scholarly journals Niche conservatism in a plant with long invasion history: the case of the Peruvian peppertree (Schinus molle, Anacardiaceae) in Mexico

2020 ◽  
Vol 153 (1) ◽  
pp. 3-11
Author(s):  
Jorge E. Ramírez-Albores ◽  
Gustavo Bizama ◽  
Ramiro O. Bustamante ◽  
Ernesto I. Badano
Keyword(s):  
Invasive Plants ◽  
Species Distribution ◽  
Environmental Conditions ◽  
Species Distribution Models ◽  
Large Fraction ◽  
Niche Conservatism ◽  
Invasion History ◽  
Distribution Models ◽  
Schinus Molle ◽  
Suitable Habitats

Background and aim – Invasive plants should only colonize habitats meeting the environmental conditions included in their native niches. However, if they invade habitats with novel environmental conditions, this can induce shifts in their niches. This may occur in plants with long invasion histories because they interacted with the environmental conditions of invaded regions over long periods of time. We focused on this issue and evaluated whether the niche of the oldest plant invader reported in Mexico, the Peruvian peppertree, is still conserved after almost 500 years of invasion history. Methods – We compared climatic niches of the species between the native and invaded region. We later used species distribution models (SDM) to visualize the geographical expression of both niches in Mexico. Results – The invasive niche of the Peruvian peppertree is fully nested within the native niche. Although this suggests that the niche is conserved, this also indicates that a large fraction of the native niche is empty in the invaded region. The SDM from the native region indicated that Mexico contains habitats meeting the conditions included in this empty fraction of the native niche and, thus, this invasion should continue expanding. Nevertheless, the SDM calibrated with data from the invaded region indicated that peppertrees have colonized all suitable habitats indicated by its invasive niche and, thus, their populations should no longer expand. Conclusion – Our results suggests that the niche of the Peruvian peppertree is partially conserved in Mexico. This may have occurred because individuals introduced into Mexico constituted a small, nonrepresentative sample of the full niche of the species.

scholarly journals Constructing ecological indices for urban environments using species distribution models

2021 ◽  
Author(s):  
Ariel Levi Simons ◽  
Stevie Caldwell ◽  
Michelle Fu ◽  
Jose Gallegos ◽  
Michael Gatheru ◽  
...  
Keyword(s):  
Los Angeles ◽  
Species Distribution ◽  
Indicator Species ◽  
Environmental Conditions ◽  
Native Species ◽  
Species Distribution Models ◽  
Urban Environments ◽  
Ecological Indices ◽  
Distribution Models ◽  
The Mean

Abstract In an increasingly urbanized world, there is the need for a framework to assess ecological conditions in these anthropogenically dominated environments. Using species observations from the Global Biodiversity Information Facility (GBIF), along with remotely sensed environmental layers, we used MaxEnt to construct species distribution models (SDMs) of native and non-native species in Los Angeles. 25 native and non-native Indicator species were selected based on the sensitivities of their SDM, as measured by the Symmetric Extremal Dependence Index (SEDI), to environmental gradients. These SDMs were summarized to produce ecological indices of native and non-native biodiversity in Los Angeles. We found native indicator species to have a greater sensitivity to environmental conditions than their non-native counterparts, with the mean SEDI score of native and non-native species MaxEnt models being 0.72 and 0.71 respectively. While both sets of species were sensitive to land use categories and housing density, native species were more sensitive to natural landscape variables while non-native ones were more sensitive to measures of water and soil contamination. Using random forest modeling we also found our native index could be more reliably predicted, given environmental conditions, than its non-native counterpart. The mean Pearson correlation between actual and predicted index values were 0.86 and 0.84 for native and non-native species. From these results we conclude that using SDMs to predict the biodiversity of environmental species is a suitable approach towards evaluating ecological conditions in urban environments, with the environmental sensitivity of native SDMs outperforming non-native ones.

