested landscapes during periods of dispersal thereby modifying outbreak severity and frequency.We developed a spatially explicit tri-trophic model by combining a forest landscape model and a system of difference equations to evaluate how the interactions between the SBW, its hosts and its parasitoids are disrupted by habitat loss and fragmentation. We designed landscapes with varying quantities of habitat and fragmentation levels to measure their influence on population dynamics and outbreak severity and frequency.Our model simulations show that landscapes with higher proportion of host tree habitat were also characterised by greater SBW average densities, average cumulative defoliation and outbreak severity, which agrees with earlier studies. However, the bottom-up control by tree hosts breaks down as their proportion in the landscape decreased or when they formed isolated patches. Indeed, we found that landscapes with a low proportion of habitat fragmented into relatively large distant patches produced greatest variability in average SBW abundance and cumulative defoliation between simulation runs. In landscapes with higher habitat proportion, increased isolation between patches produced asynchronous outbreaks. Moreover, in these landscapes, parasitoids occupy fewer habitat cells than the SBW, suggesting that they were not able to efficiently track their prey. Together, these results indicate that increased distance between habitat patches can disrupt trophic control of SBW leading to increased unpredictability and asynchrony in outbreak dynamics.For continued effective management of SBW using current deterministic decision-making tools, we recommend prioritising the maintenance of habitat connectivity in forested landscapes.

Implications and impacts of aligning regional agriculture with a healthy diet

One of the most intractable challenges currently facing agricultural systems is the need to produce sufficient food for all to enjoy a healthy balanced diet while minimising impacts to the environment. Balancing these competing goals is especially intractable because most food systems are not locally bounded. This study aims to investigate the likely impacts on production, profit and the environment that result from aligning food systems to a healthy diet, as defined by EAT-Lancet. For this, we consider two distinct areas of the UK, one in East Anglia and the other in South Wales. These two regions reflect different ecosystems and therefore differing specialisations in UK agriculture. We used the Rothamsted Landscape Model (a detailed agroecosystems process-based model) to predict soil carbon dynamics, nutrient flows and crop production for the dominant crops grown in these regions, and the IPCC inventory models to estimate emissions from six livestock systems. Two scenarios were considered, one in which the study regions had to meet healthy diet requirements independently of each other and another in which they could do so collectively. To map their production to healthy diets, both study areas require increases in the production of plant proteins and reductions in the production of red meat. While changes in production can feed more people a healthy diet compared to the business-as-usual state, the overall calories produced reduces dramatically. Emissions and leaching decrease under the healthy diet scenarios and pesticide impacts remain largely unchanged. We show that local infrastructure and environment have a bearing on how “localised” food systems can be without running into substantial constraints. Whilst isolation of the farming system to a regional level, as explored here, is unlikely to be practical, we nevertheless demonstrate that aligning agricultural production towards healthier diets can generate food systems with many associated benefits in terms of agroecosystems' health and resilience to shocks in the food supply chain.

A 2,000‐Year Record of Eelgrass (Zostera marina L.) Colonization Shows Substantial Gains in Blue Carbon Storage and Nutrient Retention

AbstractAssessing historical environmental conditions linked to habitat colonization is important for understanding long‐term resilience and improving conservation and restoration efforts. Such information is lacking for the seagrass Zostera marina, an important foundation species across cold‐temperate coastal areas of the Northern Hemisphere. Here, we reconstructed environmental conditions during the last 14,000 years from sediment cores in two eelgrass (Z. marina) meadows along the Swedish west coast, with the main aims to identify the time frame of seagrass colonization and describe subsequent biogeochemical changes following establishment. Based on vegetation proxies (lipid biomarkers), eelgrass colonization occurred about 2,000 years ago after geomorphological changes that resulted in a shallow, sheltered environment favoring seagrass growth. Seagrass establishment led to up to 20‐ and 24‐fold increases in sedimentary carbon and nitrogen accumulation rates, respectively. This demonstrates the capacity of seagrasses as efficient ecosystem engineers and their role in global change mitigation and adaptation through CO2 removal, and nutrient and sediment retention. By combining regional climate projections and landscape models, we assessed potential climate change effects on seagrass growth, productivity and distribution until 2100. These predictions showed that seagrass meadows are mostly at risk from increased sedimentation and hydrodynamic changes, while the impact from sea level rise alone might be of less importance in the studied area. This study showcases the positive feedback between seagrass colonization and environmental conditions, which holds promise for successful conservation and restoration efforts aimed at supporting climate change mitigation and adaptation, and the provision of several other crucial ecosystem services.

