scholarly journals Matching Data Types to the Objectives of Species Distribution Modeling: An Evaluation With Marine Fish Species

2021 ◽  
Vol 8 ◽  
Author(s):  
Jing Luan ◽  
Chongliang Zhang ◽  
Yupeng Ji ◽  
Binduo Xu ◽  
Ying Xue ◽  
...  
Keyword(s):  
Random Forest ◽  
Species Distribution ◽  
Ordinal Data ◽  
Species Distribution Modeling ◽  
Data Type ◽  
Distribution Model ◽  
Distribution Data ◽  
Data Types ◽  
Abundance Data ◽  
Distribution Modeling

Species distribution model (SDM) is a crucial tool for forecasting ranges of species and mirroring habitat references and quality. Different types of species distribution data have been commonly used in SDMs regarding different purposes and availability, whereas, the influences of data types on model performances have not been well understood. This study considered three data types characterized by different levels of organism information and cost in data acquisitions, namely presence/absence (P/A), ordinal data, and abundance data. We developed a range of distribution models for nine demersal species in the coastal waters of Shandong Peninsula, China, using two modeling algorithms [the Generalized Additive Model (GAM) and Random Forest]. Firstly, we evaluated the performances of all models on predicting species occurrence (i.e., habitat suitability or range boundaries), and then compared the models built with ordinal data and abundance data on projecting ordinal predictions (i.e., relative density or habitat quality). Their predictive abilities were assessed through cross-validation tests with diverse performance measurements. Overall, no data type is superior in all situations, but combined with two algorithms, the abundance data slightly outperformed the ordinal data and P/A data unexpectedly exerted reliable performances. Specifically, the effectiveness of data type for two application purposes of SDMs substantially varied with modeling algorithms, revealing that GAMs always benefit most from ordinal data and the opposite was true for Random Forest. For some small resident organisms with moderate prevalence, rough distribution data might be adopted for providing reliable projections. Our findings highlight the importance of clarifying the objectives of SDMs when choosing data types for species distribution modeling.

scholarly journals SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4095 ◽  
Author(s):  
Jason L. Brown ◽  
Joseph R. Bennett ◽  
Connor M. French
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Species Distribution Modeling ◽  
Environmental Data ◽  
Distribution Model ◽  
Data Types ◽  
Distribution Models ◽  
Distribution Modeling ◽  
Modeling Software ◽  
Cost Paths

SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.

scholarly journals Better providers of habitat for Javan slow loris (Nycticebus javanicus E. Geoffroy 1812): A species distribution modeling approach in Central Java, Indonesia

2020 ◽  
Vol 21 (5) ◽  
Author(s):  
Mahfut Sodik ◽  
Satyawan Pudyatmoko ◽  
Pujo Semedi Hargo Yuwono ◽  
Muhammad Tafrichan ◽  
Muhammad Ali Imron
Keyword(s):  
Species Distribution ◽  
Species Distribution Modeling ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Conservation Areas ◽  
Slow Loris ◽  
Modeling Approach ◽  
Central Java ◽  
Distribution Modeling ◽  
Central Java Province

Abstract. Sodik M, Pudyatmoko S, Yuwono PSH, Tafrichan M, Imron MA. 2020. Better providers of habitat for Javan slow loris (Nycticebus javanicus E. Geoffroy 1812): A species distribution modeling approach in Central Java, Indonesia. Biodiversitas 21: 1890-1900. The Javan slow loris is an arboreal and nocturnal primate endemic to Java, which is known to inhabit primary and secondary forest habitats, such as swamps, plantations, and bamboo forest. The population of the Javan slow loris continues to decline significantly due to forest degradation, habitat loss/fragmentation, and illegal trade. Conservation of this small primate in Java has been hampered by a paucity of local data on how conservation areas support this species. This study aims to build a spatial distribution model of the Javan slow loris and analyzing the role of each stakeholder plays on land use type to support the conservation of N. javanicus. By utilizing Species Distribution Modeling (SDM) with Maximum Entropy species distribution modeling approach, the researchers were able to highlight the importance of which conservation areas in Central Java that play crucial role to conserve the N. javanicus population. Data on the presence of the Javan slow loris was obtained from the result of a survey undertaken in 2017 and communication with researchers. Elevation, slope, landcover, rainfall, distance to road, distance to settlement, distance to river (water source), and NDVI were used as environmental variables. Results showed that 0.76% (25,715.4 ha) of the total area of the Central Java Province is suitable for their habitat. In addition, results revealed that 2.2% of suitable habitat is present within conservation areas, 4.6% in protected forest areas, and 93.2% outside of protected areas. The Javan slow loris is predicted to be mostly scattered in the northern part of Central Java Province. The Javan slow loris is widely distributed in plantations, their most dominant habitat. The findings of this study show that the small percentage of suitable habitat presents within protected forest and conservation areas cannot sustainably maintain the extant Javan slow loris population. Thus, it is important for the Indonesian government and other key related stakeholders to work together in combination with educating local communities to preserve the habitat and population of N. javanicus.

scholarly journals Clarifying species dependence under joint species distribution modeling

2019 ◽  
Author(s):  
Alan E. Gelfand ◽  
Shinichiro Shirota
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Species Distribution Modeling ◽  
Region Of Interest ◽  
Cape Floristic Region ◽  
Distribution Model ◽  
List Type ◽  
Multivariate Normal ◽  
Odds Ratios ◽  
Distribution Modeling