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 Morphological and molecular evidence support the taxonomic separation of the medically important Neotropical spiders Phoneutria depilata (Strand, 1909) and P. boliviensis (F.O. Pickard-Cambridge, 1897) (Araneae, Ctenidae)

ZooKeys ◽  
2021 ◽  
Vol 1022 ◽  
pp. 13-50
Author(s):  
Nicolas A. Hazzi ◽  
Gustavo Hormiga
Keyword(s):  
Species Distribution ◽  
Species Delimitation ◽  
Species Distribution Models ◽  
Allopatric Speciation ◽  
Molecular Evidence ◽  
Niche Conservatism ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Nucleotide Sequence Data ◽  
Occurrence Records

The species of the genus Phoneutria (Ctenidae), also called banana spiders, are considered amongst the most venomous spiders in the world. In this study we revalidate P. depilata (Strand, 1909), which had been synonymized with P. boliviensisis (F.O. Pickard-Cambridge, 1897), using morphological and nucleotide sequence data (COI and ITS-2) together with species delimitation methods. We synonymized Ctenus peregrinoides, Strand, 1910 and Phoneutria colombiana Schmidt, 1956 with P. depilata. Furthermore, we designated Ctenus signativenter Strand, 1910 as a nomen dubium because the exact identity of this species cannot be ascertained with immature specimens, but we note that the type locality suggests that the C. signativenter syntypes belong to P. depilata. We also provide species distribution models for both species of Phoneutria and test hypotheses of niche conservatism under an allopatric speciation model. Our phylogenetic analyses support the monophyly of the genus Phoneutria and recover P. boliviensis and P. depilata as sister species, although with low nodal support. In addition, the tree-based species delimitation methods also supported the separate identities of these two species. Phoneutria boliviensis and P. depilata present allopatric distributions separated by the Andean mountain system. Species distribution models indicate lowland tropical rain forest ecosystems as the most suitable habitat for these two Phoneutria species. In addition, we demonstrate the value of citizen science platforms like iNaturalist in improving species distribution knowledge based on occurrence records. Phoneutria depilata and P. boliviensis present niche conservatism following the expected neutral model of allopatric speciation. The compiled occurrence records and distribution maps for these two species, together with the morphological diagnosis of both species, will help to identify risk areas of accidental bites and assist health professionals to determine the identity of the species involved in bites, especially for P. depilata.

scholarly journals The Need of Species Distribution Models Metadata: Using Species Distribution Model to Address Decision Making on Climate Change

2018 ◽  
Vol 2 ◽  
pp. e25478 ◽  
Author(s):  
Wilian Costa ◽  
Leonardo Miranda ◽  
Rafael Borges ◽  
Antonio Saraiva ◽  
Vera Imperatriz-Fonseca ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Species Distribution ◽  
Environmental Conditions ◽  
Species Distribution Models ◽  
Controlled Vocabulary ◽  
Decision Makers ◽  
Distribution Models ◽  
Mandatory Requirement ◽  
Priority Species

