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 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.

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 Muddy Boots Beget Wisdom: Implications for Rare or Endangered Plant Species Distribution Models

Diversity ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 10 ◽  
Author(s):  
Nora Oleas ◽  
Kenneth Feeley ◽  
Javier Fajardo ◽  
Alan Meerow ◽  
Jennifer Gebelein ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Strategies ◽  
Distribution Models ◽  
Online Data ◽  
Species Ranges ◽  
Geographic Ranges ◽  
Standard Data ◽  
Herbarium Collections ◽  
Species Specific

Species distribution models (SDMs) are popular tools for predicting the geographic ranges of species. It is common practice to use georeferenced records obtained from online databases to generate these models. Using three species of Phaedranassa (Amaryllidaceae) from the Northern Andes, we compare the geographic ranges as predicted by SDMs based on online records (after standard data cleaning) with SDMs of these records confirmed through extensive field searches. We also review the identification of herbarium collections. The species’ ranges generated with corroborated field records did not agree with the species’ ranges based on the online data. Specifically, geographic ranges based on online data were significantly inflated and had significantly different and wider elevational extents compared to the ranges based on verified field records. Our results suggest that to generate accurate predictions of species’ ranges, occurrence records need to be carefully evaluated with (1) appropriate filters (e.g., altitude range, ecosystem); (2) taxonomic monographs and/or specialist corroboration; and (3) validation through field searches. This study points out the implications of generating SDMs produced with unverified online records to guide species-specific conservation strategies since inaccurate range predictions can have important consequences when estimating species’ extinction risks.

scholarly journals Species-specific adaptations determine how aridity and biotic interactions drive the assembly of dryland plant communities

2017 ◽  
Author(s):  
Miguel Berdugo ◽  
Fernando T. Maestre ◽  
Sonia Kéfi ◽  
Nicolas Gross ◽  
Yoann Le Bagousse-Pinguet ◽  
...  
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Species Distribution ◽  
Community Assembly ◽  
Species Distribution Models ◽  
List Type ◽  
Relative Importance ◽  
Distribution Models ◽  
Species Specific ◽  
Plant Community Assembly

AbstractDespite being a core ecological question, disentangling individual and interacting effects of plant-plant interactions, abiotic factors and species-specific adaptations as drivers of community assembly is challenging. Studies addressing this issue are growing rapidly, but they generally lack empirical data regarding species interactions and local abundances, or cover a narrow range of environmental conditions.We analysed species distribution models and local spatial patterns to isolate the relative importance of key abiotic (aridity) and biotic (facilitation and competition) drivers of plant community assembly in drylands worldwide. We examined the relative importance of these drivers along aridity gradients and used information derived from the niches of species to understand the role that species-specific adaptations to aridity play in modulating the importance of community assembly drivers.Facilitation, together with aridity, was the major driver of plant community assembly in global drylands. Due to community specialization, the importance of facilitation as an assembly driver decreased with aridity, and became non significant at the border between arid and semiarid climates. Under the most arid conditions, competition affected species abundances in communities dominated by specialist species. Due to community specialization, the importance of aridity in shaping dryland plant communities peaked at moderate aridity levels.Synthesis: We showed that competition is an important driver of community assembly even under harsh environments, and that the effect of facilitation collapses as driver of species relative abundances under high aridity because of the specialization of the species pool to extremely dry conditions. Our findings pave the way to develop more robust species distribution models aiming to predict the consequences of ongoing climate change on community assembly in drylands, the largest biome on Earth.

scholarly journals [Final version available] Explainable Artificial Intelligence enhances the ecological interpretability of black-box species distribution models

2020 ◽  
Author(s):  
Masahiro Ryo ◽  
Boyan Angelov ◽  
Stefano Mammola ◽  
Jamie M. Kass ◽  
Blas M. Benito ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Complex Model ◽  
Boosted Regression Trees ◽  
Learning Approaches ◽  
Distribution Models ◽  
Interpretable Model ◽  
Scale Behavior

Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model accuracy has steadily increased as a result, but the interpretability of the fitted models, for example the relative importance of predictor variables or their causal effects on focal species, has not always kept pace. Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions. We describe the rationale behind xAI and provide a list of tools that can be used to help ecological modelers better understand complex model behavior at different scales. As an example, we perform a reproducible SDM analysis in R on the African elephant and showcase some xAI tools such as local interpretable model-agnostic explanation (LIME) to help interpret local-scale behavior of the model. We conclude with what we see as the benefits and caveats of these techniques and advocate for their use to improve the interpretability of machine learning SDMs.

scholarly journals Drivers of Solidago species invasion in Central Europe---Case study in the landscape of the Carpathian Mountains and their foreground

2021 ◽  
Author(s):  
Chathura Perera ◽  
Tomasz Szymura ◽  
Adam Zając ◽  
Dominika Chmolowska ◽  
Magdalena Szymura
Keyword(s):  
Central Europe ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Boosted Regression Trees ◽  
Sampling Effort ◽  
Similar Species ◽  
Distribution Models ◽  
Carpathian Mountains ◽  
Explanatory Variables ◽  
Historical Distribution

Abstract Aim: The invasion process is a complex, context-dependent phenomenon; nevertheless, it can be described using the PAB framework. This framework encompasses the joint effect of propagule pressure (P), abiotic characteristics of the environment (A), and biotic characteristics of both the invader and recipient vegetation (B). We analyzed the effectiveness of proxies of PAB factors to explain the spatial pattern of Solidago canadensis and S. gigantea invasion using invasive species distribution models. Location: Carpathian Mountains and their foreground, Central Europe. Methods: The data on species presence or absence were from an atlas of neophyte distribution based on a 2 × 2 km grid, covering approximately 31,200 km2 (7752 grid cells). Proxies of PAB factors, along with data on historical distribution of invaders were used as explanatory variables in Boosted Regression Trees models to explain the distribution of invasive Solidago. The areas with potentially lower sampling effort were excluded from analysis based on a target species approach. Results: Proxies of the PAB factors helped to explain the distribution of both S. canadensis and S. gigantea. Distributions of both species were limited climatically because a mountain climate is not conducive to their growth; however, the S. canadensis distribution pattern was correlated with proxies of human pressure, whereas S. gigantea distribution was connected with environmental characteristics. The varied responses of species with regard to distance from their historical distribution sites indicated differences in their invasion drivers. Main conclusions: Proxies of PAB are helpful in the choice of explanatory variables as well as the ecological interpretation of species distribution models. The results underline that human activity can cause variation in the invasion of ecologically similar species.

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