scholarly journals Mammalia, Chiroptera, Thyropteridae, Thyroptera tricolor Spix, 1823: distribution extension in Ecuador

Check List ◽  
2010 ◽  
Vol 6 (2) ◽  
pp. 227
Author(s):  
Carlos E. Boada ◽  
Diego G. Tirira ◽  
M. Alejandra Camacho ◽  
Santiago F. Burneo
Keyword(s):  
Maximum Entropy ◽  
Geographical Distribution ◽  
Coastal Region ◽  
Predictive Distribution ◽  
Distribution Model ◽  
Breeding Female ◽  
The Andes ◽  
Thyroptera Tricolor

In Ecuador, Thyroptera tricolor is distributed on the northern coastal region, in Amazonia, and in the foothills of the Andes between 50 to 1,800 m of altitude. We reported a capture of a non-breeding female at El Descanso, Los Ríos Province, in the central coastal region of Ecuador. With this record, we have extended the geographical distribution of T. tricolor in Ecuador 55 km further south. Using the available data for Ecuador, a predictive distribution model was generated using a Maximum Entropy approach.

scholarly journals A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Billy Joel M. Almarinez ◽  
Mary Jane A. Fadri ◽  
Richard Lasina ◽  
Mary Angelique A. Tavera ◽  
Thaddeus M. Carvajal ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Habitat Suitability ◽  
Species Distribution Model ◽  
Biological Control Agent ◽  
The Philippines ◽  
Distribution Model ◽  
Field Surveys ◽  
Probability Of Occurrence ◽  
Precipitation Seasonality ◽  
Bioclimatic Variables

Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.

scholarly journals An Integrative Dynamic Model of Colombian Population Distribution, Based on the Maximum Entropy Principle and Matter, Energy, and Information Flow

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1172
Author(s):  
César Cardona-Almeida ◽  
Nelson Obregón ◽  
Fausto A. Canales
Keyword(s):  
Maximum Entropy ◽  
Information Exchange ◽  
New Technologies ◽  
Population Distribution ◽  
Knowledge Society ◽  
Maximum Entropy Principle ◽  
Distribution Model ◽  
Human Society ◽  
Natural Systems ◽  
Entropy Principle

Human society has increased its capacity to exploit natural resources thanks to new technologies, which are one of the results of information exchange in the knowledge society. Many approaches to understanding the interactions between human society and natural systems have been developed in the last decades, and some have included considerations about information. However, none of them has considered information as an active variable or flowing entity in the human–natural/social-ecological system, or, moreover, even as a driving force of their interactions. This paper explores these interactions in socio-ecological systems by briefly introducing a conceptual frame focused on the exchange of information, matter, and energy. The human population is presented as a convergence variable of these three physical entities, and a population distribution model for Colombia is developed based on the maximum entropy principle to integrate the balances of related variables as macro-state restrictions. The selected variables were electrical consumption, water demand, and higher education rates (energy, matter, and information). The final model includes statistical moments for previous population distributions. It is shown how population distribution can be predicted yearly by combining these variables, allowing future dynamics exploration. The implications of this model can contribute to bridging information sciences and sustainability studies.

scholarly journals Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 859 ◽  
Author(s):  
Yuling Chen ◽  
Baoguo Wu ◽  
Zhiqiang Min
Keyword(s):  
Weibull Distribution ◽  
Maximum Entropy ◽  
Measurement Data ◽  
Similarity Index ◽  
Distribution Model ◽  
Diameter Distribution ◽  
Weibull Function ◽  
Prediction Ability ◽  
Actual Distribution ◽  
Modeling And Prediction

Research Highlights: Improving the prediction accuracy represents a popular forest simulation modeling issue, and exploring the optimal maximum entropy (MaxEnt) distribution is a new effective method for improving the diameter distribution model simulation precision to overcome the disadvantages of Weibull. Background and Objectives: The MaxEnt distribution is the closest to the actual distribution under the constraints, which are the main probability density distributions. However, relatively few studies have addressed the optimization of stand diameter distribution based on MaxEnt distribution. The objective of this study was to introduce application of the MaxEnt distribution on modeling and prediction of stand diameter distribution. Materials and Methods: The long-term repeated measurement data sets consisted of 260 diameter frequency distributions from China fir (Cunninghamia lanceolate (Lamb.) Hook) plantations in the southern China Guizhou. The Weibull distribution and the MaxEnt distribution were applied to the fitting of stand diameter distribution, and the modeling and prediction characteristics of Weibull distribution and MaxEnt distribution to stand diameter distribution were compared. Results: Three main conclusions were obtained: (1) MaxEnt distribution presented a more accurate simulation than three-parametric Weibull function; (2) the Chi-square test showed diameter distributions of unknown stands can be well estimated by applying MaxEnt distribution based on the plot similarity index method (PSIM) and Weibull distribution based on the parameter prediction method (PPM); (3) the MaxEnt model can deal with the complex nonlinear relationship and show strong prediction ability when predicting the stand distribution structure. Conclusions: With the increase of sample size, the PSIM has great application prospects in the dynamic prediction system of stand diameter distribution.

