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Figure5 in Comparative Study in Habitat Suitability Analysis of Wild Water Buffalo (Bubalus arnee) in Two Flood Plains of Chitwan National Park (CNP), Nepal

Figure5. IVI of Tree Species in Eastern Sector

Figure1 in Comparative Study in Habitat Suitability Analysis of Wild Water Buffalo (Bubalus arnee) in Two Flood Plains of Chitwan National Park (CNP), Nepal

Figure1. Study Area

Figure4 in Comparative Study in Habitat Suitability Analysis of Wild Water Buffalo (Bubalus arnee) in Two Flood Plains of Chitwan National Park (CNP), Nepal

Figure4. IVI of Grass Species in Western Sector

Figure3 in Comparative Study in Habitat Suitability Analysis of Wild Water Buffalo (Bubalus arnee) in Two Flood Plains of Chitwan National Park (CNP), Nepal

Figure3. IVI of Grass Species in Eastern Sector

Figure8 in Comparative Study in Habitat Suitability Analysis of Wild Water Buffalo (Bubalus arnee) in Two Flood Plains of Chitwan National Park (CNP), Nepal

Figure8. Habitat Suitability Map of Western Flood Plain

Figure7 in Comparative Study in Habitat Suitability Analysis of Wild Water Buffalo (Bubalus arnee) in Two Flood Plains of Chitwan National Park (CNP), Nepal

Figure7. Habitat Suitability Map of Eastern Flood Plain

opencc-by-4.0Sep 2024View details →

Mapping the potential habitat suitability, and opportunities of bush encroacher species in Southern Africa: a case study of the SteamBioAfrica project.

Senegalia mellifera (Benth) Seigler & Ebinger. , Dichrostachys cinerea (L.) Wight & Arn. and Terminalia sericea Burch. Ex DC., are three important bush encroacher species that contribute to the well-known ecological process named "thicketization" in Southern Africa. This issue has persisted for many years, impacting species distribution, plant communities, soil, and fauna dynamics. According to climate change projections, Southern Africa is expected to become drier and warmer in future scenarios, creating favourable conditions for proliferation of bush encroacher species. In the paper, we analyze the habitat suitability of these bush encroachers through MaxEnt 3.4.4 under three different future climate models and scenarios. Future projections were made using the shared socio-economic pathways (SSPs) for greenhouse gas concentrations, specifically SSP245-585 across two-time horizons: 2041-2060 and 2061-2180. We utilized <a href="https://www.worldclim.org/data/cmip6/cmip6climate.html">WorldClim 1-km resolution climate data</a> from three global Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) that obtained better results than CMIP5 models (Bouramdane 2022; Mmame and Ngongondo 2024): the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1.2-HR), the Hadley Climate Center Earth System Model (UK-ESM1.0-LL) and the Institute Model for Numerical Mathematics (INM-CM5-0).

opencc-by-4.0Oct 2024View details →

Data for the project: Shifts in habitat suitability for harbour porpoises leads to reduced importance of Marine Protected Areas

Location data from 111 tracked harbour porpoises were collected over the period 1997–2022 as part of a long-term satellite telemetry monitoring program in Denmark. Individual harbour porpoises were fitted with Argos satellite tags after being incidentally trapped in pound nets, which are used in near-shore commercial fisheries in the Inner Danish waters. Argos tags were programmed to make a limited number of daily satellite uplinks and acquire a location at pre-defined times (time of day and duty cycles) to increase the battery lifetime. Duty cycles of the tags varied between 1 and 4 days. Pre-processing of location data included filtering out unlikely locations using the Argos-Filter v7.03 and the removal of locations on land and those collected within 24 hours after tagging to reduce behavioural bias caused by capture and tagging. After the data cleaning process, 9 345 locations collected by 111 harbour porpoises were included in this study with an average tracking duration of 118.5 days per individual (min = 8 days, max = 522 days). Dataset includes coordinates (latitude and longitude ) of each location and the season and time period it was collected.

opencc-zeroDec 2020View details →

Novel insights into habitat suitability for Amazonian freshwater mussels linked with hydraulic and landscape drivers

Novel insights into habitat suitability for two Unionida freshwater mussels, Castalia ambigua Lamarck, 1819 (Hyriidae) and Anodontites elongatus (Swainson, 1823) (Mycetopodidae), is presented on the basis of hydraulic variables linked with the riverbed in six 500 m reaches in an eastern Amazonian river basin. Within the reaches, there was strong habitat heterogeneity in hydrodynamics and substrate composition. In addition, we investigated stressors based on landscape modification that are associated with declines in mussel density. We measured hydraulic variables for each 500 m reach, and landscape stressors at two spatial scales (subcatchment and riparian buffer forest). We used the Random Forest algorithm, a tree-based model, to predict the hydraulic variables linked with habitat suitability for mussels, and to predict which landscape stressors were most associated with mussel density declines. Both mussel species were linked with low substrate heterogeneity and greater riverbed stability (low Froude and Reynolds numbers), especially at high flow (low stream power). Different sediment grain size preferences were observed between mussel species: Castalia ambigua was associated with medium sand, and Anodontites elongatus with medium and fine sand. Declines in mussel density were associated with modifications linked to urbanization at small scales (riparian buffer forest), especially with percent of and distance from rural settlements, distance to the nearest street, and road density. In summary, the high variance explained in both hydraulic and landscape models indicated high predictive power, suggesting that our findings may be extrapolated and used as a baseline to test hypotheses of habitat suitability in other Amazonian rivers for Castalia ambigua and Anodontites elongatus, and also for other freshwater mussel species. Our results highlight the urgent need for aquatic habitat conservation to maintain sheltered habitats during high flow as well as mitigate the effects of landscape modifications at the riparian buffer scale, both of which are important for maintaining dense mussel populations and habitat quality.

