Predicting the distribution of a portunid crab in Patagonian coastal waters

2020 ◽  
Vol 638 ◽  
pp. 95-105 ◽  
Author(s):  
P de la Barra ◽  
G Svendsen ◽  
MA Romero ◽  
MS Avaca ◽  
M Narvarte
Keyword(s):  
Species Distribution Model ◽  
Habitat Preferences ◽  
Silty Sand ◽  
Distribution Model ◽  
Commercial Fishing ◽  
Depth Range ◽  
Prey Biomass ◽  
Portunid Crab ◽  
Crab Density ◽  
Benthic Survey

Ovalipes trimaculatus is a commercially important, cosmopolitan portunid crab. However, environmental conditions that drive its distribution have never been studied. Thus, we aimed to assess the habitat preferences of this species in northern Patagonia by developing a species distribution model. We obtained spatial quantitative data of the crab and its prey species from a benthic survey performed prior to commercial fishing in the area. We used measurements of environmental condition, biomass, richness and evenness of its prey as predictors, and modeled the density of O. trimaculatus through a delta-gamma model, and produced a map of its predicted biomass. Prey biomass, depth and substrate type were the most important variables for predicting the distribution of O. trimaculatus. The model predicts a restricted depth range, with maximum crab density at 10 m (the shallowest depth sampled) in bottom substrates of relatively fine granulometry, such as silty sand and silt. Prey biomass does not determine crab presence, but, where the crab is present, higher prey biomass correlates with higher crab abundance. Our results accurately predicted 1 of the 2 actual fishing grounds; hence, the model may be a useful tool for local fishers and managers.

scholarly journals Population biology and ecology of the endangered Euphorbia susannae Marloth, an endemic to the Little Karoo, South Africa

2021 ◽  
Vol 22 (10) ◽  
Author(s):  
Laaiqah Jabar ◽  
Stefan John Siebert ◽  
Michele Pfab ◽  
Dirk Cilliers
Keyword(s):  
South Africa ◽  
Species Distribution ◽  
Population Biology ◽  
Species Distribution Model ◽  
Habitat Preferences ◽  
Distribution Model ◽  
Nurse Plants ◽  
Populations At Risk ◽  
In The Wild

Abstract. Jabar L, Siebert SJ, Pfab MF, Cilliers DP. 2021. Population biology and ecology of the endangered Euphorbia susannae Marloth, an endemic to the Little Karoo, South Africa. Biodiversitas 22: 4583-4596. Many euphorbias in the semi-arid parts of South Africa are restricted edaphic specialists with small populations at risk of extinction. Euphorbia susannae is one such species, which grows on the edges of quartz patches along a section of the northern foot slopes of the Langeberg Mountains. This study set out to acquire data on the biology and ecology of the species during a first-ever comprehensive field survey. The resultant dataset allowed for the determination of the geographic distribution of the species, as well as the size and number of populations. Biotic and abiotic environmental variables were employed to generate a habitat profile and a species distribution model. The population structure, regeneration potential, and stability of each subpopulation and the population were also assessed. Euphorbia susannae is a range-restricted species (EOO 170 km2 and AOO 36 km2) confined to eight subpopulations that vary considerably in size. According to this study, 1845 individuals remain in the wild. Habitat preferences of the species were considered in detail and linked to a species distribution model for conservation purposes. It was shown that the species preferred nurse plants. The smallest adults size class was the largest cohort in all the sub-populations and was ascribed to pulse recruitments after an unknown favorable event. Although individuals were not evenly distributed among the size classes, annual recruitments levels were healthy, suggesting good pollination, seed set, and germination conditions.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Billy Joel M. Almarinez ◽  
Mary Jane A. Fadri ◽  
Richard Lasina ◽  
Mary Angelique A. Tavera ◽  
Thaddeus M. Carvajal ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Habitat Suitability ◽  
Species Distribution Model ◽  
Biological Control Agent ◽  
The Philippines ◽  
Distribution Model ◽  
Field Surveys ◽  
Probability Of Occurrence ◽  
Precipitation Seasonality ◽  
Bioclimatic Variables

Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

Modeling Habitat Suitability and Management Options for Maintaining Round Whitefish (Prosopium cylindraceum) in Adirondack Ponds

2021 ◽  
Author(s):  
Amy Kathleen Conley ◽  
Matthew D. Schlesinger ◽  
James G. Daley ◽  
Lisa K. Holst ◽  
Timothy G. Howard
Keyword(s):  
Species Distribution Model ◽  
Management Strategies ◽  
Distribution Model ◽  
Nonnative Species ◽  
Simulated Population ◽  
Management Options ◽  
The Past ◽  
Almost All ◽  
Reintroduction Success ◽  
Maximum Air Temperature

Habitat loss, acid precipitation, and nonnative species have drastically reduced the number of Adirondack waterbodies occupied by round whitefish (Prosopium cylindraceum). The goal of this study was to 1) increase the probability of reintroduction success by modeling the suitability of ponds for reintroduction and 2) better understand the effects of different rates of pond reclamation. We created a species distribution model that identified 70 waterbodies that were physically similar to occupied ponds. The most influential variables for describing round whitefish habitat included trophic, temperature, and alkalinity classes; waterbody maximum depth; maximum air temperature; and surrounding soil texture and impervious surface. Next, we simulated population dynamics under a variety of treatment scenarios and compared the probability of complete extirpation using a modified Markov model. Under almost all management strategies, and under pressure from nonnative competitors like that observed in the past 30 years, the number of occupied ponds will decline over the next 100 years. However, restoring one pond every 3 years would result in a 99% chance of round whitefish persistence after 100 years.

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