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 Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8059 ◽  
Author(s):  
Benjamin M. Marshall ◽  
Colin T. Strine
Keyword(s):  
Social Media ◽  
North America ◽  
South America ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Status ◽  
Model Performance ◽  
Distribution Models ◽  
Occurrence Data ◽  
Occurrence Records

A species’ distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr’s API. We show the biodiversity database’s 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

scholarly journals Testing the Influence of Seascape Connectivity on Marine-Based Species Distribution Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Giorgia Cecino ◽  
Roozbeh Valavi ◽  
Eric A. Treml
Keyword(s):  
Species Distribution ◽  
Larval Dispersal ◽  
Species Distribution Models ◽  
Temporal Dynamics ◽  
Explanatory Power ◽  
Model Performance ◽  
Boosted Regression Trees ◽  
Distribution Models ◽  
Flexible Tool ◽  
Species Specific

Species distribution models (SDMs) are commonly used in ecology to predict species occurrence probability and how species are geographically distributed. Here, we propose innovative predictive factors to efficiently integrate information on connectivity into SDMs, a key element of population dynamics strongly influencing how species are distributed across seascapes. We also quantify the influence of species-specific connectivity estimates (i.e., larval dispersal vs. adult movement) on the marine-based SDMs outcomes. For illustration, seascape connectivity was modeled for two common, yet contrasting, marine species occurring in southeast Australian waters, the purple sea urchin, Heliocidaris erythrogramma, and the Australasian snapper, Chrysophrys auratus. Our models illustrate how different species-specific larval dispersal and adult movement can be efficiently accommodated. We used network-based centrality metrics to compute patch-level importance values and include these metrics in the group of predictors of correlative SDMs. We employed boosted regression trees (BRT) to fit our models, calculating the predictive performance, comparing spatial predictions and evaluating the relative influence of connectivity-based metrics among other predictors. Network-based metrics provide a flexible tool to quantify seascape connectivity that can be efficiently incorporated into SDMs. Connectivity across larval and adult stages was found to contribute to SDMs predictions and model performance was not negatively influenced from including these connectivity measures. Degree centrality, quantifying incoming and outgoing connections with habitat patches, was the most influential centrality metric. Pairwise interactions between predictors revealed that the species were predominantly found around hubs of connectivity and in warm, high-oxygenated, shallow waters. Additional research is needed to quantify the complex role that habitat network structure and temporal dynamics may have on SDM spatial predictions and explanatory power.

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>

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