Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay, China

2018 ◽  
Vol 14 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Ying Xue ◽  
Kisei Tanaka ◽  
Huaming Yu ◽  
Yong Chen ◽  
Lisha Guan ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Prey Abundance ◽  
Distribution Models ◽  
Two Phase ◽  
Haizhou Bay ◽  
New Framework

scholarly journals Spatial distribution of marine macro-litter on the seafloor in the northern Mediterranean Sea: the MEDITS initiative

2020 ◽  
Vol 83 (S1) ◽  
pp. 257 ◽  
Author(s):  
Maria Teresa Spedicato ◽  
Walter Zupa ◽  
Pierluigi Carbonara ◽  
Fabio Fiorentino ◽  
Maria Cristina Follesa ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Generalized Additive Models ◽  
Marine Ecosystem ◽  
Anthropogenic Pollution ◽  
Additive Models ◽  
Spatial Density ◽  
Southern Cyprus ◽  
Local Water ◽  
Global Threat ◽  
Anticyclonic Eddies

Marine litter is one of the main sources of anthropogenic pollution in the marine ecosystem, with plastic representing a global threat. This paper aims to assess the spatial distribution of plastic macro-litter on the seafloor, identifying accumulation hotspots at a northern Mediterranean scale. Density indices (items km–2) from the MEDITS trawl surveys (years 2013-2015) were modelled by generalized additive models using a Delta-type approach and several covariates: latitude, longitude, depth, seafloor slope, surface oceanographic currents and distances from main ports. To set thresholds for the identification of accumulation areas, the percentiles (85th, 90th and 95th) of the plastic spatial density distribution were computed on the raster data. In the northern Mediterranean marine macro-litter was widespread (90.13% of the 1279 surveyed stations), with plastic by far the most recurrent category. The prediction map of the plastic density highlighted accumulation areas (85th, 90th and 95th percentiles of the distribution, respectively, corresponding to 147, 196 and 316 items km–2) in the Gulf of Lions, eastern Corsica, the eastern Adriatic Sea, the Argo-Saronic region and waters around southern Cyprus. Maximum densities were predicted in correspondence to the shallower depths and in proximity to populated areas (distance from the ports). Surface currents and local water circulation with cyclonic and anticyclonic eddies were identified as drivers likely facilitating the sinking to the bottoms of floating debris.

Spatial distribution analysis of the North Pacific spiny dogfish,Squalus suckleyi, in the North Pacific using generalized additive models

2017 ◽  
Vol 26 (6) ◽  
pp. 668-679 ◽  
Author(s):  
Toshikazu Yano ◽  
Seiji Ohshimo ◽  
Minoru Kanaiwa ◽  
Tsutomu Hattori ◽  
Masa-aki Fukuwaka ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
North Pacific ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Spiny Dogfish ◽  
Distribution Analysis ◽  
The North ◽  
The North Pacific

scholarly journals Testing the ability of species distribution models to infer variable importance

2019 ◽  
Author(s):  
Adam B. Smith ◽  
Maria J. Santos
Keyword(s):  
Predictive Accuracy ◽  
Permutation Test ◽  
Generalized Additive Models ◽  
Simulated Data ◽  
Variable Importance ◽  
Additive Models ◽  
Environmental Data ◽  
Boosted Regression Trees ◽  
Distribution Models ◽  
Model Algorithm

