Geomorphometric Variables Research Articles

Habitat suitability models reveal extensive distribution of deep warm-water coral frameworks in the Red Sea

AbstractDeep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.

Explanation of the influence of geomorphometric variables on the landform classification based on selected areas in Poland

In recent years, automatic image classification methods have significantly progressed, notably black box algorithms such as machine learning and deep learning. Unfortunately, such efforts only focused on improving performance, rather than attempting to explain and interpret how classification models actually operate. This article compares three state-of-the-art algorithms incorporating random forests, gradient boosting and convolutional neural networks for geomorphological mapping. It also attempts to explain how the most effective classifier makes decisions by evaluating which of the geomorphometric variables are most important for automatic mapping and how they affect the classification results using one of the explainable artificial intelligence techniques, namely accumulated local effects (ALE). This method allows us to understand the relationship between predictors and the model’s outcome. For these purposes, eight sheets of the digital geomorphological map of Poland on the scale of 1:100,000 were used as the reference material. The classification results were validated using the holdout method and cross-validation for individual sheets representing different morphogenetic zones. The terrain elevation entropy, absolute elevation, aggregated median elevation and standard deviation of elevation had the greatest impact on the classification results among the 15 geomorphometric variables considered. The ALE analysis was conducted for the XGBoost classifier, which achieved the highest accuracy of 92.8%, ahead of Random Forests at 84% and LightGBM at 73.7% and U-Net at 59.8%. We conclude that automatic classification can support geomorphological mapping only if the geomorphological characteristics in the predicted area are similar to those in the training dataset. The ALE plots allow us to analyze the relationship between geomorphometric variables and landform membership, which helps clarify their role in the classification process.

Towards site-specific management of soil organic carbon: Comparing support vector machine and ordinary kriging approaches based on pedo-geomorphometric factors

Site-specific management of soil organic carbon (SOC) is crucial for cleaner, sustainable agricultural production and climate change mitigation. The Neyshabur plain of northeastern Iran is plagued with spatially variable critically low SOC. Thus, in this study management zones (MZs) were developed for the site-specific management of SOC. Soil samples (0–0.3 m) and digital elevation model-derived geomorphometric variables were collected at 288 locations. Soil pH, clay content, available phosphorus, available potassium, and the vertical distance to channel network significantly (p < 0.05) correlated with SOC. Subsequently, support vector machine (SVM) and ordinary kriging (OK) approaches were used to map the spatial variability and develop MZs based on SOC and its above-listed pedo-geomorphometric covariates. Generally, the coefficient of determination (R2) and root mean square error (RMSE) of OK and SVM models used to interpolate pedo-geomorphometric covariates, were similar. However, the Fuzzy Performance Index (FPI) and Normalized Classification Entropy (NCE) indices suggest that the MZs based on the SVM model (FPI = 0.091, NCE = 0.113) are more accurate compared to those determined using the OK (FPI = 0.135, NCE = 0.164) method. Although the results demonstrate the potential of both OK and SVM approaches for site-specific management of SOC in the Neyshabur plain, the average difference of 0.8 g kg−1 SOC in the SVM approach compared to OK (p < 0.05) was found to be significant, especially along transitional boundaries between MZs. Thus, the SVM-based MZs developed in this study can be used for more accurate site-specific application of organic amendments, mineral fertilizers, and management practices that improve carbon sequestration and sorption of chemical contaminants in the Neyshabur plain of Iran.

Exploring the Spatial Dynamics of Endangered Nothofagus alesandrii and Its Relationship with Geomorphometric Variables and Native Tree Species in a Stand of Its Northern Distribution

Nothofagus alessandrii is an endangered species with limited and fragmented distribution in the Maule coastal forest of central Chile. Understanding the factors and processes that influence the natural growth of this species is crucial for mitigating its ecological vulnerability. The primary objective of this research is to determine the spatial distribution pattern of N. alessandrii and its association with geomorphometric variables (slope, elevation, and exposure), as well as its association with other tree species in a representative forest located in the northernmost natural distribution range of the species. To achieve this, the coordinates (x, y, z) of all N. alessandrii individuals and accompanying tree species, along with their slope, elevation, and exposure, were obtained using a total station. A spatial analysis tool based on distance indices (SADIE) was used to quantify the spatial pattern of N. alessandrii and detect local aggregates, as well as determine the degree of spatial association between pairs of variables. The results showed that N. alessandrii trees had a random distribution pattern and a significant spatial association with the studied geomorphometric variables. An additional significant finding was the lack of spatial association observed between N. alessandrii and the accompanying species. In conclusion, our study provides valuable information on the spatial distribution and ecological correlates of the endangered N. alessandrii in a fragmented forest ecosystem of central Chile. The results highlight the importance of geomorphometric variables in shaping the distribution pattern of the species, which can be used to guide restoration and conservation efforts.

