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.

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 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 Conservation biogeography of lotic fishes in the Missouri and Colorado River basins

2019 ◽  
Author(s):  
◽  
Landon Lee Pierce
Keyword(s):  
Species Distribution ◽  
Fish Assemblage ◽  
Native Species ◽  
Species Distribution Models ◽  
Colorado River ◽  
River Basins ◽  
Assemblage Structure ◽  
Native Fish ◽  
Fish Assemblage Structure ◽  
Distribution Models

To improve our understanding of lotic fish ecology and improve conservation efforts, I 1) identified potentially ecologically important tributaries (PEITs) and evaluated their effects on fish assemble structure, 2) evaluated factors affecting spatial transferability of species distribution models (SDMs), and 3) evaluated the drivers of non-native fish establishment in the Missouri and Colorado River basins (MRB and CRB). The effects of PEIT likely vary among rivers as all Missouri River PEITs affected fish assemblage structure, but only half of upper Colorado River basin PEITs affected fish assemblage structure. Species distribution models transferred from the MRB to the CRB for 15 of 25 species, but transferability was not predictable based on species characteristics, re-enforcing the hypothesis that transferability is species-and contextspecific. Support for Human Activity, Biotic Resistance and Biotic Acceptance hypotheses as the drivers of non-native fish establishment varied by family, but these hypotheses rarely explained significant variability in the probability of non-native Salmonidae, Catostomidae, and Cyprinidae occurrence. These results may suggest that other factors (e.g., natural factors) drive non-native species distributions at the spatial (i.e., grain-stream segment; extents-physiographic divisions, and MRB and CRB combined) and taxonomic (i.e., family) scales considered in this study. This study aids conservations efforts by providing an efficient approach for identifying ecologically important tributaries and improving predictions of non-native species establishment.

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.

scholarly journals An invasion risk map for non-native aquatic macrophytes of the Iberian Peninsula

2017 ◽  
Vol 74 (1) ◽  
pp. 055 ◽  
Author(s):  
Argantonio Rodríguez-Merino ◽  
Rocío Fernández-Zamudio ◽  
Pablo García-Murillo
Keyword(s):  
Iberian Peninsula ◽  
Species Distribution ◽  
Native Species ◽  
Aquatic Macrophytes ◽  
Species Distribution Models ◽  
Green Revolution ◽  
High Sensitivity ◽  
Risk Map ◽  
Invasion Risk ◽  
Distribution Models

Freshwater systems are particularly susceptible to non-native organisms, owing to their high sensitivity to the impacts that are caused by these organisms. Species distribution models, which are based on both environmental and socio-economic variables, facilitate the identification of the most vulnerable areas for the spread of non-native species. We used MaxEnt to predict the potential distribution of 20 non-native aquatic macrophytes in the Iberian Peninsula. Some selected variables, such as the temperature seasonality and the precipitation in the driest quarter, highlight the importance of the climate on their distribution. Notably, the human influence in the territory appears as a key variable in the distribution of studied species. The model discriminated between favorable and unfavorable areas with high accuracy. We used the model to build an invasion risk map of aquatic macrophytes for the Iberian Peninsula that included results from 20 individual models. It showed that the most vulnerable areas are located near to the sea, the major rivers basins, and the high population density areas. These facts suggest the importance of the human impact on the colonization and distribution of non-native aquatic macrophytes in the Iberian Peninsula, and more precisely agricultural development during the Green Revolution at the end of the 70’s. Our work also emphasizes the utility of species distribution models for the prevention and management of biological invasions.

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