scholarly journals Earth and field observations underpin metapopulation dynamics in complex landscapes: Near-term study on carabids

2020 ◽  
Vol 117 (23) ◽  
pp. 12877-12884
Author(s):  
Jonathan Giezendanner ◽  
Damiano Pasetto ◽  
Javier Perez-Saez ◽  
Cristiana Cerrato ◽  
Ramona Viterbi ◽  
...  
Keyword(s):  
Fitness Function ◽  
Monitoring Program ◽  
Spatially Explicit ◽  
Metapopulation Dynamics ◽  
Observation Data ◽  
Field Observations ◽  
Target Species ◽  
Distribution Models ◽  
Earth Observations ◽  
Near Term

Understanding risks to biodiversity requires predictions of the spatial distribution of species adapting to changing ecosystems and, to that end, Earth observations integrating field surveys prove essential as they provide key numbers for assessing landscape-wide biodiversity scenarios. Here, we develop, and apply to a relevant case study, a method suited to merge Earth/field observations with spatially explicit stochastic metapopulation models to study the near-term ecological dynamics of target species in complex terrains. Our framework incorporates the use of species distribution models for a reasoned estimation of the initial presence of the target species and accounts for imperfect and incomplete detection of the species presence in the study area. It also uses a metapopulation fitness function derived from Earth observation data subsuming the ecological niche of the target species. This framework is applied to contrast occupancy of two species of carabids (Pterostichus flavofemoratus,Carabus depressus) observed in the context of a large ecological monitoring program carried out within the Gran Paradiso National Park (GPNP, Italy). Results suggest that the proposed framework may indeed exploit the hallmarks of spatially explicit ecological approaches and of remote Earth observations. The model reproduces well the observed in situ data. Moreover, it projects in the near term the two species’ presence both in space and in time, highlighting the features of the metapopulation dynamics of colonization and extinction, and their expected trends within verifiable timeframes.

An Optimization Model for the Identification of Temperature in Intelligent Building

2011 ◽  
Vol 4 (2) ◽  
pp. 61-69 ◽  
Author(s):  
ZhenYa Zhang ◽  
HongMei Cheng ◽  
ShuGuang Zhang
Keyword(s):  
Neural Network ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Fitness Function ◽  
Temperature Data ◽  
Temperature Fields ◽  
Observation Data ◽  
Feed Forward Neural Network ◽  
Intelligent Building ◽  
Feed Forward

Methods for the reconstruction of temperature fields in an intelligent building with temperature data of discrete observation positions is a current topic of research. To reconstruct temperature field with observation data, it is necessary to model the identification of temperature in each observation position. In this paper, models for temperature identification in an intelligent building are formalized as optimization problems based on observation temperature data sequence. To solve the optimization problem, a feed forward neural network is used to formalize the identification structure, and connection matrixes of the neural network are the identification parameters. With the object function for the given optimization problem as the fitness function, the training of the feed forward neural network is driven by a genetic algorithm. The experiment for the precision and stability of the proposed method is designed with real temperature data from an intelligent building.

scholarly journals Estimating efficiency of survey and commercial trawl gears from comparisons of catch-ratios

2017 ◽  
Vol 74 (5) ◽  
pp. 1448-1457 ◽  
Author(s):  
Nicola D. Walker ◽  
David L. Maxwell ◽  
Will J. F. Le Quesne ◽  
Simon Jennings
Keyword(s):  
Monitoring Program ◽  
Data File ◽  
Species Group ◽  
Additive Models ◽  
Reference Points ◽  
Target Species ◽  
Commercial Catch ◽  
The North ◽  
Gear Efficiency ◽  
Species Groups

Abstract Assumptions about gear efficiency and catchability influence estimates of abundance, mortality, reference points and catch potential. Despite the need to better quantify fishing effects on some target species and on many non-target species taken as bycatch, there are few gear efficiency estimates for some of the most widely deployed towed fishing gears in the northeast Atlantic. Here, we develop a method that applies generalised additive models to catch-at-length data from trawl surveys and a commercial catch and discard monitoring program in the North Sea to estimate catch-ratios. We then rescale these catch-ratios and fit relationships to estimate gear efficiency. When catches of individuals by species were too low to enable species-specific estimates, gear efficiency was estimated for species-groups. Gear efficiency (and associated uncertainty) at length was ultimately estimated for 75 species, seven species-groups and for up to six types of trawl gear per species or species-group. Results are illustrated for dab (Limanda limanda), grey gurnard (Eutrigula gurnardus) and thornback ray (Raja clavata), two common non-target species and a depleted elasmobranch. All estimates of gear efficiency and uncertainty, by length, species, species-group and gear, are made available in a supplementary data file.