AbstractModels of species’ distributions and niches are frequently used to infer the importance of range- and niche-defining variables. However, the degree to which these models can reliably identify important variables and quantify their influence remains unknown. Here we use a series of simulations to explore how well models can 1) discriminate between variables with different influence and 2) calibrate the magnitude of influence relative to an “omniscient” model. To quantify variable importance, we trained generalized additive models (GAMs), Maxent, and boosted regression trees (BRTs) on simulated data and tested their sensitivity to permutations in each predictor. Importance was inferred by calculating the correlation between permuted and unpermuted predictions, and by comparing predictive accuracy of permuted and unpermuted predictions using AUC and the Continuous Boyce Index. In scenarios with one influential and one uninfluential variable, models were unable to discriminate reliably between variables in conditions that are normally challenging for generating accurate predictions: training occurrences <8-64; prevalence >0.5; small spatial extent; environmental data with coarse resolution when spatial autocorrelation is low; and correlation between environmental variables where |r| >0.7. When two variables influenced the distribution equally, importance was underestimated when species had narrow or intermediate niche breadth. Interactions between variables in how they shaped the niche did not affect inferences about their importance. When variables acted unequally, the effect of the stronger variable was overestimated. GAMs and Maxent discriminated between variables more reliably than BRTs, but no algorithm was consistently well-calibrated vis-à-vis the omniscient model. Algorithm-specific measures of importance like Maxent’s change-in-gain metric were less robust than the permutation test. Overall, high predictive accuracy did not connote robust inferential capacity. As a result, requirements for reliably measuring variable importance are likely more stringent than for creating models with high predictive accuracy.

scholarly journals DISTRIBUCIÓN ESPACIAL DE SEDIMENTOS EN LA REGIÓN NORTE DEL CARIBE COLOMBIANO

2016 ◽  
Vol 41 (1) ◽  
Author(s):  
Jorge Paramo ◽  
Luisa Espinosa ◽  
Blanca Posada ◽  
Samuel Núñez ◽  
Seydi Benavides
Keyword(s):  
Spatial Distribution ◽  
Calcium Carbonate ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Caribbean Region ◽  
Sediment Type ◽  
Sediment Samples ◽  
Sediment Types ◽  
Caco3 Content ◽  
Geostatistical Techniques

The spatial distribution of sediments in the continental shelf, their granulometry (phi) and composition (content of calcium carbonate, CaCO3) is described, taking into account the localization (depth, latitude and longitude) to explain their source and distribution and to establish their relationship with the more productive areas in the northern Colombian Caribbean region. Sediment samples were collected in 68 stations during two surveys carried out in December 2005 and February 2006. Granulometry was determined with sieving separation method and medium grains values of sediment (PHI = F) were calculated. Additionally calcium carbonate (CaCO3) contents were determined. Cluster analysis was performed to characterize groups of similar stations in terms of sediment types and sediment type maps were made using geostatistical techniques. Analysis of relationship between sediment types, according to their PHI, with depth, latitude and longitude, and CaCO3 content with sediment types and depth, was made with Generalized Additive Models (GAM). According to spatial distribution of sediments was possible characterize three sectors in agreement with the values of PHI: 1) from Río Buritaca to Río Camarones with fine sands and muds, 2) from Riohacha to Cabo de la Vela with very coarse sands and sands, 3) from Cabo de la Vela to Puerto Estrella with fine sands and muds.

scholarly journals Blowing in the wind? The effect of weather on the intensity and spatial distribution of crime

2019 ◽  
Author(s):  
Rannveig Hart ◽  
Willy Pedersen ◽  
Torbjørn Skardhamar
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Negative Binomial ◽  
Generalized Additive Models ◽  
Spatial Models ◽  
Weather Conditions ◽  
Additive Models ◽  
Extensive Literature ◽  
Weather Effects ◽  
Count Models

Despite an extensive literature on weather and crime, the magnitude of weather effects on crime and their implications for practical policing remain unclear. Similarly, the effects of weather on the location of crime have barely been explored empirically. We investigated how weather influences the intensity and spatial distribution of crime in Oslo, the capital of Norway. Geocoded locations of criminal offences were combined with data on temperature, wind, and rain. We used negative binomial count models to assess the effect of weather on the intensity of crime and generalized additive models (GAMs) to test for spatial variations. The intensity and spatial distribution of crime were not very sensitive to weather in Oslo. The largest effect was for drug crimes, for which maximum relative to minimum temperature was related to a single incident increase every six days. No effects were found for dislocation in the spatial models. In Oslo, Norway, weather conditions are of little importance for practical policing. The effects of weather on the intensity of crime are miniscule, and effects on the location of crime even smaller.

scholarly journals Predicting suitable coastal habitat for sei whales, southern right whales and dolphins around the Falkland Islands

