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Integration and harmonization of trait data from plant individuals across heterogeneous sources Ecol. Inform. (IF 2.511) Pub Date : 2021-02-27 Tim P. Lenters; Andrew Henderson; Caroline M. Dracxler; Guilherme A. Elias; Suzanne Mogue Kamga; Thomas L.P. Couvreur; W. Daniel Kissling
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Development of a species identification system of Japanese bats from echolocation calls using convolutional neural networks Ecol. Inform. (IF 2.511) Pub Date : 2021-02-17 Keigo Kobayashi; Keisuke Masuda; Chihiro Haga; Takanori Matsui; Dai Fukui; Takashi Machimura
Bats inhabit all continents except Antarctica, and they have enormous potential as bioindicators. Therefore, monitoring bats helps us to understand the surrounding environmental changes. However, bats are nocturnal, which makes it difficult to visually monitor their behavior. This paper proposes a bat species identifier method based on the analysis of ultrasound called echolocation calls, which is
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On estimating the parameters of generalized logistic model from census data: Drawback of classical approach and reliable inference using Bayesian framework Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Dipali Vasudev Mestry; Amiya Ranjan Bhowmick
In ecology, a nonconstant functional relationship between per capita growth rate and population size is referred as density dependence and mathematical models are utilized to detect its presence in natural populations. The theta-logistic model (parameterized by rm: intrinsic growth rate; θ: shape parameter; and K: carrying capacity) has been extensively discussed through various generalizations in
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Large-scale zero-shot learning in the wild: Classifying zoological illustrations Ecol. Inform. (IF 2.511) Pub Date : 2021-01-30 Lise Stork; Andreas Weber; Jaap van den Herik; Aske Plaat; Fons Verbeek; Katherine Wolstencroft
In this paper we analyse the classification of zoological illustrations. Historically, zoological illustrations were the modus operandi for the documentation of new species, and now serve as crucial sources for long-term ecological and biodiversity research. By employing computational methods for classification, the data can be made amenable to research. Automated species identification is challenging
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Identification of disease using deep learning and evaluation of bacteriosis in peach leaf Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Saumya Yadav; Neha Sengar; Akriti Singh; Anushikha Singh; Malay Kishore Dutta
Bacteriosis is one of the most common and devastating diseases for peach crops all over the world. Timely identification of bacteriosis disease is necessary for reducing the usage of pesticides and minimize loss of crops. In this proposed work, convolutional neural network (CNN) models using deep learning and an imaging method is developed for bacteriosis detection from the peach leaf images. In the
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Deep learning for supervised classification of temporal data in ecology Ecol. Inform. (IF 2.511) Pub Date : 2021-02-13 César Capinha; Ana Ceia-Hasse; Andrew M. Kramer; Christiaan Meijer
Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the
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Large-scale prediction of tropical stream water quality using Rough Sets Theory Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Laysson Guillen Albuquerque; Fabio de Oliveira Roque; Francisco Valente-Neto; Ricardo Koroiva; Daniel Forsin Buss; Darcílio Fernandes Baptista; Luiz Ubiratan Hepp; Mônica Luisa Kuhlmann; S. Sundar; Alan P. Covich; João Onofre Pereira Pinto
Assessing water quality in streams is usually measured at the local scale and often it is spatially restricted. To scale-up water-condition assessment, there is a need to use new tools that enable prediction of large-scale changes in water quality by expanding the analysis to landscape-levels and bioclimatic predictors. In addition, the traditional use of linear models in biomonitoring can be inappropriate
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Naive Bayesian fusion based deep learning networks for multisegmented classification of fishes in aquaculture industries Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Abinaya N.S.; Susan D.; Rakesh Kumar S.
