scholarly journals Predictor species: Improving assessments of rare species occurrence by modelling environmental co-responses

2019 ◽  
Author(s):  
Peter R. Thompson ◽  
William F. Fagan ◽  
Phillip P.A. Staniczenko
Keyword(s):  
Rare Species ◽  
Spatial Scales ◽  
Single Species ◽  
Conservation Strategy ◽  
Individual Species ◽  
Calibration Data ◽  
Distribution Models ◽  
New Approach ◽  
Effective Conservation ◽  
Better Than

ABSTRACTDesigning an effective conservation strategy requires understanding where rare species are located. Although species distribution models are primarily used to identify patterns at large spatial scales, their general methodology is relevant for predicting the occurrence of individual species at specific locations. Here we present a new approach that uses Bayesian networks to improve predictions by modelling environmental co-responses among species. For species from a European peat bog community, our approach consistently performs better than single-species models, and better than conventional multi-species models for rare species when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.

scholarly journals MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 1327-1334 ◽  
Author(s):  
W. Xu ◽  
B. Hays ◽  
R. Fayrer-Hosken ◽  
A. Presotto
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Area Under The Curve ◽  
Remotely Sensed Data ◽  
Distribution Models ◽  
African Savanna ◽  
Multi Scale ◽  
Land Cover Map ◽  
Better Than

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (<i>Loxodonta africana</i>) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.

scholarly journals MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 1327-1334
Author(s):  
W. Xu ◽  
B. Hays ◽  
R. Fayrer-Hosken ◽  
A. Presotto
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Area Under The Curve ◽  
Remotely Sensed Data ◽  
Distribution Models ◽  
African Savanna ◽  
Multi Scale ◽  
Land Cover Map ◽  
Better Than

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (<i>Loxodonta africana</i>) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.

scholarly journals The Contribution of the Spatial Hydrological Niche to Species Diversity in Rare Plant Communities of English Floodplain Meadows

2021 ◽  
Author(s):  
Gonzalo García-Baquero Moneo ◽  
David Gowing ◽  
Hilary Wallace
Keyword(s):  
Plant Communities ◽  
Beta Diversity ◽  
Spatial Scales ◽  
Conservation Strategy ◽  
Individual Species ◽  
Rare Plant ◽  
Relative Contribution ◽  
Local Plant ◽  
Below Ground ◽  
The Individual

Abstract The hydrological niche is one of the few below ground spatial environmental niches, which has been shown to structure English floodplain meadows and other European and African herbaceous ecosystems. However, both the relative contribution of hydrological heterogeneity to the structure of English floodplain meadows across spatial scales and the forms of the individual species’ responses to hydrological heterogeneity remain largely unknown. Here we use a survey database of 2440 evaluation units sampled in fifteen English floodplain meadows to dissect the spatial architecture of this metacommunity and describe the relationship between the abundance of individual species and hydrological heterogeneity. Of the tested species 65% responded to spatial hydrological heterogeneity, with both monotonic and hump-shaped responses. We found that between-site beta-diversity is much stronger than within-site beta-diversity, with between-site scale hydrological variation explaining twice as much variation in community structure as within-site scale. This leads to the conclusion that a conservation strategy of rare plant communities should include not only the preservation of the diversity of local hydrological regimes, but, specially, the inclusion in the conservation system of as many and environmentally varied local plant communities as possible.

A new approach based on graph matching and evolutionary approach for sport scheduling problem

2020 ◽  
pp. 1-16
Author(s):  
Meriem Khelifa ◽  
Dalila Boughaci ◽  
Esma Aïmeur
Keyword(s):  
Graph Matching ◽  
State Of The Art ◽  
Travel Cost ◽  
Round Robin ◽  
New Approach ◽  
Traveling Tournament Problem ◽  
Significant Interest ◽  
National League ◽  
Better Than

The Traveling Tournament Problem (TTP) is concerned with finding a double round-robin tournament schedule that minimizes the total distances traveled by the teams. It has attracted significant interest recently since a favorable TTP schedule can result in significant savings for the league. This paper proposes an original evolutionary algorithm for TTP. We first propose a quick and effective constructive algorithm to construct a Double Round Robin Tournament (DRRT) schedule with low travel cost. We then describe an enhanced genetic algorithm with a new crossover operator to improve the travel cost of the generated schedules. A new heuristic for ordering efficiently the scheduled rounds is also proposed. The latter leads to significant enhancement in the quality of the schedules. The overall method is evaluated on publicly available standard benchmarks and compared with other techniques for TTP and UTTP (Unconstrained Traveling Tournament Problem). The computational experiment shows that the proposed approach could build very good solutions comparable to other state-of-the-art approaches or better than the current best solutions on UTTP. Further, our method provides new valuable solutions to some unsolved UTTP instances and outperforms prior methods for all US National League (NL) instances.

scholarly journals A Fully Automatic Calibration System for Hydrometers in NIM

MAPAN ◽  
2021 ◽  
Author(s):  
Jintao Wang ◽  
Xiang Liu ◽  
Wencai Shi ◽  
Changhong Xu
Keyword(s):  
Full Range ◽  
Density Measurement ◽  
Fused Silica ◽  
Automatic Calibration ◽  
Liquid Density ◽  
Calibration Data ◽  
Calibration System ◽  
Solid Density ◽  
Fully Automatic ◽  
Better Than