Improving the analysis of the soil-plant-atmosphere system thought earth observations in large monocrop cereal sequences

2020 ◽  
Author(s):  
David Rivas-Tabares ◽  
Juan J. Martín-Sotoca ◽  
Antonio Saa-Requejo ◽  
Ana María Tarquis
Keyword(s):  
Time Series ◽  
Soil Properties ◽  
Vegetation Index ◽  
Crop Yields ◽  
Observation Data ◽  
Precipitation Regime ◽  
Agricultural Insurance ◽  
Residual Time ◽  
Earth Observations ◽  
Atmosphere System

<p>Crop yields of rainfed cereal are highly dependent of the soil-plant-atmosphere system, especially referred to the weather conditions and soil properties. The study of this interaction is feasible through the earth observations of historical data. Remote sensing data and agricultural survey work together identifying and analyzing plots with monocrop cereal sequences. In this research, we investigate the relation of the Normalized Difference Vegetation Index (NDVI) residual time series behavior relative to soil classes from Self-Organizing Maps (SOM) and the precipitation residual time series.</p><p>The midlands of Eresma-Adaja watershed (Dueros’ River basin, Spain) is historically depicted to rainfed cereal agriculture, some evidence of monocropping sequences are worrisome the water availability in the area. Within this area, two contrasting soil properties sites were selected to assess plots with at least 20 years of rainfed monocropping sequences but under similar weather regime. This allows analyzing the effect and relationships of this practice by soil type in time. For this, we treat the NDVI and precipitation time residual series as signals. The use of the Generalized Structure Function applied to these residual time series and the Hurst exponent, serve to confirm the soil properties differences from SOM and to reinforce the scaling properties of soil-climate interaction in semiarid regions for cereals in monocrop. As a result, the NDVI and precipitation series present an antipersistence behavior supporting that precipitation regime is influencing as the same manner the NDVI residual time series among complimentary factors.</p><p><strong>ACKNOWLEDGEMENTS</strong></p><p>Finding for this work was partially provided by Boosting agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020. The authors also acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish <em>Ministerio de Ciencia Innovación y Universidades</em> of Spain. The data provided by ITACyL and AEMET is greatly appreciated.</p><p> </p>

DO ABSENCE DATA MATTER WHEN MODELLING FISH HABITAT PREFERENCE USING A GENETIC TAKAGI-SUGENO FUZZY MODEL?

2012 ◽  
Vol 20 (supp02) ◽  
pp. 233-245 ◽  
Author(s):  
SHINJI FUKUDA ◽  
BERNARD DE BAETS
Keyword(s):  
Habitat Preference ◽  
Fish Habitat ◽  
Fuzzy Model ◽  
Data Sets ◽  
Observation Data ◽  
Target Species ◽  
Entire Data ◽  
Presence Data ◽  
Management Plans ◽  
Takagi Sugeno

Information on species distributions is of key importance when designing management plans for a target species or ecosystem. This paper illustrates the effects of absence data on fish habitat prediction and habitat preference evaluation using a genetic Takagi-Sugeno fuzzy model. Three independent data sets were prepared from a series of fish habitat surveys conducted in an agricultural canal in Japan. To quantify the effects of absence data, two kinds of abundance data (entire data and presence data) were used for developing a fuzzy habitat preference model (FHPM). As a result, habitat preference curves (HPCs) obtained from presence data resulted in similar HPCs between the three data sets, while those obtained from entire data slightly differed according to the data sets. The higher generalization ability of the FHPMs obtained from presence data supports the usefulness of presence data for better extracting the habitat preference information of a target species from field observation data.

scholarly journals Adaptive-scale wide-field reconstruction for radio synthesis imaging

2020 ◽  
Vol 640 ◽  
pp. A80
Author(s):  
L. Zhang ◽  
L. G. Mi ◽  
M. Zhang ◽  
X. Liu ◽  
C. L. He
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Wide Field ◽  
Observation Data ◽  
Field Observations ◽  
High Dynamic Range Imaging ◽  
Range Imaging ◽  
Low Frequencies ◽  
Very Large Array ◽  
High Dynamic

Sky curvature and non-coplanar effects, caused by low frequencies, long baselines, or small apertures in wide field-of-view instruments such as the Square Kilometre Array (SKA), significantly limit the imaging performance of an interferometric array. High dynamic range imaging essentially requires both an excellent sky model and the correction of imaging factors such as non-coplanar effects. New CLEAN deconvolution with adaptive-scale modeling already has the ability to construct significantly better narrow-band sky models. However, the application of wide-field observations based on modern arrays has not yet been jointly explored. We present a new wide-field imager that can model the sky on an adaptive-scale basis, and the sky curvature and the effects of non-coplanar observations with the w-projection method. The degradation caused by the dirty beam due to incomplete spatial frequency sampling is eliminated during sky model construction by our new method, while the w-projection mainly removes distortion of sources far from the image phase center. Applying our imager to simulated SKA data and the real observation data of the Karl G. Jansky Very Large Array (an SKA pathfinder) suggested that our imager can handle the effects of wide-field observations well and can reconstruct more accurate images. This provides a route for high dynamic range imaging of SKA wide-field observations, which is an important step forward in the development of the SKA imaging pipeline.

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