Integrating a UAV-Derived DEM in Object-Based Image Analysis Increases Habitat Classification Accuracy on Coral Reefs

Very shallow coral reefs (&lt;5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products into ecologically relevant habitat maps are still relatively underdeveloped. Here, we demonstrate that incorporating geomorphometric variables (derived from the DEM) in addition to spectral information (derived from the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM and classified through object-based image analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV’s DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.

New geomorphometric variables for non-continuous hillslopes – Assessing the value for digital soil mapping

We present a set of new geomorphometric variables that express landscape position relative to breaklines in hillslopes, and test whether these variables are of value in explaining soil property variation in three study sites in the United States, the Netherlands, and Spain. Underlying this work is the recognition that slope breaks, such as cliff lines, lynchets, and large slumped blocks, are associated with processes that affect soil formation around them. For each study site, we digitized slope breaks and calculated vertical and horizontal distance to the nearest lower and higher slope breaks, as well as the relative position between multiple slope breaks, the slope increase at the next higher slope break, and the slope decrease at the next lower slope break. We then assessed the value of these geomorphometric variables in the prediction of slope properties by simple linear regression. At each study site, models were fitted to existing soil observations using a traditional set of geomorphometric variables, and the traditional set plus our newly developed variables.Model comparison indicated that the new variables substantially improved model fit and reduced model error for the site in the United States (Kansas, n = 100), improved model fit but did not reduce model error for the site in the Netherlands (Limburg, n = 192), and did not solve model overfitting issues for the small dataset in Spain (Malaga, n = 66).

Автоматизированное распознавание генетических типов рельефа Кольского полуострова по морфометрическим данным

The automatic geomorphological mapping based on the Earth’s surface remote sensing data has been developed in recent years. The aim of the research is an attempt of automatic creation of the Kola Peninsula geomorphological map at the morphogenetic legend’s principle. It was gained based on the random forest classification technique. As input data a several geomorphometric variables were used only (the basic ones – elevation, slope angle, curvatures etc., and many those of surface texture). Developing such algorithms for different territories and hierarchical levels of landforms analysis contributes to propagate the techniques of the fast geomorphological mapping. On the training data covering only 1,3 % of the study area with known labels for one of thirteen probable landform types, the reconstruction of geomorphological boundaries and the automatic creation of the geomorphological map were carried out. The accuracy of the resulting map was 81 %. Elevation has the greatest discrimination power according to Kola Peninsula landform types. In addition, some area-based local geomorphometric variables characterizing terrain pattern has big discrimination power. The lowest power relates with the “classical” local-based geomorphometric variables. The results of the work can be used in the development of automated landforms mapping systems at the level of morphogenetic types.

Ambientes de ocorrência do porífero Oncosclera jewelli no Parque Estadual Do Tainhas, Planalto Meridional do RS: uma análise geomorfométrica

O Parque Estadual do Tainhas abrange ambientes de ocorrência do porífero Oncosclera jewelli. Nosso objetivo é indicar as áreas de potencial ocorrência da espécie a partir de fatores relacionados à assinatura geomorfométrica dos pontos de ocorrência ao longo do Rio Tainhas no Parque e sua zona de amortecimento. Foram realizados cruzamentos e análises de dados a partir da construção e manipulação de um banco de dados geográficos, nos SIGs SPRING-5.4.3 e QGIS-2.18, contendo: a) MDEs oriundos das bases Topodata, Embrapa e Alos, obtidos por sensores remotos orbitais para toda a área de estudo, e MDE obtido por imagens aéreas geradas por Drone e as variáveis geomorfométricas produzidas: declividade, aspecto e índice de posição topográfica; b) mapa geológico do Rio Grande do Sul; c) mapa de uso e cobertura do solo, com base em imagens do satélite GeoEye. Os resultados demonstraram que a espécie ocorre exclusivamente sobre a unidade litológica da Formação Serra Geral. O relevo é marcado por terrenos de baixa declividade e orientação sul e leste e em relevo aplainado. Foi gerado o Mapa de Potencial Ocorrência da espécie, demonstrando que aproximadamente 4,5% do comprimento total dos trechos de cursos d’água na área de estudo reúnem as condicionantes geomorfométricas identificadas como de potencial ocorrência da espécie.