Evaluating the long-term effects of near-natural restoration on post-fire forest dynamics in a wildland-urban interface landscape

Forests in the wildland-urban interface (WUI) are of high value but vulnerable to wildland fires due to abundant fire ignitions and flammable forest fuels. Restoring the post-fire Wildland-Urban Interface (WUI) forest landscape is of utmost importance in order to maintain ecosystem service provision. The near-natural restoration strategy is widely employed in vegetation restoration as it enables the formation of healthy, stable, and diverse artificial mixed forests that resemble natural forests. To evaluate the long-term effects of near-natural restoration on the WUI forest landscape, which are largely unclear, we used a wildfire in 2019 near Shenyang City in northeast China as an example and investigated the post-fire forest dynamics under two different scenarios (i.e., natural succession and near-natural restoration) based on the forest landscape model. The results demonstrated that near-natural restoration can significantly accelerate the restoration process in terms of forest biomass, species biodiversity, and age structure. Under the near-natural restoration scenario, the biomass of the burned area can be quickly restored within 20 years after the fire. At the species level, the biomass and proportion of pioneer tree species such as Pinus tabuliformis and Robinia pseudoacacia decreased under the near-natural restoration scenario, while other species started to increase. Then post-fire near-natural planting accelerated the restoration of forest biodiversity, by 2070, the Shannon–Wiener index was predicted to be 1.49 under natural succession and remained at 2.02 under near-natural restoration. In terms of age structure, near-natural restoration shortens the recovery time of fire trails to mature forests. In summary, near-natural restoration accelerates forest recovery in post-fire WUI areas. Our results highlighted the impact of near-natural restoration on forest conservation to inform post-fire forest planning and management practices.

Diverse mutant selection windows shape spatial heterogeneity in evolving populations.

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.

Overcoming conceptual hurdles to accurately represent trees as cohorts in forest landscape models

Forecasting the response of vegetation to climate change and human actions at global to landscape scales often relies on system models. Many such models represent the composition and demography of vegetation using age class cohorts of species or vegetation types. Forest landscape models (FLM) simulate spatially explicit forest dynamics in response to disturbance and abiotic drivers and constraints at landscape spatial and temporal scales (105 – 108 ha, hundreds of years), and many represent trees as species-age cohorts on a grid of cells to make the model tractable at landscape scales. However, it has become evident that complications arise when calculating cell-level summaries involving multiple cohorts because one cannot assume that all cohorts simultaneously occupy the entire physical space of a cell. A fundamental assumption of a widely used FLM (LANDIS-II) is that cohorts are homogeneously distributed across the grid cells that they occupy. It is only implicitly assumed that the stems (trunks) and crowns (foliage) of the individual trees that make up the cohorts physically occupy some proportion of the cell's area with a non-clustered distribution, but the model does not track those proportions. In reality, when cohort biomass is reduced by disturbance or turnover (self-thinning), gaps are created that are at least temporarily unoccupied. In current versions of LANDIS-II, such gaps are ignored. We describe a new approach for cohort-based FLMs to estimate those proportions (including open space) and apply them to compute more realistic estimates of light attenuation and cell-level estimates of cohort and total cell biomass. This approach accounts for gap dynamics in a FLM where stems are not explicitly modeled, allowing cohorts to partially fill cells. These improvements allow more accurate simulation of forest “gap” dynamics in cohort-based landscape models to produce more accurate absolute biomass estimates at species, cell and landscape scales.

