scholarly journals Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

2019 ◽  
Vol 12 (1) ◽  
pp. 80 ◽  
Author(s):  
Andreas Dittrich ◽  
Stephanie Roilo ◽  
Ruth Sonnenschein ◽  
Cristiana Cerrato ◽  
Michael Ewald ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Monitoring Program ◽  
Predictor Variable ◽  
Species Distributions ◽  
Additive Models ◽  
Suitable Habitat ◽  
Operating Characteristics ◽  
Important Species ◽  
Rove Beetles

Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended.

scholarly journals Projecting climate-driven shifts in demersal fish habitat in Iceland's waters

2021 ◽  
Author(s):  
Julia G Mason ◽  
Pamela J Woods ◽  
Magnús Thorlacius ◽  
Kristinn Guðnason ◽  
Vincent S Saba ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Habitat Change ◽  
Additive Models ◽  
Suitable Habitat ◽  
Habitat Distribution ◽  
Important Species ◽  
Thermal Habitat

As climate change shifts marine species distribution and abundance worldwide, projecting local changes over decadal scales may be a valuable adaptive strategy for managers and industry. In Iceland, one of the top fish-producing nations in the world, long-term monitoring enables model simulations of groundfish species habitat distribution. We used generalized additive models to characterize suitable thermal habitat for 47 fish species in Iceland's waters. We then projected changes in thermal habitat by midcentury with an ensemble of five general circulation models from the Coupled Model Intercomparison Program 6 (CMIP6) and NOAA (CM2.6) and two scenarios (SSP 5-8.5 and SSP 2-4.5). We find a general northward shift in centroids of habitat distribution, with variable regional dynamics among species. Species thermal affinity was the most significant predictor of future habitat change, with warmer-water species more likely to see projected increases in suitable habitat. We present spatially explicit habitat change projections for commercially and culturally important species. These projections might serve as guideposts to inform long-term management decisions about regional and species-specific suitability for Iceland's fisheries, infrastructure investment, and risk evaluation under climate change.

scholarly journals Predicting the Habitat Suitability of Melaleuca cajuputi Based on the MaxEnt Species Distribution Model

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1449
Author(s):  
Nor Zafirah Ab Ab Lah ◽  
Zulkifli Yusop ◽  
Mazlan Hashim ◽  
Jamilah Mohd Mohd Salim ◽  
Shinya Numata
Keyword(s):  
Species Distribution ◽  
Habitat Suitability ◽  
Local Economy ◽  
Suitable Habitat ◽  
Climate Change Scenarios ◽  
Important Species ◽  
Habitat Area ◽  
Potential Habitat ◽  
Melaleuca Cajuputi ◽  
The Future

Gelam tree or Melaleuca cajuputi (M. cajuputi) is an important species for the local economy as well as coastal ecosystem protection in Terengganu, Malaysia. This study aimed at producing a current habitat suitability map and predicting future potential habitat distribution for M. cajuputi in Terengganu based on Species distribution modeling (SDM) using the Maximum Entropy principle. Our modeling results show that for the current potential distribution of M. cajuputi species, only 10.82% (1346.5 km2) of Terengganu area is suitable habitat, which 0.96% of the areas are under high, 2.44% moderate and 7.42% poor habitat suitability. The model prediction for future projection shows that the habitat suitability for M. cajuputi would decrease significantly in the year 2050 under RCP 4.5 where the largest contraction from suitable to unsuitable habitat area is about 442.1 km2 and under RCP 2.6 is the highest expansion from unsuitable to suitable habitat area (267.5 km2). From the future habitat suitability projection, we found that the habitat suitability in Marang would degrade significantly under all climate scenarios, while in Setiu the habitat suitability for M. cajuputi remains stable throughout the climate change scenarios. The modeling prediction shows a significant influence on the soil properties, temperature, and precipitation during monsoon months. These results could benefit conservationist and policymakers for decision making. The present model could also give a perception into potential habitat suitability of M. cajuputi in the future and to improve our understanding of the species’ response under the changing climate.

scholarly journals An updated understanding of Texas bumble bee (Hymenoptera: Apidae) species presence and potential distributions in Texas, USA

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3612 ◽  
Author(s):  
Jessica L. Beckham ◽  
Samuel Atkinson
Keyword(s):  
Citizen Science ◽  
Species Distributions ◽  
Field Studies ◽  
The United States ◽  
Bumble Bee ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Species List ◽  
Central Texas ◽  
Declining Species

Texas is the second largest state in the United States of America, and the largest state in the contiguous USA at nearly 700,000 sq. km. Several Texas bumble bee species have shown evidence of declines in portions of their continental ranges, and conservation initiatives targeting these species will be most effective if species distributions are well established. To date, statewide bumble bee distributions for Texas have been inferred primarily from specimen records housed in natural history collections. To improve upon these maps, and help inform conservation decisions, this research aimed to (1) update existing Texas bumble bee presence databases to include recent (2007–2016) data from citizen science repositories and targeted field studies, (2) model statewide species distributions of the most common bumble bee species in Texas using MaxEnt, and (3) identify conservation target areas for the state that are most likely to contain habitat suitable for multiple declining species. The resulting Texas bumble bee database is comprised of 3,580 records, to include previously compiled museum records dating from 1897, recent field survey data, and vetted records from citizen science repositories. These data yielded an updated state species list that includes 11 species, as well as species distribution models (SDMs) for the most common Texas bumble bee species, including two that have shown evidence of range-wide declines: B. fraternus (Smith, 1854) and B. pensylvanicus (DeGeer, 1773). Based on analyses of these models, we have identified conservation priority areas within the Texas Cross Timbers, Texas Blackland Prairies, and East Central Texas Plains ecoregions where suitable habitat for both B. fraternus and B. pensylvanicus are highly likely to co-occur.