New Geomorphometric Variables for Non-Continuous Hillslopes – Assessing the Value for Digital Soil Mapping

New Geomorphometric Variables for Non-Continuous Hillslopes – Assessing the Value for Digital Soil Mapping

Applications in remote sensing and environmental analysis

The present study aims to describe the morphologic and geographic structuring and report a relief morphometric analysis in a cutout of the environmental planning. To do so, geoprocessing techniques were widely used and supported by systemic theory. The main objective is to process relief data in Geographic Information System (GIS) and contribute to the database of characteristics from different local geomorphometric variables. Thus, the theorical basis of the research suggests the use of General System Theory based on their concepts to understand the morphologic structure of the relief at different levels, also the condition of the relief shapes and their classification. This case study occurs methodologically, defining the relief category as an environmental component of direct and vital interaction with other resources and dynamic components, considering mainly the whole environmental system. In this study, the environmental system is the Sucuriú River watershed. Therefore, to get the environmental analysis of the relief, the methodology consists of pre-processing and processing of digital land modeling data based on Synthetic Aperture Radar (SAR) records. Those can obtain different characteristics by using the methodology of geomorphometric variables extraction, which are the set of variables subject to land measuring. The results should cartographically reveal the dynamics and structural morphology of the relief, observing important parameters of relief configuration and concluding with the presentation and correlation of the relief shapes dynamics in all the considered environmental system.

Using geomorphometric approach to investigate spatial pattern and intensity of erosional dissection in a block-faulted topography (Orlickie-Bystrzyckie Mountains, Central Europe)

The paper presents an attempt to recognize areas of enhanced erosional dissection, based on a combined analysis of several geomorphometric variables and using four different approaches (rank-based, cold and hot spot analysis, k-means clustering of ‘raw’ data, and k-means clustering of local measures of spatial autocorrelation). The study area includes the Orlickie-Bystrzyckie Mountains Block (OBMB), which is a block-faulted range within the Sudetes Mountains, Central Europe, typified by fault-generated range fronts, remnant planation surfaces and topographic ramps due to large-scale tilting. Geology is diverse, with basement rocks and sedimentary cover, and the relevant timescale is late Cenozoic. Morphometric variables derived from a LiDAR-based DEM include contour length, standard deviation of elevation, median valley depth and standard deviation of curvature, and are considered to account for various morphometric signatures of erosional dissection. While deriving compound measures of dissection, three grid cell sizes were considered. Within the OBMB several areas were identified as erosionally dissected to a considerable degree. Their spatial pattern is complex and reflects several factors simultaneously. Differential uplift appears as the main trigger, but the erosional response varies, depending mainly on the size of runoff contributing area – itself related to the pattern of uplift, and the distance from the margins of elevated areas. Lithology (bedrock resistance to erosion) seems to play a subordinate role. We also discuss various issues related to the procedures themselves, pointing out their advantages and limitations. Procedural setups proposed in this paper are not site-specific and may be used in various topographic and geological settings.

A geomorphometric model to determine topographic parameters controlling wildfires occurrence in tropical dry forests

Tropical dry forests (TDF) are commonly affected by wildfires, especially during the dry season and drought periods. Many studies have identified ecological, pedological, and climatic variables that control fires ignition as well as other anthropogenic causes. Nonetheless, the intrinsic geomorphometric variables that shape the different TDF landscapes have been underestimated. Hence, we used a Generalized Linear Model (GLM) to study the occurrence of wildfires in a TDF on a TDF located in a protected area at northwest Costa Rica. The influences of nine topographic parameters (TP) were tested on the occurrence of wildfires during a period of 23-years (1997–2020). The statistical analysis demonstrate that Aspect (compass direction that a terrain surface faces) and Wind Exposition (areas degree exposed to wind) are the most influential TP to explain the wildfires occurrence in TDF. The results may be valuable for the understanding and assessment of geomorphometric variables controlling wildfires occurrence in TDF. Therefore, highlighting the importance of fire risk management in ecosystems comprising almost the half of the total of tropical forests proves relevant.

Physically based morphostructural land surface segmentation: Case of the Alps and Western Carpathians

AbstractThe presented morphostructural segmentation of the Alps and Western Carpathians uses physically based geomorphometric variables as input data and object‐based image analysis as a procedure to delineate objects of interest. A previous suitability evaluation of simple object‐based methodology showed its potential in recognition of morphostructural features. When segmenting the Western Carpathians, we moved towards a more specific object‐based approach—a fusion of segmentation and classification at several hierarchical levels. Such an approach was evaluated as not necessary in the less contrasting terrain in the Alps, where only a simple segmentation was used for evaluation of its efficiency. Use of physically based geomorphometric variables as input data brought enhancement of subsequent morphotectonic interpretations. After the segmentations, an index of steady state (ISS) for the entire object levels and their parts was computed. This ISS was used to evaluate and compare the two study areas in terms of their geodynamic development. Similarities and differences in development of these relatively alike mountain ranges are interpreted.

