Species distribution models for natural enemies of monarch butterfly (Danaus plexippus) larvae and pupae: distribution patterns and implications for conservation

2016 ◽  
Vol 20 (2) ◽  
pp. 223-237 ◽  
Author(s):  
Shaun M. McCoshum ◽  
Shannon L. Andreoli ◽  
Carl M. Stenoien ◽  
Karen S. Oberhauser ◽  
Kristen A. Baum
Keyword(s):  
Species Distribution ◽  
Natural Enemies ◽  
Species Distribution Models ◽  
Danaus Plexippus ◽  
Distribution Patterns ◽  
Monarch Butterfly ◽  
Distribution Models ◽  
Monarch Butterfly Danaus Plexippus

scholarly journals Monarch Butterfly, Danaus plexippus Linnaeus (Lepidoptera: Nymphalidae: Danainae)

EDIS ◽  
2009 ◽  
Vol 2009 (1) ◽  
Author(s):  
Andrei Sourakov
Keyword(s):  
Life Cycle ◽  
Natural Enemies ◽  
Conservation Status ◽  
Danaus Plexippus ◽  
Monarch Butterfly ◽  
Fact Sheet ◽  
Monarch Butterfly Danaus Plexippus

EENY-442, a 9-page illustrated fact sheet by Andrei Sourakov, is part of the Featured Creatures collection. It describes this well-known migrating butterfly with charismatic appearance, their distribution, description, biology and life cycle, hosts and monarch toxicity, conservation status, and natural enemies. Includes references. Published by the UF Department of Entomology and Nematology, November 2008. EENY-442/IN780: Monarch Butterfly, Danaus plexippus Linnaeus (Lepidoptera: Nymphalidae: Danainae) (ufl.edu)

Understanding the ecological niche to elucidate spatial strategies of the southernmost Tupinambis lizards

2013 ◽  
Vol 34 (4) ◽  
pp. 551-565 ◽  
Author(s):  
Sofía Lanfri ◽  
Valeria Di Cola ◽  
Sergio Naretto ◽  
Margarita Chiaraviglio ◽  
Gabriela Cardozo
Keyword(s):  
Species Distribution ◽  
Evolutionary Biology ◽  
Species Distribution Models ◽  
Regional Scale ◽  
Distribution Patterns ◽  
Niche Differentiation ◽  
Ecological Niches ◽  
Distribution Models ◽  
Interspecific Differences ◽  
Spatial Strategies

Understanding factors that shape ranges of species is central in evolutionary biology. Species distribution models have become important tools to test biogeographical, ecological and evolutionary hypotheses. Moreover, from an ecological and evolutionary perspective, these models help to elucidate the spatial strategies of species at a regional scale. We modelled species distributions of two phylogenetically, geographically and ecologically close Tupinambis species (Teiidae) that occupy the southernmost area of the genus distribution in South America. We hypothesized that similarities between these species might have induced spatial strategies at the species level, such as niche differentiation and divergence of distribution patterns at a regional scale. Using logistic regression and MaxEnt we obtained species distribution models that revealed interspecific differences in habitat requirements, such as environmental temperature, precipitation and altitude. Moreover, the models obtained suggest that although the ecological niches of Tupinambis merianae and T. rufescens are different, these species might co-occur in a large contact zone. We propose that niche plasticity could be the mechanism enabling their co-occurrence. Therefore, the approach used here allowed us to understand the spatial strategies of two Tupinambis lizards at a regional scale.

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.

Sign in / Sign up

Export Citation Format

Share Document