scholarly journals Remote Sensing Is Changing Our View of the Coast: Insights from 40 Years of Monitoring at Narrabeen-Collaroy, Australia

2018 ◽  
Vol 10 (11) ◽  
pp. 1744 ◽  
Author(s):  
Kristen Splinter ◽  
Mitchell Harley ◽  
Ian Turner
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Temporal Resolution ◽  
Monitoring Program ◽  
Google Earth ◽  
Airborne Lidar ◽  
Data Collection Program ◽  
Insight Into ◽  
Collection Program

Narrabeen-Collaroy Beach, located on the Northern Beaches of Sydney along the Pacific coast of southeast Australia, is one of the longest continuously monitored beaches in the world. This paper provides an overview of the evolution and international scientific impact of this long-term beach monitoring program, from its humble beginnings over 40 years ago using the rod and tape measure Emery field survey method; to today, where the application of remote sensing data collection including drones, satellites and crowd-sourced smartphone images, are now core aspects of this continuing and much expanded monitoring effort. Commenced in 1976, surveying at this beach for the first 30 years focused on in-situ methods, whereby the growing database of monthly beach profile surveys informed the coastal science community about fundamental processes such as beach state evolution and the role of cross-shore and alongshore sediment transport in embayment morphodynamics. In the mid-2000s, continuous (hourly) video-based monitoring was the first application of routine remote sensing at the site, providing much greater spatial and temporal resolution over the traditional monthly surveys. This implementation of video as the first of a now rapidly expanding range of remote sensing tools and techniques also facilitated much wider access by the international research community to the continuing data collection program at Narrabeen-Collaroy. In the past decade the video-based data streams have formed the basis of deeper understanding into storm to multi-year response of the shoreline to changing wave conditions and also contributed to progress in the understanding of estuary entrance dynamics. More recently, ‘opportunistic’ remote sensing platforms such as surf cameras and smartphones have also been used for image-based shoreline data collection. Commencing in 2011, a significant new focus for the Narrabeen-Collaroy monitoring program shifted to include airborne lidar (and later Unmanned Aerial Vehicles (UAVs)), in an enhanced effort to quantify the morphological impacts of individual storm events, understand key drivers of erosion, and the placing of these observations within their broader regional context. A fixed continuous scanning lidar installed in 2014 again improved the spatial and temporal resolution of the remote-sensed data collection, providing new insight into swash dynamics and the often-overlooked processes of post-storm beach recovery. The use of satellite data that is now readily available to all coastal researchers via Google Earth Engine continues to expand the routine data collection program and provide key insight into multi-decadal shoreline variability. As new and expanding remote sensing technologies continue to emerge, a key lesson from the long-term monitoring at Narrabeen-Collaroy is the importance of a regular re-evaluation of what data is most needed to progress the science.

scholarly journals Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
George Colares Silva Filho ◽  
Juliana Martins dos Santos ◽  
Paulo Cesar Mendes Villis ◽  
Ingrid Santos Gonçalves ◽  
Isael Coelho Correia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Physicochemical Properties ◽  
Monitoring Program ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Solids Content

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Biotropica ◽  
2018 ◽  
Vol 50 (5) ◽  
pp. 758-767 ◽  
Author(s):  
Pablo Pérez Chaves ◽  
Kalle Ruokolainen ◽  
Hanna Tuomisto
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Tree Species ◽  
Model Tree ◽  
Lowland Amazonia

Abstract W MP37: Novel Prognostic Scores for Early Prediction of Outcome Following Aneurysmal Subarachnoid Hemorrhage

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Blessing Jaja ◽  
Hester Lingsma ◽  
Ewout Steyerberg ◽  
R. Loch Macdonald ◽  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cross Validation ◽  
Prediction Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Model Performance ◽  
Predictor Variable ◽  
Predictive Ability ◽  
Prognostic Scores ◽  
Operating Characteristics ◽  
Fisher Grade

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a cerebrovascular emergency. Currently, clinicians have limited tools to estimate outcomes early after hospitalization. We aimed to develop novel prognostic scores using large cohorts of patients reflecting experience from different settings. Methods: Logistic regression analysis was used to develop prediction models for mortality and unfavorable outcomes according to 3-month Glasgow outcome score after SAH based on readily obtained parameters at hospital admission. The development cohort was derived from 10 prospective studies involving 10936 patients in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository. Model performance was assessed by bootstrap internal validation and by cross validation by omission of each of the 10 studies, using R2 statistic, Area under the receiver operating characteristics curve (AUC), and calibration plots. Prognostic scores were developed from the regression coefficients. Results: Predictor variable with the strongest prognostic strength was neurologic status (partial R2 = 12.03%), followed by age (1.91%), treatment modality (1.25%), Fisher grade of CT clot burden (0.65%), history of hypertension (0.37%), aneurysm size (0.12%) and aneurysm location (0.06%). These predictors were combined to develop 3 sets of hierarchical scores based on the coefficients of the regression models. The AUC at bootstrap validation was 0.79-0.80, and at cross validation was 0.64-0.85. Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities of the outcomes. Conclusions: The novel prognostic scores have good predictive ability and potential for broad application as they have been developed from prospective cohorts reflecting experience from different centers globally.

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