Prioritization and Analysis of Watershed: A Study Applied to Municipal Solid Waste

This paper shows a watershed prioritization analysis applied to municipal solid waste. The study area was the macrowatershed “Cañón del Sumidero”, in the state of Chiapas, Mexico. Geographic information systems, multi-criteria evaluation techniques, as well as several geomorphometric, land use, vegetation and waste management variables were used. The results indicate that, of the set of watersheds analyzed (4 subwatersheds and 80 microwatersheds), only 14 (2 subwatersheds and 12 microwatersheds) have high priority, since they are severely affected by the mismanagement of solid waste. This is also due to the major presence of urban settlements, which are places with different dynamics in terms of population growth, migration, as well as access to infrastructure and services, such as collection and final disposal of waste. Additionally, the incidence of certain biophysical and geomorphometric variables, such as steep slopes, high rainfall and high drainage density, among others, exacerbate the waste-related problems. The remaining watersheds (2 subwatersheds and 68 microwatersheds) showed moderate or low prioritization values because of the low amount of solid waste produced there. Finally, this work concludes that the regionalization of municipalities and the management of solid waste through decentralized operating agencies can help solve solid waste management problems since this approach would permit to delegate non-primary activities from watershed operating agencies to other specialized waste agencies.

Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map

Understanding the spatial distribution of soil nutrients and factors affecting their concentration and availability is crucial for soil fertility management and sustainable land utilization while quantifying factors affecting soil nitrogen distribution in Qorveh-Dehgolan plain is mostly lacking. This study, thus, aimed at digital modeling and mapping the spatial distribution of topsoil total nitrogen (TN) in Qorveh-Dehgolan plain with an area of 150,000 ha using random forest (RF), decision tree (DT), and cubist (CB) algorithms. A total of 130 observation points were collected from a depth of 0 to 30cm from topsoil surfaces based on a random sampling pattern. Then, soil physicochemical properties, calcium carbonate equivalent, organic carbon, and topsoil total nitrogen were measured. A number of 51 environmental variables including 31 geomorphometric attributes derived from a digital elevation model with 12.5-m spatial resolution, 13 spectral indices and reflectance from SENTINEL-2 satellite (MSIsensor), and five soil properties and two spatial variables of latitude and longitude were used as covariates for digital mapping of topsoil total nitrogen. The most appropriate covariates were then selected by the Boruta algorithm in the R software environment. A standard deviation map was produced to show model uncertainty. The covariate selection resulted in the separation of 14 effective covariates in the spatial prediction of topsoil total nitrogen by using the data mining algorithms. The validation of digital mapping of topsoil total nitrogen by RF, DT, and CB models using 20% of independent data showed root mean square error (RMSE) of 0.032, 0.035, and 0.043%; mean absolute error (MAE) of 0.0008, 0.001, and 0.002%; and based on the coefficients of determination of 0.42, 0.38, 0.35, respectively. Relative importance (RI) of environmental covariates using the %IncMSE index indicated the importance of two geomorphometric variables of midslope position and normalized height along with SAVI and NDVI remote sensing variables in the spatial modeling and distribution of total nitrogen in the studied lands. The RF prediction and associated uncertainty maps, with show high accuracy and low standard deviation in the most part of study area, reveled low overfitting and overtraining in soil-landscape modeling; so, this model can lead to the development of a digital map of soil surface properties with acceptable accuracy for sustainable land utilization.

Total phosphorus and climate are equally important predictors of water quality in lakes

Water quality degradation is one of the largest threats to freshwater ecosystems. Nutrient inputs, land use changes, and climate are expected to be the most important drivers of water quality degradation. Here, we quantify the relative influence of nutrient inputs, climate, and lake geomorphometry on primary production in freshwater lakes globally, using chlorophyll a (chla) as a proxy. We used a large lake chlorophyll database that included chla and total phosphorus, in addition to lake geomorphometric variables (mean depth, watershed area, elevation, surface area, volume, residence time) and climate (air temperature, precipitation, cloud cover, solar radiation) for 2561 freshwater lakes around the globe. Our model was able to explain 60% of the variation in chla concentrations. Of that, total phosphorus (TP) explained 42%, a combination of climate variables explained 38%, and geomorphometrics explained 20% of the variation. Although there have been increased efforts and regulations in place for land use and farming, nutrient inputs continue to be the leading cause of primary production in lakes. However, the influence of climatic variables acting synergistically (temperature, precipitation, cloud cover and solar radiation) is nearly equal to that of total phosphorus, suggesting nutrient management efforts are not sufficient alone to mitigate water quality degradation. Our findings underscore the critical need to incorporate climate factors into water quality management given current climate change.

