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opencc-zeroJan 2021View details →

Data from: Habitat modeling of Irrawaddy dolphins (Orcaella brevirostris) in the eastern Gulf of Thailand

Aim: The Irrawaddy dolphin (Orcaella brevirostris) is an endangered cetacean found throughout Southeast Asia. The main threat to this species is human encroachment, led by entanglement in fishing gear. Information on this data-poor species' ecology and habitat use is needed to effectively inform spatial management. Location: We investigated the habitat of a previously unstudied group of Irrawaddy dolphins in the eastern Gulf of Thailand, between the villages of Laem Klat and Khlong Yai, in Trat Province. This location is important as government groups plan to establish a marine protected area. Methods: We carried out boat-based visual line transect surveys with concurrent oceanographic measurements and used hurdle models to evaluate this species' patterns of habitat use in this area. Results: Depth most strongly predicted dolphin presence, while temperature was a strong predictor of group size. The highest probability of dolphin presence occurred at around 10.0 m with an optimal depth range of 7.50 to 13.05 m. The greatest number of dolphins was predicted at 24.93oC with an optimal range between 24.93 and 25.31oC. Dolphins are most likely to occur in two primary locations, one large region in the center of the study area (11o54'18"N to 11o59'23"N) and a smaller region in the south (11o47'28"N to 11o49'59"N). Protections for this population will likely have the greatest chance of success in these two areas. Main Conclusions: The results of this work can inform management strategies within the immediate study area by highlighting areas of high habitat use that should be considered for marine spatial planning measures, such as the creation of marine protected areas. Species distribution models for this species in Thailand can also assist conservation planning in other parts of the species' range by expanding our understanding of habitat preferences.

opencc-by-4.0Nov 2023View details →

Orthophotos and 2D hydraulic modelling results used for habitat suitability modelling of the River Inn section (river km 35.3-48) in SE Germany

The aerial RGB picture acquisition was performed on October 7 (bypass channel) and 11 (side channel) 2022 using a DJI-Matrice 210 V2 RTK drone. For the image acquisition, the drone mounted the DJI Zenmuse X5S RGB camera. The flight was realized at an altitude of about 120 m, ensuring a lateral and longitudinal overlap of the images of about 80%. Gound Control Points (GCPs) have been disposed along the study site, and their position georeferenced using a Emlid Reach RS2 RTK GPS system.  After data collection, an RGB orthomosaic with a spatial resolution of 25 cm was generated, using PIX4Dmapper  v4.7.5 (www.pix4d.com).The hydrodynamic simulations were performed with the freeware software BASEMENT v3.2 (https://basement.ethz.ch), which solves the 2D shallow-water equations using a finite volume approach over two-dimensional unstructured meshes. Computational meshes were created using the QGIS plugin BASEmesh 2, with spatially varying element sizes, which were set to be finer in areas expected to be suitable for spawning and as nursery grounds, or when needed to more accurately represent the local morphological complexity.

opencc-zeroMar 2024View details →

Targeting fin whale conservation in the North-Western Mediterranean Sea: Insights on movements and behaviour from biologging and habitat modelling

Biologging and habitat modelling are key tools supporting the development of conservation measures and mitigating the effects of anthropogenic pressures on marine species. Here, we analysed satellite telemetry data and foraging habitat preferences in relation to chlorophyll-a productivity fronts to understand the movements and behaviour of endangered Mediterranean fin whales (Balaenoptera physalus) during their spring-summer feeding aggregation in the North-Western Mediterranean Sea. Eleven individuals were equipped with Argos satellite transmitters across three years, with transmissions averaging 23.5 ± 11.3 days. Hidden Markov Models were used to identify foraging behaviour, revealing how individuals showed consistency in their use of seasonal core feeding grounds; this was supported by the distribution of potential foraging habitat. Importantly, tracked whales spent most of their time in areas with no explicit protected status within the study region. This highlights the need for enhanced time- and place-based conservation actions to mitigate the effects of anthropogenic impacts for this species, notably ship strike risk and noise disturbance in an area of exceptionally high maritime traffic levels. These findings strengthen the need to further assess critical habitats and Important Marine Mammal Areas that are crucial for focussed conservation, management, and mitigation efforts.

opencc-zeroApr 2024View details →

Evaluating the predictors of habitat use and successful reproduction in a model bird species using a large scale automated acoustic array

