scholarly journals Data Management Plan for PhD Thesis "Climatic Limitation of Alien Weeds in New Zealand: Enhancing Species Distribution Models with Field Data"

2016 ◽  
Vol 2 ◽  
pp. e8664-1
Author(s):  
Jennifer Pannell
Keyword(s):  
New Zealand ◽  
Data Management ◽  
Species Distribution ◽  
Field Data ◽  
Species Distribution Models ◽  
Management Plan ◽  
Distribution Models ◽  
Phd Thesis

Uncertainty analysis of crowd-sourced and professionally collected field data used in species distribution models of Taiwanese moths

2015 ◽  
Vol 181 ◽  
pp. 102-110 ◽  
Author(s):  
Yu-Pin Lin ◽  
Dongpo Deng ◽  
Wei-Chih Lin ◽  
Rob Lemmens ◽  
Neville D. Crossman ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Species Distribution ◽  
Field Data ◽  
Species Distribution Models ◽  
Distribution Models

Putting prey into the picture: improvements to species distribution models for bottlenose dolphins in Doubtful Sound, New Zealand

2020 ◽  
Vol 653 ◽  
pp. 191-204
Author(s):  
S Bennington ◽  
W Rayment ◽  
S Dawson
Keyword(s):  
New Zealand ◽  
Habitat Use ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Bottlenose Dolphins ◽  
Model Performance ◽  
Additive Models ◽  
Distribution Models ◽  
Marine Predators ◽  
Abiotic Variables

Species distribution models (SDMs) often rely on abiotic variables as proxies for biotic relationships. This means that important biotic relationships may be missed, creating ambiguity in our understanding of the drivers of habitat use. These problems are especially relevant for populations of predators, as their habitat use is likely to be strongly influenced by the distribution of their prey. We investigated habitat use of a population of a top predator, bottlenose dolphins Tursiops truncatus, in Doubtful Sound, New Zealand, using generalised additive models, and compared the results of models with and without biotic predictor variables. We found that although habitat use by bottlenose dolphins was significantly correlated with abiotic variables that likely describe foraging areas, introduction of biotic variables describing potential prey almost doubled the deviance explained, from 19.8 to 39.1%. Biotic variables were the most important of the predictors used, and indicated that the dolphins showed a preference for areas with a high abundance of a reef fish, girdled wrasse Notolabrus cinctus. For the dolphins of Doubtful Sound, these results show the importance of prey distribution in driving habitat use. On a broader scale, our results indicate that making an effort to include true biotic descriptors in SDMs can improve model performance, resulting in better understanding of the drivers of distribution of marine predators.

scholarly journals Modelling the distribution of an oviparous skink, Oligosoma suteri

2021 ◽  
Author(s):  
◽  
Vaughn I. Stenhouse
Keyword(s):  
Climate Change ◽  
New Zealand ◽  
Spatial Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Degree Days ◽  
Data Logger ◽  
Distribution Models ◽  
Soil Temperatures ◽  
Incubation Duration