AbstractHydrometers are widely used in industry for liquid density measurement. It is important to achieve rapid and high accuracy calibration for hydrometers. Based on the Archimedes principle, a fully automatic hydrometer calibration system in NIM was designed using Cuckow’s method. The liquid density of n-tridecane (C13H28)is calibrated with 441 g high-purity fused silica ring as the solid density standard. The buoyancy of hydrometer is measured by static weighing system with resolution 0.01 mg. The alignment between liquid surface and hydrometer scale was achieved by the lifting platform with the positioning accuracy of 10 μm. According to the weighing value of hydrometer in air and liquid, the density correction value at different scales is calculated. Hydrometer covering a full range (650–1500) kg/m3can be calibrated without changing the liquid. Taking the calibration data of PTB as reference, the experimental data show that the measurement uncertainty of this system is better than 0.3 division (k = 2).

scholarly journals New Cancer Stochastic Models Involving Both Hereditary and Nonhereditary Cancer Cases: A New Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-19
Author(s):  
Wai-Yuan Tan ◽  
Hong Zhou
Keyword(s):  
Cancer Incidence ◽  
Stochastic Models ◽  
Biological Information ◽  
Cancer Genes ◽  
New Approach ◽  
Human Cancers ◽  
Seer Data ◽  
Sampling Procedures ◽  
And Control ◽  
Better Than

To incorporate biologically observed epidemics into multistage models of carcinogenesis, in this paper we have developed new stochastic models for human cancers. We have further incorporated genetic segregation of cancer genes into these models to derive generalized mixture models for cancer incidence. Based on these models we have developed a generalized Bayesian approach to estimate the parameters and to predict cancer incidence via Gibbs sampling procedures. We have applied these models to fit and analyze the SEER data of human eye cancers from NCI/NIH. Our results indicate that the models not only provide a logical avenue to incorporate biological information but also fit the data much better than other models. These models would not only provide more insights into human cancers but also would provide useful guidance for its prevention and control and for prediction of future cancer cases.

scholarly journals A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products

2020 ◽  
Vol 24 (4) ◽  
pp. 2061-2081 ◽  
Author(s):  
Xudong Zhou ◽  
Jan Polcher ◽  
Tao Yang ◽  
Ching-Sheng Huang
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Variance Partitioning ◽  
Uncertainty Estimation ◽  
The Other ◽  
New Approach ◽  
Multiple Datasets ◽  
Uncertainty Estimates ◽  
Spatiotemporal Scales

Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gauge-based precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new three-dimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.

An Improved Wavelet Threshold De-Noising Data Processing Method Research in Deformation Monitoring

2011 ◽  
Vol 90-93 ◽  
pp. 2858-2863
Author(s):  
Wei Li ◽  
Xu Wang
Keyword(s):  
Data Processing ◽  
Deformation Monitoring ◽  
Processing Method ◽  
Threshold Function ◽  
Wavelet Coefficients ◽  
New Approach ◽  
Data Processing Method ◽  
Soft Threshold ◽  
Fixed Bias ◽  
Better Than

Due to the soft and hard threshold function exist shortcomings. This will reduce the performance in wavelet de-noising. in order to solve this problem，This article proposes Modulus square approach. the new approach avoids the discontinuity of the hard threshold function and also decreases the fixed bias between the estimated wavelet coefficients and the wavelet coefficients of the soft-threshold method．Simulation results show that SNR and MSE are better than simply using soft and hard threshold，having good de-noising effect in Deformation Monitoring.

scholarly journals Locally rare species influence grassland ecosystem multifunctionality

2016 ◽  
Vol 371 (1694) ◽  
pp. 20150269 ◽  
Author(s):  
Santiago Soliveres ◽  
Peter Manning ◽  
Daniel Prati ◽  
Martin M. Gossner ◽  
Fabian Alt ◽  
...  
Keyword(s):  
Land Use ◽  
Rare Species ◽  
Common Species ◽  
Ecosystem Functions ◽  
Individual Species ◽  
Conservation Priorities ◽  
Below Ground ◽  
Species Specific ◽  
The Relationship ◽  
Ecosystem Multifunctionality

Species diversity promotes the delivery of multiple ecosystem functions (multifunctionality). However, the relative functional importance of rare and common species in driving the biodiversity–multifunctionality relationship remains unknown. We studied the relationship between the diversity of rare and common species (according to their local abundances and across nine different trophic groups), and multifunctionality indices derived from 14 ecosystem functions on 150 grasslands across a land-use intensity (LUI) gradient. The diversity of above- and below-ground rare species had opposite effects, with rare above-ground species being associated with high levels of multifunctionality, probably because their effects on different functions did not trade off against each other. Conversely, common species were only related to average, not high, levels of multifunctionality, and their functional effects declined with LUI. Apart from the community-level effects of diversity, we found significant positive associations between the abundance of individual species and multifunctionality in 6% of the species tested. Species-specific functional effects were best predicted by their response to LUI: species that declined in abundance with land use intensification were those associated with higher levels of multifunctionality. Our results highlight the importance of rare species for ecosystem multifunctionality and help guiding future conservation priorities.

scholarly journals Global downscaling of remotely sensed soil moisture using neural networks

2018 ◽  
Vol 22 (10) ◽  
pp. 5341-5356 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatiotemporal Scales ◽  
Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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