The emergence of continental to global scale biodiversity data has led to growing understanding of patterns in species distributions, and the determinants of these distributions, at large spatial scales. However, identifying the specific mechanisms, including demographic processes, and determining species distributions remains difficult, as large-scale data are typically restricted to observations of only species presence. New remote automated approaches for collecting data, such as automated recording units (ARUs), provide a promising avenue towards direct measurement of demographic processes, such as reproduction, that cannot feasibly be measured at scale by traditional survey methods. In this study, we analyze data collected by ARUs from 452 survey points across an approximately 1500 km study region to compare patterns in adult and juvenile distributions in the Great Horned Owl (Bubo virginianus). We specifically examine whether habitat associated with successful reproduction is the same as that associated with adult presence. We postulated that congruence between these two distributions would suggest that all areas of the species' range contribute equally to maintenance of the population, whereas significant differences would suggest more specificity in the species' requirements for successful reproduction. We filtered adult and juvenile calls of the species for manual review using automated classification and constructed single season occupancy models to compare land cover and vegetation covariates which significantly predicted presence of each life stage. We found that habitat use by adults was significantly predicted by increasing amounts of forest cover, reduced forest basal area, and lower elevations whereas juvenile presence was significantly predicted only by decreasing amounts of forest cover, a pattern opposite that of adults. These results show that presence of adult Great Horned Owls is not a sufficient proxy for locations at which reproduction occurs, and also demonstrate a highly scalable workflow that could be used for similar analyses in other sound-producing species.

opencc-by-4.0Nov 2024View details →

Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

opencc-by-4.0Nov 2024View details →

Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Fig.1 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig.1. Sampling sites for Vestia turgida in Ukraine (photo by O. Baidashnikov).

Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 5. Partial dependence plot for terrain roughness index (tri).

Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 7. Partial dependence plot for silt content (SLT).

Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 6. Partial dependence plot for pH water (phh2o).

opencc-zeroMay 2022View details →

Data from: Occurrence-habitat mismatching and niche truncation when modelling distributions affected by anthropogenic range contractions

Aims: Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence-habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location: Virtual Southeast Asia Methods: Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past, recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results: Greater ARC resulted in greater occurrence-habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence-habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions: We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of anthropogenic range contractions, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximise the temporal range of anthropogenic predictors, exclude mismatched occurrences, and test for residual data misrepresentations.

opencc-zeroMay 2022View details →

Modelling the potential global distribution of suitable habitat for the biological control agent Heterorhabditis indica

Entomopathogenic nematode (EPN) Heterorhabditis indica is a promising biocontrol candidate. Despite the acknowledged importance of EPN in pest control, no extensive data sets or maps have been developed on their distribution at global level. This study is the first attempt to generate Ecological Niche Models (ENM) for H. indica and its global Habitat Suitability Map (HSM) to generate biogeographical information and predicts its global geographical range of prospective areas for its exploration and to help identify the suitable release areas for biocontrol purpose. The aim of the modelling exercise was to access the influence of temperature and soil moisture on the biogeographical patterns of H. indica at the global level. CLIMEX software was used to model the distribution of H. indica and access to the influence of environmental variable on its global distribution. In total, 162 records of H. indica occurrence from 27 countries over 25 years was combined to generate the known distribution data. The model was further fine-tuned using the direct experimental observations of the H. indica's growth response to temperature and soil moisture. Model predicts much of the tropics and subtropics has suitable climatic conditions for H. indica. It further predicts that H. indica distribution can extends into warmer temperate climates. Examination of the model output, predictions maps at a global level indicate that H. indica distribution may be limited by cold stress, heat stress and dry stresses in different areas. However, cold stress appears to be the major limiting factor. This study, highlighted an efficient way to construct HSM for EPN potentially useful in the search/release of target species in new locations. The study showed that H. indica which is known as warm adapted EPN generally found in tropics and subtropics can potentially establish itself in warmer temperate climates as well. The model can also be used to decide the release timing of EPN by adjusting with season for maximum growth. The model developed in the current study clearly identified the value and potential of Habitat Suitability Map (HSM) in planning of future surveys and application of H. indica.

opencc-zeroJun 2022View details →

Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data

As landscape-scale conservation models grow in prominence, assessments of how wildlife utilise multiple-use landscapes are required to inform effective conservation and management planning. Such efforts should strive to incorporate multi-species perspectives to maximise value for conservation, and should account for scale to accurately capture species-environment relationships. We show that the random forest machine learning algorithm can be used to model large-scale sign-based data in a multi-scale framework. We used this method to investigate scale-dependent habitat associations for 16 mammal species of high conservation importance across the southern Kavango Zambezi (KAZA) Transfrontier Conservation Area in Botswana and Zimbabwe. Our findings revealed substantial variation in the factors shaping habitat use across species, and illustrate that different species often have divergent responses to the same environmental and anthropogenic factors, and differ in the scales at which they respond to them. For all variables across all species, scale optimisation most often selected our largest scale. Precipitation, soil nutrients, and vegetation appeared to be the most important factors determining mammal distributions, likely through their associations with food resources for herbivores and, in turn, prey availability for carnivores. Anthropogenic pressures also had an important influence on habitat use, with many species selecting against areas with high cattle density. The variety of relationships with human density indicated that species vary in their tolerance of humans. We found a consistent positive relationship with areas under high protection, and negative relationship with unprotected and less-strictly protected areas. Policy implications: This study highlights the importance of adopting a multi-scale, multi-species approach for critical decision-making processes that depend on understanding wildlife distributions and habitat associations, such as protected area, corridor, and buffer zone prioritisation. We use our findings to identify changing rainfall patterns and increasing livestock numbers as two emerging trends that may impact wildlife distributions, both within sub-Saharan Africa and on a global scale.