Uncertainty and Overfitting in Fluvial Landform Classification Using Laser Scanned Data and Machine Learning: A Comparison of Pixel and Object-Based Approaches

Floodplains are valuable scenes of water management and nature conservation. A better understanding of their geomorphological characteristic helps to understand the main processes involved. We performed a classification of floodplain forms in a naturally developed area in Hungary using a Digital Terrain Model (DTM) of aerial laser scanning. We derived 60 geomorphometric variables from the DTM and prepared a geomorphological map of 265 forms (crevasse channels, point bars, swales, levees). Random Forest classification was conducted with Recursive Feature Elimination (RFE) on the objects (mean pixel values by forms) and on the pixels of the variables. We also evaluated the classification probabilities (CP), the spatial uncertainties (SU), and the overfitting in the function of the number of the variables. We found that the object-based method had a better performance (95%) than the pixel-based method (78%). RFE helped to identify the most important 13–20 variables, maintaining the high model performance and reducing the overfitting. However, CP and SU were not efficient measures of classification accuracy as they were not in accordance with the class level accuracy metric. Our results help to understand classification results and the specific limits of laser scanned DTMs. This methodology can be useful in geomorphologic mapping.

Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil

Digital elevation model (DEM) data were used with climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. Typically, plantation and individual stand growth and productivity estimates, such as Site Index (SI) and Mean Annual Increment (MAI), are based on field measures of height, tree diameter and age. Using a Random Forest modelling approach, SI and MAI were related to: (i) DEM-based geomorphometric variables and (ii) WorldClim historical macro-climatic measures. Three operational SI classes (high, medium and low productivity) in 180 stands were mapped with an overall accuracy of 91.6%. Medium and high productivity sites were the most accurately classified. Low productivity sites had 76.5% producer’s accuracy and 92.9% user’s accuracy, and were the most extensive in the study area. Such sites are considered of high importance from a plantation management perspective since additional forestry operations are likely required to address low productivity and growth.

Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers

Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.

Mapping of Adaptive Traits Enabled by a High-Density Linkage Map for Lake Trout.

Understanding the genomic basis of adaptative intraspecific phenotypic variation is a central goal in conservation genetics and evolutionary biology. Lake trout (Salvelinus namaycush) are an excellent species for addressing the genetic basis for adaptive variation because they express a striking degree of ecophenotypic variation across their range; however, necessary genomic resources are lacking. Here we utilize recently-developed analytical methods and sequencing technologies to (1) construct a high-density linkage and centromere map for lake trout, (2) identify loci underlying variation in traits that differentiate lake trout ecophenotypes and populations, (3) determine the location of the lake trout sex determination locus, and (4) identify chromosomal homologies between lake trout and other salmonids of varying divergence. The resulting linkage map contains 15,740 single nucleotide polymorphisms (SNPs) mapped to 42 linkage groups, likely representing the 42 lake trout chromosomes. Female and male linkage group lengths ranged from 43.07 to 134.64 centimorgans, and 1.97 to 92.87 centimorgans, respectively. We improved the map by determining coordinates for 41 of 42 centromeres, resulting in a map with 8 metacentric chromosomes and 34 acrocentric or telocentric chromosomes. We use the map to localize the sex determination locus and multiple quantitative trait loci (QTL) associated with intraspecific phenotypic divergence including traits related to growth and body condition, patterns of skin pigmentation, and two composite geomorphometric variables quantifying body shape. Two QTL for the presence of vermiculations and spots mapped with high certainty to an arm of linkage group Sna3, growth related traits mapped to two QTL on linkage groups Sna1 and Sna12, and putative body shape QTL were detected on six separate linkage groups. The sex determination locus was mapped to Sna4 with high confidence. Synteny analysis revealed that lake trout and congener Arctic char (Salvelinus alpinus) are likely differentiated by three or four chromosomal fissions, possibly one chromosomal fusion, and 6 or more large inversions. Combining centromere mapping information with putative inversion coordinates revealed that the majority of detected inversions differentiating lake trout from other salmonids are pericentric and located on acrocentric and telocentric linkage groups. Our results suggest that speciation and adaptive divergence within the genus Salvelinus may have been associated with multiple pericentric inversions occurring primarily on acrocentric and telocentric chromosomes. The linkage map presented here will be a critical resource for advancing conservation oriented genomic research on lake trout and exploring chromosomal evolution within and between salmonid species.