TASSE (Terrain Attribute Selection for Spatial Ecology) Toolbox v. 1.1
Abstract
Toolbox for ArcGIS built from results of Lecours et al. (2017) and validated in Lecours et al. (2016). This toolbox generates six independent terrain attributes that together summarize topographic or bathymetric variability. References: Lecours, V., Devillers, R., Simms, A.E., Lucieer, V.L., and Brown, C.J. (2017) Towards a framework for terrain attribute selection in environmental studies. Environmental Modelling & Software, 89:19-30. Lecours, V., Brown, C.J., Devillers, R., Lucieer, V.L., and Edinger, E.N. (2016) Comparing selections of environmental variables for ecological studies: a focus on terrain attributes. PLoS ONE, 11:e0167128. TO CITE: v. 1.1: Lecours, V. (2017) Terrain Attribute Selection for Spatial Ecology (TASSE) v. 1.0: Lecours, V. (2015) Terrain Attribute Selection for Spatial Ecology (TASSE)
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... The ArcGIS Pro Spatial Analyst extension was used to derive the slope, slope of slope, general curvature, planform curvature, and profile curvature. The Benthic Terrain Modeler (BTM) toolbox in ArcGIS was used to compute the vector ruggedness measure (VRM), fine-scale benthic position index (BPI) using and inner radius of 5 and an outer radius of 25, and broad-scale BPI using an inner radius of 25 and an outer radius of 250 [38]. The TASSE toolbox was used to derive relative distance from mean value (i.e., a measure of relative position), standard deviation (i.e., a measure of rugosity), and northerness and easterness (i.e., non-circular derivatives of aspect, the ...
... orientation of the slope) [34,36,38]. Given the availability of bathymetric data in the sampling universe (Fig 4), depth was limited to the 0 to 600 m range, which encompassed the queen snappers' observed depth range and slightly beyond. ...
Queen snapper (Etelis oculatus) is of interest from an ecological and management perspective as it is the second most landed finfish species (by total pounds) as determined by Puerto Rico commercial landings (2010–2019). As fishing activities progressively expand into deeper waters, it is critical to gather data on deep-sea fish populations to identify essential fish habitats (EFH). In the U.S. Caribbean, the critically data-deficient nature of this species has made this challenging. We investigated the use of ensemble species distribution modeling (ESDM) to predict queen snapper distribution along the coast of Puerto Rico. Using occurrence data and terrain attributes derived from bathymetric datasets at different resolutions, we developed species distribution models unique to each sampling region (west, northeast, and southeast Puerto Rico) using seven different algorithms. Then, we developed ESDM models to analyze fish distribution using the highest-performing algorithms for each region. Model performance was evaluated for each ensemble model, with all depicting ‘excellent’ predictive capability (AUC > 0.8). Additionally, all ensemble models depicted ‘substantial agreement’ (Kappa > 0.7). We then used the models in combination with existing knowledge of the species’ range to produce binary maps of potential queen snapper distributions. Variable importance differed across spatial resolutions of 30 m (west region) and 8 m (northeast and southeast region); however, bathymetry was consistently one of the best predictors of queen snapper suitable habitat. Positive detections showed strong regional patterns localized around large bathymetric features, such as seamounts and ridges. Despite the data-deficient condition of queen snapper population dynamics, these models will help facilitate the analysis of their spatial distribution and habitat preferences at different spatial scales. Our results therefore provide a first step in designing long-term monitoring programs targeting queen snapper, and determining EFH and the general distribution of this species in Puerto Rico.
... All bathymetry-derived morphological features were obtained using the Benthic Terrain Modeller (BTM) (Walbridge et al., 2018) and the Terrain Attribute Selection for Spatial Ecology (TASSE) (Lecours, 2017) toolboxes in ArcGIS Pro® v.2.7. The curvature layers were derived using the curvature tool in ArcGIS Pro® using default parameters. ...
... RF is an ensemble modelling approach that combines the results of (Lecours, 2017). ...
Seafloor habitat maps are an important management tool used to delineate distinct regions of the seabed based on their biophysical properties. Spatially continuous bathymetry and backscatter-derived terrain features are commonly used as proxies for environmental conditions and processes that affect species distributions. Multi-scale approaches are increasingly applied to assess the relevant scales at which species co-occur. As the optimal scale(s) may be unknown, features can be calculated at multiple successive scales, yet this results in numerous highly correlated features that may negatively impact model interpretability. To address this increased dimensionality, feature selection approaches can be used to identify the most relevant features. Here, filter and wrapper approaches are assessed to select features from a highly dimensional multi-scale dataset. Terrain features describing the seabed were calculated across ten scales at two coastal sites in Placentia Bay, Newfoundland, Canada. Five species assemblages were identified using ground-truth underwater video sampling. Features predicting the presence of assemblages were assessed using the two selection methods, and the set of chosen features was modelled using three machine learning algorithms: extreme gradient boosting (XGB), random forest (RF), and support vector machines (SVM). The XGB model with features selected by scale-factor from the Boruta wrapper algorithm had the highest accuracy according to cross-validation- (61.67%, kappa 0.49). Bathymetry and terrain attributes were the most important predictors of assemblage occurrence across various analysis scales encompassing both broader and fine-scale variability of the seabed. The proposed feature reduction and selection approach improved the overall accuracy of predictions, and the resulting biological complexity captured in our habitat maps established baseline data for an ecologically significant coastal region.
