scholarly journals Distribution of Five Aquatic Plants Native to South America and Invasive Elsewhere under Current Climate

Ecologies ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 27-42
Author(s):  
Vanessa Lozano
Keyword(s):  
Aquatic Plants ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Temperate Climate ◽  
Ecological Niche Models ◽  
Current Climate ◽  
Winter Temperatures ◽  
Invasive Aquatic Plants ◽  
Aquatic Invasion ◽  
Effective Use

Biological invasions and climate pose two of the most important challenges facing global biodiversity. Certainly, climate change may intensify the impacts of invasion by allowing invasive plants to increase in abundance and further expand their ranges. For example, most aquatic alien plants in temperate climate are of tropical and subtropical origins and the northern limits of their ranges are generally determined by minimum winter temperatures, and they will probably expand their distributions northwards if climate warms. The distribution of five invasive aquatic plants in freshwater systems across continents were investigated. Their global distributions in the current climate were modeled using a recently developed ensemble species distribution model approach, specifically designed to account for dispersal constraints on the distributions of range-expanding species. It was found that the species appear capable of substantial range expansion, and that low winter temperature is the strongest factor limiting their invasion. These findings can be used to identify areas at risk of recently introduction of neophytes, and develop future monitoring programs for aquatic ecosystems, prioritizing control efforts, which enables the effective use of ecological niche models to forecast aquatic invasion in other geographic regions.

Potential Distribution of Nysius simulans (Hemiptera: Lygaeidae) in Soybean Crops in South America Under Current and Future Climate

2020 ◽  
Vol 113 (4) ◽  
pp. 1702-1710
Author(s):  
Alexandre Silva de Paula ◽  
Carlos Barreto
Keyword(s):  
Climate Change ◽  
South America ◽  
Potential Distribution ◽  
Species Distribution Model ◽  
Distribution Range ◽  
Range Shifts ◽  
Distribution Model ◽  
Natural Occurrence ◽  
Ecological Niche Models ◽  
Distribution Ranges

Abstract Nysius simulans (Stål) is a suctorial, fluid feeding herbivore that can transmit toxins and spread pathogens via saliva and is an economically important pest for soybean in South America. Currently, N. simulans in soybean is predominantly found in Argentina, but future changes in the distribution from both dispersal and range shifts due to climate change may affect soybean cultivation in southern South America. We developed a species distribution model to examine the distribution range of N. simulans. We compared the potential distribution of N. simulans under current and future projected climatic conditions in order to identify future areas of natural occurrence with ecological niche models using Maxent. Current records of N. simulans show that while the species is present in Argentina, and some areas of Brazil, Paraguay, Peru, and Uruguay, our models suggest that many new suitable areas will be available for N. simulans under climate change including other regions of Argentina, and southern Chile. Our results also predict potential future range shifts and distributions into Bolivia, but not Peru nor Brazil. In our model, seasonal trends in temperature were shown to have the greatest contribution to the potential distribution, whereas isothermality (i.e., temperature variability) was correlated to potential future distribution ranges. We conclude that current populations of N. simulans may be expanding its distribution range by diffusion (i.e., range expansion over generations at the margins of populations), and regions with potential future N. simulans distribution should be closely monitored.

scholarly journals Predicting multi-species foraging hotspots for marine turtles in the Gulf of Mexico

2020 ◽  
Vol 43 ◽  
pp. 253-266
Author(s):  
I Fujisaki ◽  
KM Hart ◽  
D Bucklin ◽  
AR Iverson ◽  
C Rubio ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Gulf Of Mexico ◽  
Species Distribution Model ◽  
Marine Turtle ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Ecological Niche Models ◽  
Turtle Species ◽  
Marine Habitats ◽  
Omission Rate

Quantifying the distribution of animals and identifying underlying characteristics that define suitable habitat are essential for effective conservation of free-ranging species. Prioritizing areas for conservation is important in managing a geographic extent that has a high level of disturbance and limited conservation resources. We examined the potential use of a species distribution model ensemble for multi-species conservation in marine habitats. Using satellite telemetry locations during foraging as input data, and ensemble ecological niche models, we predicted foraging areas for 2 nesting marine turtle species within the Gulf of Mexico (GoM): Kemp’s ridley Lepidochelys kempii (n = 63) and loggerhead Caretta caretta (n = 63). We considered 7 geophysical, biological, and climatic variables and compared contributing factors for each species’ foraging habitat selection. For both species, predicted suitable foraging habitats encompassed large areas along the GoM coast, but only intersected with each other in relatively small areas. Highly parameterized models resulted in overall greater fits, suggesting that multiple factors influence habitat selection by these species. Model validation results were mixed: cross-validation resulted in high prediction accuracy for both species, but an evaluation against independent data resulted in a low omission rate (5%) for Kemp’s ridleys and a high omission rate (72%) for loggerheads. The relatively small intersection of model-predicted foraging areas for these 2 species within the study area may indicate possible niche differentiations. The high omission rate for loggerheads indicates our samples likely underrepresent the population and illustrates the challenges in predicting suitable foraging extents for species that make dynamic movements and have greater individual variability.

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
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