High-resolution species-distribution model based on systematic sampling and indirect observations

2016 ◽  
Vol 26 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Oded Nezer ◽  
Shirli Bar-David ◽  
Tomer Gueta ◽  
Yohay Carmel
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Systematic Sampling ◽  
Model Based

Performance of a species distribution model based on presence/absence for various population sizes in the amphidromous fish species Sicyopterus japonicus

2013 ◽  
Vol 61 (1) ◽  
pp. 90-96 ◽  
Author(s):  
Norio Onikura ◽  
Ryutei Inui ◽  
Shin Oikawa ◽  
Tomonori Kawamoto
Keyword(s):  
Fish Species ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Model Based ◽  
Population Sizes

scholarly journals Once upon a time in the far south: Influence of local drivers and functional traits on plant invasion in the harsh sub-Antarctic islands

2020 ◽  
Author(s):  
Manuele Bazzichetto ◽  
François Massol ◽  
Marta Carboni ◽  
Jonathan Lenoir ◽  
Lembrechts Jonas J ◽  
...  
Keyword(s):  
High Resolution ◽  
Alien Species ◽  
Species Distribution ◽  
Plant Invasion ◽  
Species Distribution Model ◽  
Propagule Pressure ◽  
Distribution Model ◽  
Distribution Models ◽  
Related Factors ◽  
Alien Plant

AbstractAimHere, we aim to: (i) investigate the local effect of environmental and human-related factors on alien plant invasion in sub-Antarctic islands; (ii) explore the relationship between alien species features and their dependence on anthropogenic propagule pressure; and (iii) unravel key traits conferring invasiveness in the sub-Antarctic.LocationPossession Island, Crozet archipelago (French sub-Antarctic islands).TaxonNon-native vascular plants (Poaceae, Caryophyllaceae, Juncaceae).MethodsSingle-species distribution models were used to explore the effect of high-resolution topoclimatic and human-related variables on the occurrence of six of the most aggressive alien plants colonizing French sub-Antarctic islands. Furthermore, the interaction between alien species traits and their response to anthropogenic propagule pressure was analysed by means of a multi-species distribution model. This allowed identifying the features of species that were associated to low dependence on human-assisted introductions, and were thus potentially more invasive.ResultsWe observed two main invasion patterns: low-spread species strongly dependent on anthropogenic propagule pressure and high-spread species limited mainly by harsh climatic conditions. Differences in invasiveness across species mostly related to their residence time, life history and plant height, with older introductions, perennial and low-stature species being more invasive.Main conclusionsThe availability of high-resolution data improved our understanding of the role of environmental and human-related factors in driving alien species distribution on sub-Antarctic islands. At the same time, the identification of alien species features conferring invasiveness may help anticipating future problematic invasions.

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 Species distribution model reveals only highly fragmented suitable patches remaining for giant armadillo in the Brazilian Cerrado

2021 ◽  
Author(s):  
Katia Maria Paschoaletto Micchi de Barros Ferraz ◽  
Bruna Gomes de Oliveira ◽  
Nina Attias ◽  
Arnaud Leonard Jean Desbiez
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Brazilian Cerrado

Confronting existing knowledge on ecological preferences of stream macroinvertebrates with independent monitoring data using a Bayesian multi-species distribution model

2020 ◽  
Author(s):  
Peter Vermeiren ◽  
Peter Reichert ◽  
Wolfram Graf ◽  
Patrick Leitner ◽  
Astrid Schmidt-Kloiber ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Monitoring Data ◽  
Distribution Model ◽  
Ecological Preferences ◽  
Stream Macroinvertebrates

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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