... The DEM ( ≈ 0.1 km resolution; Figure 1c) was compiled from General Bathymetric Chart of the Oceans gridded data (GEBCO Compilation Group, 2020), Canadian Hydrographic Service NONNA-100 products (CHS, 2020), and DFO multibeam echosounders data, and standardized to mean water level. Six derivatives were generated using the TASSE (Terrain Attribute Selection for Spatial Ecology) Toolbox (version 1.1; Lecours, 2017): relative deviation from mean value, or RDMV, identifying local peaks and valleys; rugosity, a measure of terrain roughness; local mean; slope; and easterness and northerness, collectively describing slope aspect. According to Lecours et al. (2016), this combination maximizes the amount of information extracted from the terrain while reducing the covariation and redundancy. ...
Numerous spatiotemporal species distribution modeling frameworks are now available to the ecological practitioner. This study compared three such frameworks accessible in the R programming language: generalized additive models with spatiotemporal smooths as implemented by mgcv, spatiotemporal generalized linear mixed models based on nearest neighbor Gaussian processes as implemented by starve, and spatiotemporal generalized linear mixed models based on the stochastic partial differential equations approach as implemented by sdmTMB. The primary focus was to compare the inferences obtained from applying these frameworks to the case study of the orange‐footed sea cucumber, Cucumaria frondosa , on the Scotian Shelf off Nova Scotia, Canada. Each model was fit to catch data (2000–2019) from Fisheries and Oceans Canada's annual Research Vessel and Snow Crab surveys. Environmental covariates were sourced from high‐resolution data layers, including physical oceanographic, bathymetric, and seafloor morphometric datasets. The three models captured variability in sea cucumber distribution that would have been overlooked without a spatiotemporal approach. Although their predictions were similar, including within C. frondosa spatial reserves, the models provided different inferences regarding covariate effects. This suggests that while practitioners primarily interested in mapping species distributions need only apply the most familiar framework, those most concerned with identifying predictive environmental covariates may benefit from comparing the output from multiple approaches. Employing multiple approaches can also serve as a validation technique.
... (TASSE) (Lecours, 2017) toolboxes in ArcGIS pro v.2.7. The curvature layers were derived using the curvature tool in ArcGIS pro using default parameters. ...
Maps of seafloor habitats are important for managing marine areas as they delineate distinct regions of the seabed based on their bio-physical characteristics. Spatially continuous sonar-derived bathymetry and backscatter data, and derivative terrain and textural features are used to predict the distribution of species and communities. Various technical and conceptual methods have been recommended to develop more accurate and informative habitat maps. In support of current literature recognizing the importance of scale in determining species occurrence, Chapter 2 recommends a feature selection method for studies using multiple scales to calculate geomorphic features. Based on this information, full-coverage habitat maps of species assemblages across two coastal sites are predicted. Additionally, Chapter 3 proposes biological traits analysis (BTA) to assess the functional composition of species assemblages, and models continuous maps depicting the spatial distribution of taxonomic and functional diversity metrics. Since current methods to develop habitat maps mainly use a taxonomic approach based on species community composition, a functional traits-based approach assessing a species' behaviour, life-history, and morphology provides a stronger link to broader ecosystem functions for the region. Together, these results are complimentary and provide spatially explicit management tools to support evidence-based decision-making in a changing marine environment.
... Geomorphometry for Ecology tool in TASSE Toolbox v.1.1 is used to derive bathymetric predictors from bathymetric map [73], including slope, curvature [74], eastness, and northness [25,[75][76][77]. Meanwhile, the intensity level (dB) from the backscatter mosaic 32bit was rendered to grayscale values (i.e., backscatter mosaic 8bit). ...
Seagrass beds are important habitats in the marine environment by providing food and shelter to dugongs and sea turtles. Protection and conservation plans require detail spatial distribution of these habitats such as habitat suitability maps. In this study, machine learning techniques were tested by using Multibeam Echo Sounder System (MBES) and ground truth datasets to produce seagrass habitat suitability models at Redang Marine Park. Five bathymetric predictors and seven backscatter predictors from MBES data were used to representing topography features and sediment types in the study area. Three machine learning algorithms; Maximum Entropy (MaxEnt), Random Forests (RF), and Support Vector Machine (SVM) were tested. The results revealed that MaxEnt and RF models achieved the highest accuracy (93% and 91%, respectively) with SVM produced the lowest (67%). Depth was identified as the most significant predictor for all three models. The contributions of backscatter predictors were more central for SVM model. High accuracy models showed that suitable habitat for seagrass is distributed around shallow water areas (<20 m) and between fringing reef habitats. The findings highlight that acoustic data and machine learning are capable to predict how seagrass beds are spatially distributed which provide important information for managing marine resources.