A least-cost network neutral landscape model of human sites and routes

ContextNeutral landscape models generate virtual landscapes that enable computer-based exploration of the effects of spatial patterns on ecological processes free from the restrictions of real-world experimentation. For some questions in landscape ecology it is critical to incorporate human landscape features, such as networks, that are an integral part of human-influenced landscapes.ObjectivesThis paper outlines an approach to produce a neutral landscape model that uses the human geography principle of least-cost movement to create a network of human sites (buildings, camps, mines, settlements, farms, factories, etc.) and routes (trails, roads, railways, canals, powerlines, etc.).MethodsWe used a least-cost modelling framework to create sites prioritised on least-cost catchment areas and routes based on least-cost paths. The location of sites and routes is determined by an underlying cost-surface that defines how movement costs vary across the landscape. The range of possible network patterns was quantified via raster network metrics and was compared to real-world network data.ResultsThe proposed neutral landscape model produces networks with a wide range of possible patterns, and using real-world data can guide the selection of parameters that mimic human activity in a variety of land cover classes in real-world landscapes.ConclusionsThis network neutral landscape model extends the potential of neutral landscape models for research into human-influenced landscapes. We provide the code used to generate our examples under a permissive open-source licence.

Construction of a Waddington-like landscape model that can guide clinical exploration of p53-dynamics-activating parameters in the face of divergent p53 dynamics

The primary strategy of radiotherapies is to manipulate cell fate decision, a process mainly regulated by a spectrum of p53 dynamics. Based on their biological relevance, we analytically categorize the range of p53 levels into seven distinct level-forms, leading to the identification of eleven non-chaotic phenomena of p53 level-form dynamics. The superimposing of cell fate attractors on the co-dimension two bifurcation diagram of eleven p53 level-form dynamics, under quasi-steady state assumption, provides a mechanistic tool that can be posted as a Waddington-like landscape model for cell fate regulation by p53 dynamics. In the proposed model, the (location of) cell is represented as a control point in the bifurcation diagram representing a flattened landscape composed of 11 distinct behavioral regions, and the effort that moves the cell on the landscape is exerted by accumulating death factors upon DNA damage. Further analysis reveals that intrinsically-resistant cancer attractors inevitably exist on the landscape, and cells might have evolved to use a safe operational area whose cusp bifurcation shape is contributing to robustness via hysteresis. We further reveal specific mechanisms through which tumors acquire resistance under therapy. The proposed landscape model can be put to productive use via a reverse control methodology. The reconstruction of cancer-specific landscape can inform the design of personalized p53-dynamics-based drug combination strategies suffering from the combinatorial explosion of target parameters and the divergent p53 dynamics. We conclude that the reverse control of the proposed landscape model has the potential to bridge the gap between theoretical and clinical studies of p53 dynamics for p53-dynamics-based cancer therapies.

Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk

Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density.

Avoiding impacts of phylogenetic tip‐state‐errors on dispersal and extirpation rates in alpine plant biogeography

AbstractAimMany biogeographic analyses require some form of automated state assignment to tips of phylogenetic trees, reflecting a species presence or absence in a particular area, e.g., a biome. As datasets get exponentially larger, such procedures may increasingly induce errors (here called tip‐state‐error), but the specific algorithmic cause and consequence on downstream estimation of dispersal and extinction rates remains poorly known. We aim to improve automated tip‐scoring methods in the context of the alpine biome by leveraging elevation information. We document the profound effect of tip‐state‐errors on Dispersal‐Extirpation‐Cladogenesis (DEC) models.LocationThe European Alpine Arc.TaxonThree thousand three hundred seventeen vascular plant species, emphasizing six focal genera: Campanula, Carex, Festuca, Ranunculus, Saxifraga, and Viola.MethodsWe use GBIF data to classify whether species occur above the upper climatic treeline using a newly developed algorithm ElevDistr or a gridded landscape model of thermal belts, under various filtering thresholds. We compared classification performance using the Flora Alpina as validation data. To determine if tip‐state‐error biases the dispersal and extirpation rate estimation, we fit DEC models for selected clades using tip‐states from different classification models.ResultsElevDistr is less error prone than other approaches. Filtering thresholds lower the false positive rate but increase the false negative rate. Inflated false positive rates bias the dispersal rate estimation upward, while inflated false negative rates lead to upward bias in extirpation rate estimation.Main ConclusionsEven moderate tip‐state‐error may lead to profound systematic bias in dispersal and extinction rate estimation if an unbalanced ratio between false positive and false negative rates occurs. Therefore, careful validation is imperative, though ElevDistr alleviates this problem in the context of the alpine environment. Overall, our results suggest contrasting rates of alpine biome shifts across the studied genera and have major implications for studies addressing the likelihood of niche evolution versus geographic dispersal.