Use of predictive distribution models to describe habitat selection by bats in Colorado, USA

Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.

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Setting Favourable Habitat Reference Values for breeding birds: general principles and examples for passerine birds

Bird Conservation International ◽

10.1017/s0959270913000488 ◽

2013 ◽

Vol 24 (3) ◽

pp. 263-271 ◽

Cited By ~ 2

Author(s):

MATTIA BRAMBILLA ◽

CLAUDIO CELADA ◽

MARCO GUSTIN

Keyword(s):

Habitat Selection ◽

Habitat Quality ◽

Reference Values ◽

Breeding Success ◽

Suitable Habitat ◽

Distribution Models ◽

Species Population ◽

Definition Of ◽

Range Of Values

SummarySetting Favourable Reference Values (FRVs) can assist the definition of the conservation status of a species. FRVs may consider population, habitat, and range. FRVs can indicate a range of values for different parameters, which should allow the long-term persistence of a species/population. We propose a method for the definition of reference values for the habitat (FRV-H or HRV) of breeding bird species. HRV should cover habitat extent and quality, both required to ensure long-term persistence. Extent HRV should express a measure of suitable area, whereas quality HRV could be defined as the range of values for habitat variables known to affect habitat quality. To define an extent HRV, we built species distribution models (SDMs) and set extent HRV as the extent of potentially suitable habitat under a conservative approach. Quality HRV should refer to environmental determinants/correlates of occurrence and breeding success, and should be defined by the identification of the habitat factors affecting occurrence and reproduction. When habitat selection is adaptive, habitat suitability may approximate habitat quality, being correlated with breeding success. In that case, fine-scaled habitat/distribution models may be used to identify determinants/correlates of reproductive output, and such species-habitat relationships may help define quality HRV. We show examples using the Red-backed Shrike Lanius collurio as a model. The use of habitat selection models, which can be made spatially explicit generating distribution models, may assist the definition of both extension and quality HRVs. Species-habitat models can allow the individuation of factors and relative values affecting species occurrence/reproduction (quality HRV), and the definition of the spatial distribution and quantity of potentially suitable habitat (extent HRV). Our approach is one of the possible ones, aiming at finding a “suitable” trade-off between affordable data and scientific precision. HRVs should be used together with population and range FRVs to assess the status of a species/population.

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Defining priority areas for blue whale conservation and investigating overlap with vessel traffic in Chilean Patagonia, using a fast-fitting movement model

Scientific Reports ◽

10.1038/s41598-021-82220-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Luis Bedriñana-Romano ◽

Rodrigo Hucke-Gaete ◽

Francisco A. Viddi ◽

Devin Johnson ◽

Alexandre N. Zerbini ◽

...

Keyword(s):

Habitat Selection ◽

Species Conservation ◽

Decisive Role ◽

Single Step ◽

Blue Whale ◽

Distribution Models ◽

Movement Model ◽

Priority Areas ◽

Chilean Patagonia ◽

Order Of Magnitude

AbstractDefining priority areas and risk evaluation is of utmost relevance for endangered species` conservation. For the blue whale (Balaenoptera musculus), we aim to assess environmental habitat selection drivers, priority areas for conservation and overlap with vessel traffic off northern Chilean Patagonia (NCP). For this, we implemented a single-step continuous-time correlated-random-walk model which accommodates observational error and movement parameters variation in relation to oceanographic variables. Spatially explicit predictions of whales’ behavioral responses were combined with density predictions from previous species distribution models (SDM) and vessel tracking data to estimate the relative probability of vessels encountering whales and identifying areas where interaction is likely to occur. These estimations were conducted independently for the aquaculture, transport, artisanal fishery, and industrial fishery fleets operating in NCP. Blue whale movement patterns strongly agreed with SDM results, reinforcing our knowledge regarding oceanographic habitat selection drivers. By combining movement and density modeling approaches we provide a stronger support for purported priority areas for blue whale conservation and how they overlap with the main vessel traffic corridor in the NCP. The aquaculture fleet was one order of magnitude larger than any other fleet, indicating it could play a decisive role in modulating potential negative vessel-whale interactions within NCP.

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Synonymy of Orthomus susanae Serrano & Borges, 2009 with Orthomus annae (Donabauer, 2008) (Coleoptera: Carabidae)

Zootaxa ◽

10.11646/zootaxa.3219.1.4 ◽

2012 ◽

Vol 3219 (1) ◽

pp. 67 ◽

Cited By ~ 1

Author(s):

ARTUR R.M. SERRANO ◽

CARLOS A.S. AGUIAR ◽

MÁRIO BOIEIRO ◽

PAULO A.V. BORGES ◽

CARLA REGO ◽

...

Keyword(s):

New Species ◽

Ground Beetle ◽

Predictive Distribution ◽

Medium Size ◽

Forest Fragments ◽

Beetle Species ◽

Distribution Models ◽

Laurel Forest ◽

Xixth Century ◽

Terrestrial Biodiversity

Knowledge of the biodiversity of Madeiran islands has for long attracted entomologists in search of new, rare or biologically interesting beetle species. During the XIXth century, Trevor Wollaston extensively sampled the Madeiran islands, compiling a list of almost 700 beetle species, many of them new to science and described by him (e.g., Wollaston 1854, 1865). Thus, for many decades it was thought that the inventory of Madeiran beetle diversity was complete (Machado 2006, but see Lobo & Borges 2010). However, particularly in recent decades, a number of beetle species (most of them of small size and from groups taxonomically more difficult) have been described (see a complete list in Borges et al. 2008b) and important taxonomic revisions have also been made incorporating critical analysis by taxonomic experts. For these reasons, it was as a surprise to find a medium-size and yet undescribed ground beetle species following an extensive survey of epigean arthropods occurring in Madeiran laurel forest fragments. The new species was described in a generic revision of Madeiran Orthomus Chaudoir, 1838 submitted to Zootaxa (Serrano et al. 2009) and the species was included in the comprehensive list of Madeiran terrestrial biodiversity (Borges et al. 2008a), a reference publication on Madeiran fauna and flora published in May 2008. This book also presented a chapter where the distribution of the new species was modelled using predictive distribution models (Jiménez-Valverde et al. 2008).