scholarly journals Estimating flood extent during Hurricane Harvey using maximum entropy to build a hazard distribution model

2019 ◽  
Vol 12 (S1) ◽  
Author(s):  
William Mobley ◽  
Antonia Sebastian ◽  
Wesley Highfield ◽  
Samuel D. Brody
Keyword(s):  
Maximum Entropy ◽  
Distribution Model ◽  
Hazard Distribution

scholarly journals Geographical Distribution of Variance of Intraseasonal Variations in Western Indochina as Revealed from Radar Reflectivity Data

2008 ◽  
Vol 21 (19) ◽  
pp. 5154-5161 ◽  
Author(s):  
Satoru Yokoi ◽  
Takehiko Satomura
Keyword(s):  
Geographical Distribution ◽  
Coastal Region ◽  
Radar Reflectivity ◽  
Windward Side ◽  
Synoptic Scale ◽  
Wind Anomaly ◽  
Orographic Rainfall ◽  
Intraseasonal Variations ◽  
Reflectivity Data ◽  
Reflectivity Factor

Abstract This study reveals remarkable differences in the geographical distribution of variance between two types of intraseasonal variations in daily-mean radar reflectivity data in the western part of the Indochina Peninsula. In this region, the Downa Range lies parallel to the coast and separates the inland region from the coastal region. The 30–60-day variation of reflectivity factor dominates most of the coastal region, while its variance in the inland region is less than that of background red noise with the same frequency band. Horizontal gradients in the variance are largest over the range, implying that the mountain range plays a significant role in the geographical contrast. Correlation analysis with reanalysis data shows that the variation only in the coastal region is associated with a synoptic-scale zonal wind anomaly with the same time scale, suggesting the importance of an orographic rainfall process that brings a large amount of precipitation only to the windward side of the Downa Range. In contrast, while the 10–20-day variation of reflectivity factor has larger variance in the inland region than in the coastal region, the variation in both of the regions is correlated with synoptic-scale cyclonic circulation anomaly. A possible reason for the differences between the two types is also discussed in terms of the relationship between synoptic-scale wind anomaly field and the orientation of the Downa Range.

scholarly journals Predicting potential impacts of climate change on the geographical distribution of mountainous selaginellas in Java, Indonesia

2020 ◽  
Vol 21 (10) ◽  
Author(s):  
AHMAD DWI SETYAWAN ◽  
JATNA SUPRIATNA ◽  
NISYAWATI NISYAWATI ◽  
ILYAS NURSAMSI ◽  
SUTARNO SUTARNO ◽  
...  
Keyword(s):  
Climate Change ◽  
Geographical Distribution ◽  
Flowering Plant ◽  
Distribution Model ◽  
Climate Change Scenarios ◽  
Mountainous Areas ◽  
Bioclimatic Variables ◽  
Suitable Habitats ◽  
High Level ◽  
Impacts Of Climate Change

Abstract. Setyawan AD, Supriatna J, Nisyawati, Nursamsi I, Sutarno, Sugiyarto, Sunarto, Pradan P, Budiharta S, Pitoyo A, Suhardono S, Setyono P, Indrawan M. 2020. Predicting potential impacts of climate change on the geographical distribution of mountainous selaginellas in Java, Indonesia. Biodiversitas 21: 4866-4877. Selaginella is a genus of non-flowering plant that requires water as a medium for fertilization, as such, it prefers mountainous areas with high level of humidity. Such unique ecosystem of Selaginella is available in some parts of Java Island, Indonesia, especially in highland areas with altitude of more than 1,000 meters above sea level. However, most mountainous areas in Java are likely to be affected by climate change due to global warming, threatening the habitat and sustainability of Selaginella. This study aimed to investigate the impacts of climate change on the geographical distribution of Selaginella opaca Warb. and Selaginella remotifolia Spring. In doing so, we predicted the suitable habitats of both species using Species Distribution Model (SDM) tool of MaxEnt under present climate conditions and future conditions under four climate change scenarios. Species occurrence data were obtained from fieldworks conducted in 2007-2014 across Java Island (283 points: 144 and 139 points for S. opaca and S. remotifolia, respectively) and combined with secondary data from Global Biodiversity Information Facility (GBIF) (52 points: 35 and 17 points for S. opaca and S. remotifolia, respectively), and this dataset was used to model present geographical distribution using environmental and bioclimatic variables. Then, future distribution was predicted under four climate change scenarios: i.e. RCP (Representative Carbon Pathways) 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 in three different time periods (2030, 2050, and 2080). The results of the models showed that the extent of suitable habitats of S. opaca and S. remotifolia will be reduced between 1.8-11.4% due to changes in climatic condition, and in the areas with high level of habitat suitability, including Mount Sumbing, Mount Sindoro and Mount Dieng (Dieng Plateau), the reduction can reach up to 60%. This study adds another context of evidence to understand the potential impacts of climate change on biodiversity, especially on climate-sensitive species, such as Selaginella, in climate-risk regions like mountainous areas of Java Island.

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