Anthropogenic-induced climate change has already altered the conditions to which species have adapted locally, and consequently, shifts of occurrence areas have been previously reported (Chen et al. 2011). Anticipating the results of climate change is urgent, and using these results efficiently to guide decision-making can help to build strategies to protect species from those changes. Therefore, our objective is to propose the use of climate change impact assessments, obtained through species distribution models (SDMs), to guide decision making. The emphasis will be on data that could help determine the potentially vulnerable species and the priority areas, which could act as climate refuges, as well as wildlife corridors. SDMs are based on species occurrence points, available mainly from biological collections and observations (Franklin 2010). When combined with geospatially explicit layers of abiotic or biotic data (e. g. temperature, precipitation, land use), which defines the ecological requirements of species under study, it can generate species distribution models. These models are projected in the form of maps indicating areas where the species can find the most suitable habitats and, therefore, where one can most likely find them. To support public policies decision, the generation of robust and reliable model is an important factor. A minimum number of six occurrence points is a mandatory requirement, with non-overlapping area as a filter criteria. Unfortunatelly, in Brasil, as well as in Latin America in general, this type of data is scarce. Thus, with SDMs, four types of decision making information data regarding priority species and areas could be obtained (Fig. 1). Size of potential occurrence areas: species that have a small area of occurrence are potentially vulnerable, since they present endemism, usually living in restricted environmental conditions. In this case, any small change in environmental conditions can result in the extinction of the impacted species. Thus, this region needs to be protected. Difference between current and future area: species presenting the most significant reduction in potential areas should be prioritized by decision-makers. This measurement could be used as an indication of vulnerability. Even species that have no predicted area reduction or an increase could be prioritized in management programs due to its role in the complex interaction networks of ecosystem services, such as pollinators, seed dispersers or disease control. These species could be more resilient to network interaction changes due climate, and possibly are better able to provide their services in the extreme unfavorable climate scenarios. Areas that maintain higher species diversity in future scenarios: their protection could be prioritized in restoration and conservation programs. Especially in cases involving multiple species, those areas could be considered as climate refuges by decision-makers. Additionally, for the reconstruction and use of SDM published in peer-reviewed journals, it is necessary that all pieces of information about models, its generation, ensemble methods, data cleaning and data quality criteria applied should be available. The availability of the four above mentioned types of information can help on decision-making strategies aiming the protection of priority species and areas. In conclusion, SDMs present essential information about the present and future impacts of projected climate change and their derived data could be preserved using a standard controlled vocabulary.

scholarly journals Dynamic Species Distribution Models in the Marine Realm: Predicting Year-Round Habitat Suitability of Baleen Whales in the Southern Ocean

2021 ◽  
Vol 8 ◽  
Author(s):  
Ahmed El-Gabbas ◽  
Ilse Van Opzeeland ◽  
Elke Burkhardt ◽  
Olaf Boebel
Keyword(s):  
Species Distribution ◽  
Environmental Conditions ◽  
Habitat Suitability ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Background Information ◽  
Baleen Whale ◽  
Distribution Models ◽  
Baleen Whales ◽  
Marine Realm

Species distribution models (SDMs) relate species information to environmental conditions to predict potential species distributions. The majority of SDMs are static, relating species presence information to long-term average environmental conditions. The resulting temporal mismatch between species information and environmental conditions can increase model inference’s uncertainty. For SDMs to capture the dynamic species-environment relationships and predict near-real-time habitat suitability, species information needs to be spatiotemporally matched with environmental conditions contemporaneous to the species’ presence (dynamic SDMs). Implementing dynamic SDMs in the marine realm is highly challenging, particularly due to species and environmental data paucity and spatiotemporally biases. Here, we implemented presence-only dynamic SDMs for four migratory baleen whale species in the Southern Ocean (SO): Antarctic minke, Antarctic blue, fin, and humpback whales. Sightings were spatiotemporally matched with their respective daily environmental predictors. Background information was sampled daily to describe the dynamic environmental conditions in the highly dynamic SO. We corrected for spatial sampling bias by sampling background information respective to the seasonal research efforts. Independent model evaluation was performed on spatial and temporal cross-validation. We predicted the circumantarctic year-round habitat suitability of each species. Daily predictions were also summarized into bi-weekly and monthly habitat suitability. We identified important predictors and species suitability responses to environmental changes. Our results support the propitious use of dynamic SDMs to fill species information gaps and improve conservation planning strategies. Near-real-time predictions can be used for dynamic ocean management, e.g., to examine the overlap between habitat suitability and human activities. Nevertheless, the inevitable spatiotemporal biases in sighting data from the SO call for the need for improving sampling effort in the SO and using alternative data sources (e.g., passive acoustic monitoring) in future SDMs. We further discuss challenges of calibrating dynamic SDMs on baleen whale species in the SO, with a particular focus on spatiotemporal sampling bias issues and how background information should be sampled in presence-only dynamic SDMs. We also highlight the need to integrate visual and acoustic data in future SDMs on baleen whales for better coverage of environmental conditions suitable for the species and avoid constraints of using either data type alone.

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.

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