opencc-zeroAug 2022View details →

Climate change effects on deep-water corals – habitat suitability model input data

Deep-water corals are protected in the seas around New Zealand by legislation that prohibits intentional damage and removal, and by marine protected areas where bottom trawling is prohibited. However, these measures do not protect them from the impacts of a changing climate and ocean acidification. To enable adequate future protection from these threats we require knowledge of the present distribution of corals and the environmental conditions that determine their preferred habitat, as well as the likely future changes in these conditions, so that we can identify areas for potential refugia. In this study, we built habitat suitability models for 12 taxa of deep-water corals using a comprehensive set of sample data and predicted present and future seafloor environmental conditions from an earth system model specifically tailored for the South Pacific. These models predicted that for most taxa there will be substantial shifts in the location of the most suitable habitat and decreases in the area of such habitat by the end of the 21st century, driven primarily by decreases in seafloor oxygen concentrations, shoaling of aragonite and calcite saturation horizons, and increases in nitrogen concentrations. The current network of protected areas in the region appear to provide little protection for most coral taxa, as there is little overlap with areas of highest habitat suitability, either in the present or the future. We recommend an urgent re-examination of the spatial distribution of protected areas for deep-water corals in the region, utilising spatial planning software that can balance protection requirements against value from fishing and mineral resources, take into account the current status of the coral habitats after decades of bottom trawling, and consider connectivity pathways for colonisation of corals into potential refugia.

opencc-by-4.0Aug 2022View details →

Fig. 4 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 4. Response curves, representing the dependence of predicted suitability both on the

opencc-by-4.0Aug 2022View details →

Fig. 1 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 1. Satellite view of the study area. White circles represent the location of the colonies

opencc-by-4.0Dec 2017View details →

Dataset: Habitat suitability models to make conservation decisions based on areas of high species richness and endemism

This repository contains the files associated with the following article: Hernández-Quiroz NS, EI Badano, F Barragán-Torres, J Flores & C Pinedo-Álvarez. Habitat suitability models to make conservation decisions based on areas of high species richness and endemism. Biodiversity and Conservation, 27, pp. 3185-3200. <a href="https://doi.org/10.1007/s10531-018-1596-9">https://doi.org/10.1007/s10531-018-1596-9</a> The Microsoft Excel file (SM 01-Oak occurrences.xlsx) contains the occurrence points used to calibrate the habitat suitability model of each oak species (59 species in total). This file indicates the name of the species (column A), latitude and longitude of each occurrence point (columns B and C; in geographic coordinates) and the full set of bioclimatic variables (columns D-V) and topographic variables (columns W-Z) associated to each point. These later data are provided as they were gathered from the bioclimatic layers of WorldClim and the topographic layers of the Mexican National Institute of Statistics and Geography. The repository also contains interactive maps indicating the predicted and observed distributions of the 59 Mexican oak species (SM 02-Estimated oak distribution ranges.kmz), and the probability-based and occurrence-based map of oak richness and endemic species (SM 03-Oak richness maps.kmz). These geographic projections are provided in KMZ format to make them easy to visualize in Google Earth (freely available at www.google.com/earth). Details about these KMZ files can be consulted by accessing the file properties after opening them in Google Earth.

opencc-zeroJun 2024View details →

Modeling polar bear (Ursus maritimus) snowdrift den habitat on Alaska's Beaufort Sea coast using SnowDens-3D and ArcticDEM data

Pregnant polar bears (Ursus maritimus) excavate maternal dens in seasonal snowdrifts during fall along Alaska's Beaufort Sea coast to shelter their altricial young during birth and development. With recent sea ice decreases, bears are denning more frequently on land. Each year, the weather and blowing-snow conditions control the creation of snowdrifts across the landscape, and the available snowdrift den habitat can vary widely from one year to the next, depending on the late fall and early winter air temperature, snowfall, and wind speed and direction. We implemented a physics-based, spatiotemporal, polar bear snowdrift den habitat model (SnowDens-3D) across the eastern Alaska Beaufort Sea coast (an area of approximately 17,000 km^2^). High-resolution (2.0 m) topography data were provided by the ArcticDEM, and daily meteorological forcings were provided by NASA's MERRA-2 reanalysis. A 21-year (2000–2020) SnowDens-3D simulation was performed, and model outputs were compared with 91 historical polar bear den locations. The year-specific simulations produced viable den habitat for 98% of the observed den locations. The interannual variation in den habitat area over the 21-year period increased by approximately a factor of three from the minimum year (2001; 554 km^2^) to the maximum year (2017; 1,566 km^2^). This data archive provides the key den and den-habitat datasets produced, used, and analyzed by this project.