Integrating landscape resistance and multi-scale predictor of habitat selection for amphibian distribution modelling at large scale

Abstract Context – Species distribution modelling is a common tool in conservation biology but two main criticisms remain: (1) the use of simplistic variables that do not account for species movements and/or connectivity and (2) poor consideration of multi-scale processes driving species distributions. Objectives – We aimed to determine if including multi-scale and fine-scale movement processes in SDM predictors would improve accuracy of SDM for low-mobility amphibian species over species-level analysis.Methods – We tested and compared different SDMs for nine amphibian species with four different sets of predictors: (1) simple distance-based predictors; (2) single-scale compositional predictors; (3) multi-scale compositional predictors with a priori selection of scale based on knowledge of species mobility and scale-of-effect (4) multi-scale compositional predictors calculated using a friction-based functional grain to account for resource accessibility with landscape resistance to movement.Results - Using friction-based functional grain predictors produced slight to moderate improvements of SDM performance at large scale. The multi-scale approach, with a priori scale selection led to ambiguous results depending on the species studied, in particular for generalist species.Conclusion - We underline the potential of using a friction-based functional grain to improve SDM predictions for species-level analysis.

Download Full-text

Public Awareness: What Climate Change Scientists Should Consider

Sustainability ◽

10.3390/su12208369 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8369

Author(s):

Mohammad Rahimi

Keyword(s):

Climate Change ◽

Social Media ◽

Large Scale ◽

Public Awareness ◽

Connected Network ◽

Multi Scale ◽

Valuable Addition ◽

Scientific Approach ◽

Design Solutions ◽

Mitigate Climate Change

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).

Download Full-text

Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

Remote Sensing ◽

10.3390/rs13112074 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2074

Author(s):

Ryan R. Reisinger ◽

Ari S. Friedlaender ◽

Alexandre N. Zerbini ◽

Daniel M. Palacios ◽

Virginia Andrews-Goff ◽

...

Keyword(s):

Habitat Selection ◽

Predictive Models ◽

Regional Variation ◽

Large Scale ◽

Predictive Performance ◽

Humpback Whale ◽

Machine Learning Algorithms ◽

Humpback Whales ◽

Environmental Covariates ◽

Animal Habitat

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.

Download Full-text

The Winter Habitat Selection of Red Deer (Cervus elaphus) Based on a Multi-Scale Model

Animals ◽

10.3390/ani10122454 ◽

2020 ◽

Vol 10 (12) ◽

pp. 2454

Author(s):

Yue Sun ◽

Yanze Yu ◽

Jinhao Guo ◽

Minghai Zhang

Keyword(s):

Habitat Selection ◽

Environmental Factors ◽

Cervus Elaphus ◽

Red Deer ◽

Scale Model ◽

Winter Habitat ◽

Multi Scale ◽

Optimal Scale ◽

Scale Models ◽

Selection Of

Single-scale frameworks are often used to analyze the habitat selections of species. Research on habitat selection can be significantly improved using multi-scale models that enable greater in-depth analyses of the scale dependence between species and specific environmental factors. In this study, the winter habitat selection of red deer in the Gogostaihanwula Nature Reserve, Inner Mongolia, was studied using a multi-scale model. Each selected covariate was included in multi-scale models at their “characteristic scale”, and we used an all subsets approach and model selection framework to assess habitat selection. The results showed that: (1) Univariate logistic regression analysis showed that the response scale of red deer to environmental factors was different among different covariate. The optimal scale of the single covariate was 800–3200 m, slope (SLP), altitude (ELE), and ratio of deciduous broad-leaved forests were 800 m in large scale, except that the farmland ratio was 200 m in fine scale. The optimal scale of road density and grassland ratio is both 1600 m, and the optimal scale of net forest production capacity is 3200 m; (2) distance to forest edges, distance to cement roads, distance to villages, altitude, distance to all road, and slope of the region were the most important factors affecting winter habitat selection. The outcomes of this study indicate that future studies on the effectiveness of habitat selections will benefit from multi-scale models. In addition to increasing interpretive and predictive capabilities, multi-scale habitat selection models enhance our understanding of how species respond to their environments and contribute to the formulation of effective conservation and management strategies for ungulata.

Download Full-text

Multi-Scale Ionospheric Anomalies Monitoring and Spatio-Temporal Analysis during Intense Storm

Atmosphere ◽

10.3390/atmos12020215 ◽

2021 ◽

Vol 12 (2) ◽

pp. 215

Author(s):

Na Cheng ◽

Shuli Song ◽

Wei Li

Keyword(s):