<p>Predicting species distributions relies on understanding the fundamental constraints of climate conditions on organism’s physiological traits. Species distribution models (SDMs) provide predictions on species range limits and habitat suitability using spatial environmental data. Species distribution modelling is useful to estimate environmental conditions in time and space and how they may change in future climates. Predicting the distribution of terrestrial biodiversity requires an understanding of the mechanistic links between an organism’s traits and the environment. Implementation of mechanistic species distribution models requires knowledge of how environmental change influences physiological performance. Mechanistic modelling is considered more robust than correlative SDMs when extrapolating to novel environments predicted with climate change. I examined the spatial distribution and the impact of climate change on incubation duration of an endemic, nocturnal skink, Oligosoma suteri. My research focused on the ways a microclimate model with local weather data and degree-days can predict O. suteri’s distribution and affect incubation duration. Using a microclimate model (NicheMapR), I generated hourly soil temperatures for three depths in two substrate types (rock and sand) at a 15 km spatial resolution for the entire coastline of New Zealand and for seven depths for one substrate type (rock) for the coastline of Rangitoto/Motutapu Island at a 20 m spatial resolution. I estimated the minimum number of degree days required for successful embryonic development using a minimum temperature threshold for O. suteri eggs. I apply the incubation duration predicted by the model to map potential distribution for the two different spatial resolutions (15 km and 20 m) and I also include a climate change component to predict the potential effects on incubation duration and oviposition timing. My results from the New Zealand wide model indicate that embryonic development for O. suteri may be possible beyond their current distribution, and climate warming decreases incubation duration and lengthens the oviposition period for the New Zealand wide map. I generated maps of predicted incubation duration with depth for a coastal habitat at a higher resolution for Rangitoto/Motutapu Island. Incubation duration varied by depth with higher number of days to hatch predicted for greater depths. Temperature data loggers were installed at two different sites at three depths and were compared to the Rangitoto/Motutapu Island microclimate model. Modelled incubation durations were consistently shorter than data logger incubation durations across all three depths at both data logger sites. Species distribution model with coarse spatial and climate data can predict where soil temperatures would be suitable for successful development. A higher spatial resolution can reveal variation in incubation duration within sites indicated as suitable from the coarse resolution map. By using two different spatial extents initial starting points can be identified for which a higher resolution model can be applied to better inform management decisions relating to conservation actions and the effects of climate change for O. suteri and other species.</p>

scholarly journals Modelling the distribution of an oviparous skink, Oligosoma suteri

2021 ◽  
Author(s):  
◽  
Vaughn I. Stenhouse
Keyword(s):  
Climate Change ◽  
New Zealand ◽  
Spatial Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Degree Days ◽  
Data Logger ◽  
Distribution Models ◽  
Soil Temperatures ◽  
Incubation Duration

<p>Predicting species distributions relies on understanding the fundamental constraints of climate conditions on organism’s physiological traits. Species distribution models (SDMs) provide predictions on species range limits and habitat suitability using spatial environmental data. Species distribution modelling is useful to estimate environmental conditions in time and space and how they may change in future climates. Predicting the distribution of terrestrial biodiversity requires an understanding of the mechanistic links between an organism’s traits and the environment. Implementation of mechanistic species distribution models requires knowledge of how environmental change influences physiological performance. Mechanistic modelling is considered more robust than correlative SDMs when extrapolating to novel environments predicted with climate change. I examined the spatial distribution and the impact of climate change on incubation duration of an endemic, nocturnal skink, Oligosoma suteri. My research focused on the ways a microclimate model with local weather data and degree-days can predict O. suteri’s distribution and affect incubation duration. Using a microclimate model (NicheMapR), I generated hourly soil temperatures for three depths in two substrate types (rock and sand) at a 15 km spatial resolution for the entire coastline of New Zealand and for seven depths for one substrate type (rock) for the coastline of Rangitoto/Motutapu Island at a 20 m spatial resolution. I estimated the minimum number of degree days required for successful embryonic development using a minimum temperature threshold for O. suteri eggs. I apply the incubation duration predicted by the model to map potential distribution for the two different spatial resolutions (15 km and 20 m) and I also include a climate change component to predict the potential effects on incubation duration and oviposition timing. My results from the New Zealand wide model indicate that embryonic development for O. suteri may be possible beyond their current distribution, and climate warming decreases incubation duration and lengthens the oviposition period for the New Zealand wide map. I generated maps of predicted incubation duration with depth for a coastal habitat at a higher resolution for Rangitoto/Motutapu Island. Incubation duration varied by depth with higher number of days to hatch predicted for greater depths. Temperature data loggers were installed at two different sites at three depths and were compared to the Rangitoto/Motutapu Island microclimate model. Modelled incubation durations were consistently shorter than data logger incubation durations across all three depths at both data logger sites. Species distribution model with coarse spatial and climate data can predict where soil temperatures would be suitable for successful development. A higher spatial resolution can reveal variation in incubation duration within sites indicated as suitable from the coarse resolution map. By using two different spatial extents initial starting points can be identified for which a higher resolution model can be applied to better inform management decisions relating to conservation actions and the effects of climate change for O. suteri and other species.</p>

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.

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