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244068
Author(s):  
Mick Baines ◽  
Caroline R. Weir
Keyword(s):  
Anthropogenic Activities ◽  
Generalized Additive Models ◽  
Habitat Preferences ◽  
Additive Models ◽  
Falkland Islands ◽  
Coastal Habitat ◽  
Distribution Models ◽  
Cetacean Species ◽  
Right Whales ◽  
Southern Right Whales

Species distribution models (SDMs) are valuable tools for describing the occurrence of species and predicting suitable habitats. This study used generalized additive models (GAMs) and MaxEnt models to predict the relative densities of four cetacean species (sei whale Balaeanoptera borealis, southern right whale Eubalaena australis, Peale’s dolphin Lagenorhynchus australis, and Commerson’s dolphin Cephalorhynchus commersonii) in neritic waters (≤100 m depth) around the Falkland Islands, using boat survey data collected over three seasons (2017–2019). The model predictor variables (PVs) included remotely sensed environmental variables (sea surface temperature, SST, and chlorophyll-a concentration) and static geographical variables (e.g. water depth, distance to shore, slope). The GAM results explained 35 to 41% of the total deviance for sei whale, combined sei whales and unidentified large baleen whales, and Commerson’s dolphins, but only 17% of the deviance for Peale’s dolphins. The MaxEnt models for all species had low to moderate discriminatory power. The relative density of sei whales increased with SST in both models, and their predicted distribution was widespread across the inner shelf which is consistent with the use of Falklands’ waters as a coastal summer feeding ground. Peale’s dolphins and Commerson’s dolphins were largely sympatric across the study area. However, the relative densities of Commerson’s dolphins were generally predicted to be higher in nearshore, semi-enclosed, waters compared with Peale’s dolphins, suggesting some habitat partitioning. The models for southern right whales performed poorly and the results were not considered meaningful, perhaps due to this species exhibiting fewer strong habitat preferences around the Falklands. The modelling results are applicable to marine spatial planning to identify where the occurrence of cetacean species and anthropogenic activities may most overlap. Additionally, the results can inform the process of delineating a potential Key Biodiversity Area for sei whales in the Falkland Islands.

scholarly journals High Resolution Spatial Distribution for the Hexactinellid Sponges Asconema setubalense and Pheronema carpenteri in the Central Cantabrian Sea

2021 ◽  
Vol 8 ◽  
Author(s):  
Augusto Rodríguez-Basalo ◽  
Elena Prado ◽  
Francisco Sánchez ◽  
Pilar Ríos ◽  
María Gómez-Ballesteros ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Model Comparison ◽  
Generalized Additive Models ◽  
Additive Models ◽  
The Other ◽  
Distribution Model ◽  
Entropy Model ◽  
Maxent Model ◽  
The One

In the present work we focus on the distribution of two species of sponges. One of these is Asconema setubalense, a sponge found in rocky substrate that was sampled with a photogrammetric vehicle through georeferenced images. The other is Pheronema carpenteri, which inhabits soft bottoms and was sampled by beam trawl. For the spatial distribution modeling of both sponges, the geomorphological variables of depth, slope, broad and fine scale bathymetric position index (BPI), aspect, and types of bottoms were used, all with a resolution of 32 m. Additionally, layers of silicates and currents near the bottom were extracted from Copernicus Marine Environment Monitoring Service (CMEMS), with a resolution of ∼4 and ∼9 km, respectively. Due to the low resolution of the layers, it was considered necessary to validate their use by model comparison, where those that included these variables turned out to be more explanatory than the others. The models were developed in a complex continental break of the Central Cantabrian Sea, which comprises several submarine canyons and a seamount (Le Danois Bank). On the one hand, a very high resolution (32 m) spatial distribution model based on A. setubalense presence was developed using the MaxEnt maximum entropy model. On the other, depending on the availability of density data, generalized additive models (GAMs) were developed for P. carpenteri distribution, although in this case the sampler only allowed a maximum resolution of almost 1 Km. For the A. setubalense, the variables that best explained their distribution were ground types and depth, and for P. carpenteri, silicates, slope, northness, and eastward seawater velocity. The final model scores obtained were an AUC of 0.98 for the MaxEnt model, and an R squared of 0.87 for the GAM model.

Sign in / Sign up

Export Citation Format

Share Document