opencc-zeroJul 2021View details →

Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol

Aim: To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements, and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high quality absences from OR based on questionnaires sent to observers. Location: Both datasets cover the same large area (Sweden) and time period (2000 -2013). Methods: Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data were assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR. Results: For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species-environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found. Main Conclusions: Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist.

opencc-by-4.0Jun 2023View details →

Predicting geographic distribution and habitat suitability of Opuntia streptacantha in paleoclimatic, current, and future scenarios in Mexico

Geographical records. A total of 825 records (Figure 1), representing the natural distribution historically recognized for O. streptacantha. Maps for past, current, and future models in QGIS format

opencc-zeroApr 2023View details →

Data from: One model to rule them all: Identifying priority bat habitats from multi‐species habitat suitability models

Bats are important components of global ecosystems, providing essential ecosystem services with substantial economic benefit. Yet North American bat populations have been negatively affected by numerous factors (e.g., disease, habitat loss, and wind energy development) with compounding effects. Bats use habitats at a variety of scales, from small, isolated patches to large, contiguous corridors. Landscape‐level research is necessary to identify important habitats, patches, and corridors to strategically target management interventions. We created habitat suitability models (HSMs) for hoary bats (Lasiurus cinereus), eastern red bats (L. borealis), and tri‐colored bats (Perimyotis subflavus) across Illinois, USA using species-specific landscape and climate variables. With the 3 models from this study and a previously published HSM for Indiana bats (Myotis sodalis), we stacked binary HSMs, thereby identifying priority conservation areas across Illinois. Species exhibited different distributional patterns and habitat preferences across Illinois. Multi‐species HSMs highlight high quality habitat (i.e., ecologically important habitat that provides preferred resources for roosting, foraging, and raising young) in southern Illinois and along river riparian areas. This approach identified priority conservation areas mainly following hydrologic zones, which allows managers to strategically target restoration and conservation measures, invest funds in habitat likely to have high return‐on‐investment, and assist with decisions that affect bats (e.g., siting wind turbines and purchasing mitigation lands).

opencc-by-4.0Mar 2023View details →

Fig. 1 in Current and future suitable habitats of a range-restricted species group (Cyrtodactylus chauquangensis) in Vietnam

Fig. 1. The occurrence localities of the C. chauquangensis species group.

opencc-zeroAug 2023View details →

Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models

Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures. We sampled bees and plant species as well as their interactions in a semi-controlled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness. We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips. Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing Hieracium spp. such as Hieracium umbellatum and Pilosella officinarum; Taraxacum spp; Trifolium repens; Lotus corniculatus; Stellaria graminea; and Achillea millefolium would provide resources for diverse bee communities in our region. Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri-environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other.

opencc-zeroSep 2023View details →

Data from: Sampling methodology influences habitat suitability modeling for Chiropteran species

Technological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are utilized independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy. We sought to understand how different data types can influence HSMs using three bat species (L. borealis, L. cinereus, and P. subflavus). We compared the overlap of models created from passive-only (acoustics), active-only (mist-netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias. For each species, the active-only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active-only models performed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias. Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another.

opencc-by-4.0Sep 2023View details →

Environmental drivers and distribution of cold-water corals in the global ocean - Habitat Suitability Models

Publication Abstract Species distribution models (SDMs) are useful tools for identifying the distribution of marine species in data limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation management. Cold-water corals are globally distributed habitat forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. Ensemble modeling methods were used to predict the global habitat suitability for ten widespread cold-water coral species, including six reef Scleractinian framework-forming species and four large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian species Madrepora oculata showed the widest niche breadth, whilst most other species demonstrated somewhat limited niche breadth. The shallowest study species, Oculina varicosa, had the most distinctive niche of the group. The model outputs from this study represent the highest resolution global predictions for these species to date and are valuable in aiding the management, conservation and continued research into cold-water coral species. Data description These datasets (compressed Zip archives) contain the habitat suitability model outputs generated for the publication Tong et al., (2023) doi: 10.3389/fmars.2023.1217851, please refer to the manuscript for methodological details. These files are provided in an ArcGIS compatible TIFF format that is readable by various GIS packages and can be imported to R.  AA.zip = Acanella arbuscula DP.zip = Desmophyllum pertusum (former and now unaccepted synonym Lophelia pertusa) ER.zip = Enallopsammia rostrata GD.zip = Goniocorella dumosa MO.zip =  Madrepora oculata OV.zip = Oculina varicosa PA.zip = Paragorgia arborea PP.zip = Paramuricea placomus PR.zip =  Primnoa resedaeformis SV.zip = Solenosmilia variabilis

publicMay 2022View details →

Habitat Suitability Analysis of Larval Pacific Lamprey Habitat in the Columbia River Estuary

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publicApr 2023View details →

Data from: One model to rule them all: Identifying priority bat habitats from multi‐species habitat suitability models

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publicSep 2023View details →

Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models

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