scholarly journals Soil-Related Predictors for Distribution Modelling of Four European Crayfish Species

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2280
Author(s):  
Andrei Dornik ◽  
Mihaela Constanța Ion ◽  
Marinela Adriana Chețan ◽  
Lucian Pârvulescu
Keyword(s):  
Soil Properties ◽  
Species Distribution ◽  
Field Data ◽  
Cohesive Soil ◽  
Species Distribution Modelling ◽  
Environmental Predictors ◽  
Forest Modelling ◽  
Distribution Modelling ◽  
Sediment Cover ◽  
Crayfish Species

One of the most critical challenges in species distribution modelling is testing and validating various digitally derived environmental predictors (e.g., remote-sensing variables, topographic variables) by field data. Therefore, here we aimed to explore the value of soil properties in the spatial distribution of four European indigenous crayfish species. A database with 473 presence and absence locations in Romania for Austropotamobius bihariensis, A. torrentium, Astacus astacus and Pontastacus leptodactylus was used in relation to eight digitalised soil properties. Using random forest modelling, we found a preference for dense soils with lower coarse fragments content together with deeper sediment cover and higher clay values for A. astacus and P. leptodactylus. These descriptors trigger the need for cohesive soil river banks as the microenvironment for building their burrows. Conversely, species that can use banks with higher coarse fragments content, the highland species A. bihariensis and A. torrentium, prefer soils with slightly thinner sediment cover and lower density while not influenced by clay/sand content. Of all species, A. astacus was found related with higher erosive soils. The value of these soil-related digital descriptors may reside in the improvement of approaches in crayfish species distribution modelling to gain adequate conservation measures.

scholarly journals Predicted distribution and habitat loss for the Endangered black-faced black spider monkey Ateles chamek in the Amazon

Oryx ◽  
2018 ◽  
Vol 54 (5) ◽  
pp. 699-705 ◽  
Author(s):  
Rafael M. Rabelo ◽  
Jonas R. Gonçalves ◽  
Felipe E. Silva ◽  
Daniel G. Rocha ◽  
Gustavo R. Canale ◽  
...  
Keyword(s):  
Species Distribution ◽  
Protected Area ◽  
Forest Cover ◽  
Species Distribution Modelling ◽  
Spider Monkey ◽  
Area Network ◽  
Environmental Predictors ◽  
Distribution Modelling ◽  
Protected Area Network ◽  
Area Of Occupancy

AbstractThe rate of deforestation in the Amazon is increasing. Predictive models estimate that as a result of agricultural expansion 40% of these forests will be lost by 2050. As a consequence the habitat of forest-dwelling species such as the Endangered black-faced black spider monkey Ateles chamek is being lost, particularly along the arc of deforestation in the Brazilian Amazon. We used species distribution modelling to (1) define the distribution of this spider monkey, using environmental predictors, (2) calculate the area of this distribution covered by the protected area network, and (3) calculate the expected loss of the species’ habitat under future scenarios of deforestation. We found that the species occupies only c. 28% of its extent of occurrence. Only 32% of the species’ area of occupancy is legally protected, and the modelling suggests that 31–40% of the species’ habitat will be lost by 2050. We highlight three unprotected regions with extensive forest cover that are predicted to become severely deforested by 2050 as priority regions for expanding the protected area network. We also propose landscape management and restoration in three human-modified regions. Our study provides an example of how species distribution modelling can be applied to assess threats to species and support decision makers in implementing conservation actions.

scholarly journals The value of local community knowledge in species distribution modelling for a threatened Neotropical parrot

2021 ◽  
Author(s):  
Rebecca Biddle ◽  
Ivette Solis-Ponce ◽  
Martin Jones ◽  
Stuart Marsden ◽  
Mark Pilgrim ◽  
...  
Keyword(s):  
Species Distribution ◽  
Field Data ◽  
Species Distribution Modelling ◽  
Data Models ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Community Knowledge ◽  
Occurrence Data ◽  
Community Data

AbstractSpecies distribution models are widely used in conservation planning, but obtaining the necessary occurrence data can be challenging, particularly for rare species. In these cases, citizen science may provide insight into species distributions. To understand the distribution of the newly described and Critically EndangeredAmazona lilacina,we collated species observations and reliable eBird records from 2010–2020. We combined these with environmental predictors and either randomly generated background points or absence points generated from eBird checklists, to build distribution models using MaxEnt. We also conducted interviews with people local to the species’ range to gather community-sourced occurrence data. We grouped these data according to perceived expertise of the observer, based on the ability to identifyA. lilacinaand its distinguishing features, knowledge of its ecology, overall awareness of parrot biodiversity, and the observation type. We evaluated all models using AUC and Tjur R2. Field data models built using background points performed better than those using eBird absence points (AUC = 0.80 ± 0.02, Tjur R2 = 0.46 ± 0.01 compared to AUC = 0.78 ± 0.03, Tjur R2 = 0.43 ± 0.21). The best performing community data model used presence records from people who were able recognise a photograph ofA. lilacinaand correctly describe its distinguishing physical or behavioural characteristics (AUC = 0.84 ± 0.05, Tjur R2 = 0.51± 0.01). There was up to 92% overlap between the field data and community data models, which when combined, predicted 17,772 km2of suitable habitat. Use of community knowledge offers a cost-efficient method to obtain data for species distribution modelling; we offer recommendations on how to assess its performance and present a final map of potential distribution forA. lilacina.

scholarly journals Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change

2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Climatic Conditions ◽  
First Century ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Major Barrier ◽  
Methodological Choices ◽  
Biodiversity And Ecosystem Function

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.

Evolutionary algorithms for species distribution modelling: A review in the context of machine learning

2019 ◽  
Vol 392 ◽  
pp. 179-195 ◽  
Author(s):  
Sacha Gobeyn ◽  
Ans M. Mouton ◽  
Anna F. Cord ◽  
Andrea Kaim ◽  
Martin Volk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithms ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Distribution Modelling
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