scholarly journals Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type

Climate ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 37 ◽  
Author(s):  
Catherine Jarnevich ◽  
Nicholas Young
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Model Parameters ◽  
Climate Data ◽  
Distribution Models ◽  
Model Type ◽  
Time Periods ◽  
The Impact

Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.

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 Community Confounding In Joint Species Distribution Models

2021 ◽  
Author(s):  
Justin J. Van Ee ◽  
Jacob S. Ivan ◽  
Mevin B. Hooten
Keyword(s):  
Bark Beetle ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Management Strategies ◽  
Single Species ◽  
Distribution Model ◽  
Subalpine Forest ◽  
Community Effects ◽  
Distribution Models

Abstract Joint species distribution models have become ubiquitous for studying species-habitat relationships and dependence among species. Accounting for community structure often improves predictive power, but can also alter inference on species-habitat relationships. Modulated species-habitat relationships are indicative of community confounding: The situation in which interspecies dependence and habitat effects compete to explain species distributions. We discuss community confounding in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the inference from independent single species distribution models and a joint species distribution model. We present a method for measuring community confounding and develop a restricted version of our hierarchical model that orthogonalizes the habitat and species random effects. Our results indicate that variables associated with the severity and duration of the bark beetle epidemic suffer from community confounding. This implies that mammalian responses to the bark beetle epidemic are governed by interconnected habitat and community effects. Disentangling habitat and community effects can improve our understanding of the ecological system and possible management strategies. We evaluate restricted regression as a method for alleviating community confounding and distinguish it from other inferential methods for confounded models.

Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

2017 ◽  
pp. 427-440
Author(s):  
Balaguru Balakrishnan ◽  
Nagamurugan Nandakumar ◽  
Soosairaj Sebastin ◽  
Khaleel Ahamed Abdul Kareem
Keyword(s):  
Species Distribution ◽  
Spatial Data ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Target Species ◽  
Distribution Models ◽  
Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

scholarly journals From Remote Sensing to Species Distribution Modelling: An Integrated Workflow to Monitor Spreading Species in Key Grassland Habitats

2021 ◽  
Vol 13 (10) ◽  
pp. 1904
Author(s):  
Walter De Simone ◽  
Marina Allegrezza ◽  
Anna Rita Frattaroli ◽  
Silvia Montecchiari ◽  
Giulio Tesei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Abiotic Factors ◽  
Natura 2000 ◽  
Distribution Model ◽  
Overlay Analysis ◽  
Distribution Models ◽  
Habitat Directive ◽  
The Impact

Remote sensing (RS) has been widely adopted as a tool to investigate several biotic and abiotic factors, directly and indirectly, related to biodiversity conservation. European grasslands are one of the most biodiverse habitats in Europe. Most of these habitats are subject to priority conservation measure, and several human-induced processes threaten them. The broad expansions of few dominant species are usually reported as drivers of biodiversity loss. In this context, using Sentinel-2 (S2) images, we investigate the distribution of one of the most spreading species in the Central Apennine: Brachypodium genuense. We performed a binary Random Forest (RF) classification of B. genuense using RS images and field-sampled presence/absence data. Then, we integrate the occurrences obtained from RS classification into species distribution models to identify the topographic drivers of B. genuense distribution in the study area. Lastly, the impact of B. genuense distribution in the Natura 2000 (N2k) habitats (Annex I of the European Habitat Directive) was assessed by overlay analysis. The RF classification process detected cover of B. genuense with an overall accuracy of 94.79%. The topographic species distribution model shows that the most relevant topographic variables that influence the distribution of B. genuense are slope, elevation, solar radiation, and topographic wet index (TWI) in order of importance. The overlay analysis shows that 74.04% of the B. genuense identified in the study area falls on the semi-natural dry grasslands. The study highlights the RS classification and the topographic species distribution model’s importance as an integrated workflow for mapping a broad-expansion species such as B. genuense. The coupled techniques presented in this work should apply to other plant communities with remotely recognizable characteristics for more effective management of N2k habitats.

Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

2019 ◽  
pp. 555-568
Author(s):  
Balaguru Balakrishnan ◽  
Nagamurugan Nandakumar ◽  
Soosairaj Sebastin ◽  
Khaleel Ahamed Abdul Kareem
Keyword(s):  
Species Distribution ◽  
Spatial Data ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Target Species ◽  
Distribution Models ◽  
Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

scholarly journals When exotic introductions fail: updating invasion beliefs

2019 ◽  
Vol 22 (3) ◽  
pp. 1097-1107
Author(s):  
Daniel K. Heersink ◽  
Peter Caley ◽  
Dean Paini ◽  
Simon C. Barry
Keyword(s):  
Species Distribution ◽  
Habitat Suitability ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Region Of Interest ◽  
Distribution Model ◽  
Perna Viridis ◽  
Prior Belief ◽  
Distribution Models ◽  
Establishment Probability

AbstractDecisions regarding invasive risk of exotic species are often based on species distribution models projected onto the recipient region of interest. Such projections are essentially a measure of prior belief in the ability of an organism to invade. Whilst many decisions are made on the basis of such projections, it is less clear how such prior belief may be empirically modified on the basis of data, in particular introduction events that haven’t led to establishment. Here, using the Asian green mussel (Perna viridis) as an example, we illustrate how information on failed introduction attempts may be used to continually update our beliefs in the ability of an organism to invade per introduction, and the underlying habitat suitability for establishment. Our results show that the establishment probability of P. viridis per fouled ship visit in the supposedly favourable northern Australian waters are much lower than initially though, and are continuing to decline. A Bayesian interpretation of our results notes the dramatic reduction in our belief of the ability of P. viridis to invade in the light of what we estimate to be 100’s of fouled vessels per year visiting ports without any persistent populations establishing. Under a hypothetico-deductive approach we would reject the null (prior) species distribution model as being useful, and seek to find a better one that can withstand the challenge of data.

scholarly journals Joint species distribution models of Everglades wading birds to inform restoration planning

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245973
Author(s):  
Laura E. D’Acunto ◽  
Leonard Pearlstine ◽  
Stephanie S. Romañach
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Site Occupancy ◽  
The United States ◽  
Decision Makers ◽  
Distribution Model ◽  
Distribution Models ◽  
Landscape Characteristics ◽  
Wading Bird

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

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

scholarly journals Assessing the effect of fish size on species distribution model performance in southern Chilean rivers

2018 ◽  
Author(s):  
Daniel Zamorano ◽  
Fabio Labra ◽  
Marcelo Villarroel ◽  
Luca Mao ◽  
Shaw Lucy ◽  
...  
Keyword(s):  
Body Size ◽  
Species Distribution ◽  
River Flow ◽  
Species Distribution Model ◽  
Anthropogenic Activities ◽  
Model Performance ◽  
Distribution Model ◽  
Distribution Models ◽  
Fish Size ◽  
River Fish

Despite its theoretical relationship, the effect of body size on the performance of species distribution models (SDM) has only been assessed in a few studies of terrestrial taxa. We aim to assess the effect of body size on the performance of SDM in river fish. We study seven Chilean freshwater fish, using models trained with three different sets of predictor variables: ecological (Eco), anthropogenic (Antr) and both (Eco+Antr). Our results indicate that the performance of the Eco+Antr models improves with fish size. These results highlight the importance of two novel predictive layers: the source of river flow and the overproduction of biotopes by anthropogenic activities. We compare our work with previous studies that modeled river fish, and observe a similar relationship in most cases. We discuss the current challenges of the modeling of riverine species, and how our work helps suggest possible solutions.

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