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From descriptive to predictive distribution models: a working example with Iberian amphibians and reptiles

Frontiers in Zoology ◽

10.1186/1742-9994-3-8 ◽

2006 ◽

Vol 3 (1) ◽

Cited By ~ 14

Author(s):

JW Arntzen

Keyword(s):

Predictive Distribution ◽

Distribution Models

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Modeling Rare Species Distribution at the Edge: The Case for the Vulnerable Endemic Pyrenean Desman in France

The Scientific World JOURNAL ◽

10.1100/2012/612965 ◽

2012 ◽

Vol 2012 ◽

pp. 1-6 ◽

Cited By ~ 7

Author(s):

M. Williams-Tripp ◽

F. J. N. D'Amico ◽

C. Pagé ◽

A. Bertrand ◽

M. Némoz ◽

...

Keyword(s):

Species Distribution ◽

Species Distribution Model ◽

Action Plan ◽

Predictive Distribution ◽

Distribution Model ◽

Distribution Models ◽

Conservation Actions ◽

Present Distribution ◽

Northern Edge ◽

Spatial Transferability

The endemic Pyrenean Desman (Galemys pyrenaicus) is an elusive, rare, and vulnerable species declining over its entire and narrow range (Spain, Portugal, France, and Andorra). The principal set of conservation measures in France is a 5-years National Action Plan based on 25 conservation actions. Priority is given to update its present distribution and develop tools for predictive distribution models. We aim at building the first species distribution model and map for the northern edge of the range of the Desman and confronting the outputs of the model to target conservation efforts in the context of environmental change. Contrasting to former comparable studies, we derive a simpler model emphasizing the importance of factors linked to precipitation and not to the temperature. If temperature is one of the climate change key factors, depicted shrinkage in Desman distribution could be lower or null at the northern (French) edge suggesting thus a major role for this northern population in terms of conservation of the species. Finally, we question the applied issue of temporal and spatial transferability for such environmental favourability models when it is made at the edge of the distribution range.

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AUC: a misleading measure of the performance of predictive distribution models

Global Ecology and Biogeography ◽

10.1111/j.1466-8238.2007.00358.x ◽

2008 ◽

Vol 17 (2) ◽

pp. 145-151 ◽

Cited By ~ 1518

Author(s):

Jorge M. Lobo ◽

Alberto Jiménez-Valverde ◽

Raimundo Real

Keyword(s):

Predictive Distribution ◽

Distribution Models

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Incorporating classified dispersal assumptions in predictive distribution models – A case study with grasshoppers and bush-crickets

Ecological Modelling ◽

10.1016/j.ecolmodel.2011.04.010 ◽

2011 ◽

Vol 222 (13) ◽

pp. 2130-2141 ◽

Cited By ~ 16

Author(s):

Jörn Buse ◽

Eva Maria Griebeler

Keyword(s):

Predictive Distribution ◽

Distribution Models ◽

Bush Crickets

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Modeling the ecological niche and features of coprobiontic fungi distribution in Asia by the example of Cyathus stercoreus

Samara Journal of Science ◽

10.17816/snv2021103105 ◽

2021 ◽

Vol 10 (3) ◽

pp. 41-46

Author(s):

Vyacheslav Aleksandrovich Vlasenko ◽

Dejidmaa Turmunkh ◽

Chechekmaa Dembirelovna Nazyn ◽

Anastasia Vladimirovna Vlasenko

Keyword(s):

Ecological Niche ◽

Habitat Type ◽

Predictive Distribution ◽

Habitat Modification ◽

Mean Annual Precipitation ◽

Distribution Models ◽

Northern Border ◽

Wide Range ◽

Bioclimatic Variables ◽

Cyathus Stercoreus

Species distribution is undergoing rapid changes in the face of habitat modification and climate change. This leads to concerns about the conservation of declining species and raises ecological questions about the processes that govern species ranges and niches. As a consequence, the predictive distribution models which match species records to patterns in abiotic environmental variables have become an established tool in ecology and conservation. Maximum entropy spatial distribution modelling (MaxEnt) solves this problem by inferring species distributions and environmental tolerance based on the occurrence data. The objectives of this research were the ecological niche and running the habitat suitability modelling on dung fungal species Cyathus stercoreus based on its bioclimatic and substrate features within Asia. We constructed a map of the current geographical distribution of the dung fungus Cyathus stercoreus using MaxEnt method. We included in the model 19 WorldClim bioclimatic variables with the corresponding altitude data, and seven spatially well-dispersed species occurrence records. Despite its narrow substrate specialization, Cyathus stercoreus is climatically quite plastic and is able to develop in a wide range of variations in mean annual temperatures and mean annual precipitation, which follows from the analysis of a two-dimensional niche based on two climatic variables using the Envelope method. Modeling the distribution of basidiomycete dung fungi using the Cyathus stercoreus as an example showed that the area of their potential distribution with a zone of favorable climate is very large. Most of the zone with a favorable climate is located in the area with the probability of the presence of species up to 70%. Cyathus stercoreus is not associated with any particular habitat type. On the territory of Russia, in the south of Siberia, the species is located on the northern border of its range in the area with the least favorable bioclimatic environmental factors.

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