Magnetic Storm ◽

Large Scale ◽

Temporal Dynamics ◽

Ionospheric Disturbance ◽

Ionospheric Scintillation ◽

Small Scale ◽

Electron Content ◽

Total Electron ◽

Multi Scale ◽

Intense Storm

The ionosphere is a significant component of the geospace environment. Storm-induced ionospheric anomalies severely affect the performance of Global Navigation Satellite System (GNSS) Positioning, Navigation, and Timing (PNT) and human space activities, e.g., the Earth observation, deep space exploration, and space weather monitoring and prediction. In this study, we present and discuss the multi-scale ionospheric anomalies monitoring over China using the GNSS observations from the Crustal Movement Observation Network of China (CMONOC) during the 2015 St. Patrick’s Day storm. Total Electron Content (TEC), Ionospheric Electron Density (IED), and the ionospheric disturbance index are used to monitor the storm-induced ionospheric anomalies. This study finally reveals the occurrence of the large-scale ionospheric storms and small-scale ionospheric scintillation during the storm. The results show that this magnetic storm was accompanied by a positive phase and a negative phase ionospheric storm. At the beginning of the main phase of the magnetic storm, both TEC and IED were significantly enhanced. There was long-duration depletion in the topside ionospheric TEC during the recovery phase of the storm. This study also reveals the response and variations in regional ionosphere scintillation. The Rate of the TEC Index (ROTI) was exploited to investigate the ionospheric scintillation and compared with the temporal dynamics of vertical TEC. The analysis of the ROTI proved these storm-induced TEC depletions, which suppressed the occurrence of the ionospheric scintillation. To improve the spatial resolution for ionospheric anomalies monitoring, the regional Three-Dimensional (3D) ionospheric model is reconstructed by the Computerized Ionospheric Tomography (CIT) technique. The spatial-temporal dynamics of ionospheric anomalies during the severe geomagnetic storm was reflected in detail. The IED varied with latitude and altitude dramatically; the maximum IED decreased, and the area where IEDs were maximum moved southward.

Download Full-text

Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

Movement Ecology ◽

10.1186/s40462-021-00260-y ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Moritz Mercker ◽

Philipp Schwemmer ◽

Verena Peschko ◽

Leonie Enners ◽

Stefan Garthe

Keyword(s):

Habitat Selection ◽

Large Scale ◽

Error Rates ◽

Process Models ◽

Statistical Hypothesis ◽

Type I ◽

Tracking Data ◽

Selection Models ◽

Animal Tracking ◽

Step Selection

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

Download Full-text

Tree Swallow selection for wetlands in agricultural landscapes predicted by central-place foraging theory

The Condor ◽

10.1093/condor/duaa039 ◽

2020 ◽

Author(s):

Andrew S Elgin ◽

Robert G Clark ◽

Christy A Morrissey

Keyword(s):

Habitat Selection ◽

Central Place ◽

Foraging Theory ◽

Agricultural Landscapes ◽

Tree Swallow ◽

Field Margins ◽

Central Place Foraging ◽

Prairie Pothole ◽

Prairie Wetlands ◽

Selection For

Abstract Millions of wetland basins, embedded in croplands and grasslands, are biodiversity hotspots in North America’s Prairie Pothole Region, but prairie wetlands continue to be degraded and drained, primarily for agricultural activities. Aerial insectivorous swallows are known to forage over water, but it is unclear whether swallows exhibit greater selection for wetlands relative to other habitats in croplands and grasslands. Central-place foraging theory suggests that habitat selectivity should increase with traveling distance from a central place, such that foragers compensate for traveling costs by selecting more profitable foraging habitat. Using global positioning system (GPS) tags, we evaluated habitat selection by female Tree Swallows (Tachycineta bicolor) at 4 sites containing wetlands and where terrestrial land cover was dominated by grasslands (grass, herbaceous cover) and/or cultivated cropland. We also used sweep-net transects to assess the abundance and biomass of flying insects in different habitats available to swallows (wetland pond margins, grassy field margins, and representative uplands). As expected for a central-place forager, GPS-tagged swallows selected more for wetland ponds (disproportionate to availability), and appeared to increasingly select for wetlands with increasing distance from their nests. On cropland-dominated sites, insect abundance and biomass tended to be higher in pond margins or grassy field margins compared to cropped uplands, while abundance and biomass were more uniform among sampled habitats at sites dominated by grass and herbaceous cover. Swallow habitat selection was not clearly explained by the distribution of sampled insects among habitats; however, traditional terrestrial sampling methods may not adequately reflect prey distribution and availability to aerially foraging swallows. Overall, our results underscore the importance of protecting and enhancing prairie wetlands and other non-crop habitats in agricultural landscapes, given their disproportionate use and capacity to support breeding swallow and insect populations.

Download Full-text

Multi-scale modelling of a large scale shell-and-tube latent heat storage system for direct steam generation power plants

10.1063/5.0028816 ◽

2020 ◽

Author(s):

Clément Beust ◽

Erwin Franquet ◽

Jean-Pierre Bédécarrats ◽

Pierre Garcia ◽

Jérôme Pouvreau ◽

...

Keyword(s):

Power Plants ◽

Large Scale ◽

Storage System ◽

Steam Generation ◽

Latent Heat Storage ◽

Multi Scale ◽

Direct Steam ◽

Scale Modelling ◽

Shell And Tube ◽

Generation Power

Download Full-text

Hybrid Graph Neural Networks for Crowd Counting

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6839 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11693-11700 ◽

Cited By ~ 2

Author(s):

Ao Luo ◽

Fan Yang ◽

Xin Li ◽

Dong Nie ◽

Zhicheng Jiao ◽

...

Keyword(s):

Network Architecture ◽

Message Passing ◽

Large Scale ◽

State Of The Art ◽

Density Variation ◽

Feature Maps ◽

Crowd Counting ◽

Multi Scale ◽

Crowd Density ◽

Graph Neural Networks

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.

Download Full-text