AbstractJoint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These models attempt to capture species dependence through an associated correlation matrix arising from a set of latent multivariate normal variables. However, these associations offer little insight into dependence behavior between species at sites.We focus on presence/absence data using joint species modeling which incorporates spatial dependence between sites. For pairs of species, we emphasize the induced odds ratios (along with the joint probabilities of occurrence); they provide much clearer understanding of joint presence/absence behavior. In fact, we propose a spatial odds ratio surface over the region of interest to capture how dependence varies over the region.We illustrate with a dataset from the Cape Floristic Region of South Africa consisting of more than 600 species at more than 600 sites. We present the spatial distribution of odds ratios for pairs of species that are positively correlated and pairs that are negatively correlated under the joint species distribution model.The multivariate normal covariance matrix associated with a collection of species is only a device for creating dependence among species but it lacks interpretation. By considering odds ratios, the quantitative ecologist will be able to better appreciate the practical dependence between species that is implicit in these joint species distribution modeling specifications.

Using species distribution modeling to improve conservation of medicinal plants in Southwest China

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
B Liu ◽  
F Li ◽  
Z Guo ◽  
L Hong ◽  
W Huang ◽  
...  
Keyword(s):  
Medicinal Plants ◽  
Species Distribution ◽  
Southwest China ◽  
Species Distribution Modeling ◽  
Distribution Modeling

Species distribution modeling reveals the ecological niche of extinct megafauna from South America

2021 ◽  
pp. 1-8
Author(s):  
Thaísa Araújo ◽  
Helena Machado ◽  
Dimila Mothé ◽  
Leonardo dos Santos Avilla
Keyword(s):  
South America ◽  
Species Distribution ◽  
Environmental Changes ◽  
Species Distribution Modeling ◽  
Human Action ◽  
Northeastern Brazil ◽  
Climate Conditions ◽  
Middle Holocene ◽  
Distribution Modeling ◽  
The Impact

Abstract Climatic and environmental changes, as well as human action, have been cited as potential causes for the extinction of megafauna in South America at the end of the Pleistocene. Among megamammals lineages with Holarctic origin, only horses and proboscideans went extinct in South America during this period. This study aims to understand how the spatial extent of habitats suitable for Equus neogeus and Notiomastodon platensis changed between the last glacial maximum (LGM) and the middle Holocene in order to determine the impact that climatic and environmental changes had on these taxa. We used species distribution modeling to estimate their potential extent on the continent and found that both species occupied arid and semiarid open lands during the LGM, mainly in the Pampean region of Argentina, southern and northeastern Brazil, and parts of the Andes. However, when climate conditions changed from dry and cold during the LGM to humid and warm during the middle Holocene, the areas suitable for these taxa were reduced dramatically. These results support the hypothesis that climatic changes were a driving cause of extinction of these megamammals in South America, although we cannot rule out the impact of human actions or other potential causes for their extinction.

scholarly journals Application Natura 2000 Data For The Invasive Plants Spread Prediction

2015 ◽  
Vol 46 (4) ◽  
pp. 159-166 ◽  
Author(s):  
J. Pěknicová ◽  
D. Petrus ◽  
K. Berchová-Bímová
Keyword(s):  
Environmental Factors ◽  
Invasive Plants ◽  
Species Distribution ◽  
Natura 2000 ◽  
Distribution Model ◽  
Distribution Data ◽  
Habitat Types ◽  
Distribution Models ◽  
Occurrence Data ◽  
Heracleum Mantegazzianum

AbstractThe distribution of invasive plants depends on several environmental factors, e.g. on the distance from the vector of spreading, invaded community composition, land-use, etc. The species distribution models, a research tool for invasive plants spread prediction, involve the combination of environmental factors, occurrence data, and statistical approach. For the construction of the presented distribution model, the occurrence data on invasive plants (Solidagosp.,Fallopiasp.,Robinia pseudoaccacia,andHeracleum mantegazzianum) and Natura 2000 habitat types from the Protected Landscape Area Kokořínsko have been intersected in ArcGIS and statistically analyzed. The data analysis was focused on (1) verification of the accuracy of the Natura 2000 habitat map layer, and the accordance with the habitats occupied by invasive species and (2) identification of a suitable scale of intersection between the habitat and species distribution. Data suitability was evaluated for the construction of the model on local scale. Based on the data, the invaded habitat types were described and the optimal scale grid was evaluated. The results show the suitability of Natura 2000 habitat types for modelling, however more input data (e.g. on soil types, elevation) are needed.

Using Open-Source Data in Correlative Species Distribution Modeling of Marine Species

2018 ◽  
Vol 80 (6) ◽  
pp. 457-461
Author(s):  
Carlos A. Morales-Ramirez ◽  
Pearlyn Y. Pang
Keyword(s):  
Open Source ◽  
Species Distribution ◽  
Open Source Software ◽  
Species Distribution Modeling ◽  
Science Research ◽  
Marine Species ◽  
Distribution Modeling ◽  
Open Source Data ◽  
Rapid Changes ◽  
Source Data

Open-source data are information provided free online. It is gaining popularity in science research, especially for modeling species distribution. MaxEnt is an open-source software that models using presence-only data and environmental variables. These variables can also be found online and are generally free. Using all of these open-source data and tools makes species distribution modeling (SDM) more accessible. With the rapid changes our planet is undergoing, SDM helps understand future habitat suitability for species. Due to increasing interest in biogeographic research, SDM has increased for marine species, which were previously not commonly found in this modeling. Here we provide examples of where to obtain the data and how the modeling can be performed and taught.

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