scholarly journals Abundance distributions for tree species in Great Britain: A two-stage approach to modeling abundance using species distribution modeling and random forest

2017 ◽  
Vol 7 (4) ◽  
pp. 1043-1056 ◽  
Author(s):  
Louise Hill ◽  
Andy Hector ◽  
Gabriel Hemery ◽  
Simon Smart ◽  
Matteo Tanadini ◽  
...  
Keyword(s):  
Great Britain ◽  
Random Forest ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Modeling ◽  
Two Stage ◽  
Distribution Modeling

scholarly journals Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation

2009 ◽  
Vol 2 (3) ◽  
pp. 319-352 ◽  
Author(s):  
L. Cayuela ◽  
D. J. Golicher ◽  
A. C. Newton ◽  
M. Kolb ◽  
F. S. de Alburquerque ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Central America ◽  
Species Distribution ◽  
Conservation Planning ◽  
Tree Species ◽  
Species Distribution Modeling ◽  
Tropical Regions ◽  
Distribution Modeling ◽  
The Tropics

In this paper we aim to investigate the problems and potentialities of species distribution modeling (SDM) as a tool for conservation planning and policy development and implementation in tropical regions. We reviewed 123 studies published between 1995 and 2007 in five of the leading journals in ecology and conservation, and examined two tropical case studies in which distribution modeling is currently being applied to support conservation planning. We also analyzed the characteristics of data typically used for fitting models within the specific context of modeling tree species distribution in Central America. The results showed that methodological papers outnumbered reports of SDMs being used in an applied context for setting conservation priorities, particularly in the tropics. Most applications of SDMs were in temperate regions and biased towards certain organisms such as mammals and birds. Studies from tropical regions were less likely to be validated than those from temperate regions. Unpublished data from two major tropical case studies showed that those species that are most in need of conservation actions, namely those that are the rarest or most threatened, are those for which SDM is least likely to be useful. We found that only 15% of the tree species of conservation concern in Central America could be reliably modelled using data from a substantial source (Missouri Botanical Garden VAST database). Lack of data limits model validation in tropical areas, further restricting the value of SDMs. We concluded that SDMs have a great potential to support biodiversity conservation in the tropics, by supporting the development of conservation strategies and plans, identifying knowledge gaps, and providing a tool to examine the potential impacts of environmental change. However, for this potential to be fully realized, problems of data quality and availability need to be overcome. Weaknesses in current biological datasets need to be systematically addressed, by increasing collection of field survey data, improving data sharing and increasing structural integration of data sources. This should include use of distributed databases with common standards, referential integrity, and rigorous quality control. Integration of data management with SDMs could significantly add value to existing data resources by improving data quality control and enabling knowledge gaps to be identified.

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.

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.

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.

scholarly journals Combining connectivity and species distribution modeling to define conservation and restoration priorities for multiple species: A case study in the eastern Amazon

2021 ◽  
Vol 257 ◽  
pp. 109148
Author(s):  
Leonardo de Sousa Miranda ◽  
Marcelo Awade ◽  
Rodolfo Jaffé ◽  
Wilian França Costa ◽  
Leonardo Carreira Trevelin ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Modeling ◽  
Distribution Modeling ◽  
Multiple Species
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