... RDMV and SD were calculated using the Terrain Attribute Selection for Spatial Ecology toolbox (TASSE v.1.1) for ArcGIS (Lecours 2017a), and VRM was calculated using the Benthic Terrain Modeller (BTM) toolbox (Sappington, Longshore, and Thompson 2007;Walbridge et al. 2018), both of which allow for calculation over multiple focal window sizes. Equations for all terrain attribute calculations used in this study are provided in the Appendices. ...
The scale dependence of benthic terrain attributes is well-accepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: i) characterizing scale-specific terrain features, ii) reducing data artefacts and errors, and iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the ‘analysis distance’, was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales.
... A validation analysis was performed against depth values measured by the ROV navigation system and the ROV-mounted CTD instrument. A suite of terrain attributes was derived from the bathymetry using the TASSE toolbox for ArcGIS v1.1 (Lecours, 2017): measures of aspect (i.e., easterness and northerness), relative deviation from mean value (hereafter referred to as topographic position), slope, and standard deviation (hereafter referred to as rugosity). The combination of those measures is considered to optimize the amount of topographic variability that is captured by terrain data . ...
This case study focuses on the characterization of fine-scale habitats associated with cold-water corals in three areas off Eastern Canada. Remotely operated vehicle (ROV)-based video, oceanographic, and bathymetric data were collected in 13 dives ranging from 200 to 3000 m deep at The Gully, the Flemish Cap, and the Orphan Knoll. Maps of potential habitats were produced and spatially compared with different taxa distributions, and species distribution models were computed to quantify the association of different environmental factors with cold-water corals. Results highlighted significant differences in corals' environmental preferences at all taxonomic levels. Results also showed the importance of collecting high-resolution chemical and oceanographic data as their integration with geomorphometric variables (e.g., aspect, rugosity, slope, topographic position) provides a more comprehensive picture of environmental niches. The exploration of cold-water coral habitats helped identify many cases of co-occurrences with a variety of other taxa including many deep-sea sponges.
... The CCGS Amundsen mapped $20 km 2 in the deepest part of the study area in 2007 using a Kongsberg EM300 30 kHz echosounder, and the RV Nuliajuk mapped the remaining area in 2012 and 2013 using an EM3002 300 kHz echosounder, and in 2015 using an EM2040C 200-400 kHz echosounder. Lecours et al. (2017a) suggested a combination of six terrain variables that capture most of the morphological information of a surface, which can be derived from a bathymetric model using the "Terrain Attribute Selection for Spatial Ecology" (TASSE) toolbox (Lecours, 2017; Table 1) in ESRI ArcGIS. In addition to these, we produced eight terrain variables commonly used to describe seabed morphology using the "Benthic Terrain Modeler" (BTM; Walbridge et al., 2018) and spatial analyst toolboxes in ESRI ArcGIS (Table 1). ...
Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence-absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Tenfold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial auto-correlation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.
The Irish Shelf Seabed Geomorphological Map (ISSGM) (v2023) presented here, is the first high-resolution geomorphological map of the entire Irish continental shelf. This large-scale mapping exercise took advantage of the vast INFOMAR multibeam echosounder dataset, and used a protocol of semi-automated mapping techniques to accurately and rapidly extract seabed features. All previous mapping efforts and existing literature on the Irish shallow shelf geomorphology have also been collated and integrated in the map, critically evaluating the previous interpretations. An internationally standardised classification scheme was adopted, aligning the ISSGM (v2023) to other international geomorphological work. At a national level, this detailed geomorphological digital map is intended primarily as a resource to better inform multiple offshore activities and management of the marine environment. The map also acts as a baseline for future studies in marine geomorphology, as it identifies gaps in the knowledge and highlights areas of contentious interpretation that require further work. The map is available online on the Irish Marine Atlas (https://atlas.marine.ie – Geology Theme).
Marine Protected Areas (MPAs) are increasingly established globally as a spatial management tool to aid in conservation and fisheries management objectives. Assessing whether MPAs are having the desired effects on populations requires effective monitoring programs. A cornerstone of an effective monitoring program is an assessment of the statistical power of sampling designs to detect changes when they occur. We present a novel approach to power assessment that combines spatial point process models, integral projection models (IPMs) and sampling simulations to assess the power of different sample designs across a network of MPAs. We focus on the use of remotely operated vehicle (ROV) video cameras as the sampling method, though the results could be extended to other sampling methods. We use empirical data from baseline surveys of an example indicator fish species across three MPAs in California, USA as a case study. Spatial models simulated time series of spatial distributions across sites that accounted for the effects of environmental covariates, while IPMs simulated expected trends over time in abundances and sizes of fish. We tested the power of different levels of sampling effort (i.e., the number of 500‐m ROV transects) and temporal replication (every 1–3 yr) to detect expected post‐MPA changes in fish abundance and biomass. We found that changes in biomass are detectable earlier than changes in abundance. We also found that detectability of MPA effects was higher in sites with higher initial densities. Increasing the sampling effort had a greater effect than increasing sampling frequency on the time taken to achieve high power. High power was best achieved by combining data from multiple sites. Our approach provides a powerful tool to explore the interaction between sampling effort, spatial distributions, population dynamics, and metrics for detecting change in previously fished populations.
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