Fish classification is an essential requirement for biomass estimation, disease identification, and quality analysis. In aquaculture industries, fish classification is carried out in the processing unit. The fishes are out of water, subjecting them to structural deformation and orientation misalignments, makes classification challenging. A multisegmented fish classification technique using deep learning
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Monitoring and driving force analysis of spatial and temporal change of water area of Hongjiannao Lake from 1973 to 2019 Ecol. Inform. (IF 2.511) Pub Date : 2021-02-02 Hongye Cao; Ling Han; Zhiheng Liu; Liangzhi Li
Hongjiannao Lake is the largest desert freshwater lake in China and the world's largest breeding habitat for gulls, which is of great significance to the local ecological environment and the breeding of key protected birds, and was listed as a national key protected lake in 2012. This study uses Landsat series satellite remote sensing data from 1973 to 2019 in combination with the NDWI water body index
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Integrating data and analysis technologies within leading environmental research infrastructures: Challenges and approaches Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Robert Huber; Claudio D'Onofrio; Anusuriya Devaraju; Jens Klump; Henry W. Loescher; Stephan Kindermann; Siddeswara Guru; Mark Grant; Beryl Morris; Lesley Wyborn; Ben Evans; Doron Goldfarb; Melissa A. Genazzio; Xiaoli Ren; Barbara Magagna; Hannes Thiemann; Markus Stocker
When researchers analyze data, it typically requires significant effort in data preparation to make the data analysis ready. This often involves cleaning, pre-processing, harmonizing, or integrating data from one or multiple sources and placing them into a computational environment in a form suitable for analysis. Research infrastructures and their data repositories host data and make them available
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Balancing the needs of consumers and producers for scientific data collections Ecol. Inform. (IF 2.511) Pub Date : 2021-02-15 Deborah Agarwal; Joan Damerow; Charuleka Varadharajan; Danielle Christianson; Gilberto Pastorello; You-Wei Cheah; Lavanya Ramakrishnan
Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to long-tail data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section
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Reconstruction of damaged herbarium leaves using deep learning techniques for improving classification accuracy Ecol. Inform. (IF 2.511) Pub Date : 2021-02-02 Burhan Rashid Hussein; Owais Ahmed Malik; Wee-Hong Ong; Johan Willem Frederik Slik
Leaf is one of the most commonly used organs for species identification. The traditional identification process involves a manual analysis of individual dried or fresh leaf's features by the botanists. Recent advancements in computer vision techniques have assisted in automating the plants families/species identification process based on the digital images of leaves. However, most of the existing studies
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Impact of climate change on the distribution of Sal species Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Shambhu Nath Mishra; Hari Shankar Gupta; Nitin Kulkarni
The Sal (Shorea robusta Gaertn.) of south and northern continental Southeast Asia form mono-specific canopies in dry deciduous, moist deciduous forests. These Sal forest ecosystems are the source of ecosystem services, besides harbouring rich biodiversity. The model results show that projected climate change impacts on Sal species have the potential to trigger significant ecosystem-level responses
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Based investigate of beehive sound to detect air pollutants by machine learning Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Yangguang Zhao; Guoqing Deng; Long Zhang; Nayan Di; Xueli Jiang; Zhigang Li
As honey bees are extremely sensitive to a variety of chemicals and emit typical sound when exposed to environmental chemical, the sound of beehive may be explored as signal to monitor atmospheric pollutants. In the study, the beehives were exposed to the common air pollution chemicals of acetone, Trichloromethane, Glutaric dialdehyde and ethyl ether, and collect beehive sound data using a beehive
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Deep learning-based models for temporal satellite data processing: Classification of paddy transplanted fields Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Anuvi Rawat; Anil Kumar; Priyadarshi Upadhyay; Shashi Kumar
Deep learning-based frameworks have not been much explored to incorporate the temporal dimension of the remote sensing data. In this research work, deep learning-based models have been developed which exploit the spectral-temporal domain of bi-sensor remote sensing data obtained from Sentinel-2 and Landsat-8 satellites. Application of proposed deep learning frameworks has been tested for mapping transplanted
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BirdNET: A deep learning solution for avian diversity monitoring Ecol. Inform. (IF 2.511) Pub Date : 2021-01-27 Stefan Kahl; Connor M. Wood; Maximilian Eibl; Holger Klinck
Variation in avian diversity in space and time is commonly used as a metric to assess environmental changes. Conventionally, such data were collected by expert observers, but passively collected acoustic data is rapidly emerging as an alternative survey technique. However, efficiently extracting accurate species richness data from large audio datasets has proven challenging. Recent advances in deep
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Long-term deep learning-facilitated environmental acoustic monitoring in the Capital Region of New York State Ecol. Inform. (IF 2.511) Pub Date : 2021-02-02 M.M. Morgan; J. Braasch
The effect of anthropogenic activity on animal communication is of increasing ecological concern. Passive acoustic recording offers a robust, minimally disruptive, long-term approach to monitoring species interactions, particularly because many indicator species of environmental health factors such as biodiversity, habitat quality, and pollution produce distinct vocalizations. Machine learning algorithms
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Identification of animals and recognition of their actions in wildlife videos using deep learning techniques Ecol. Inform. (IF 2.511) Pub Date : 2021-01-27 Frank Schindler; Volker Steinhage
Biodiversity crisis has continued to accelerate. Studying animal distribution, movement and behaviour is of critical importance to address environmental challenges such as spreading of diseases, invasive species, climate and land-use change. Camera traps are an appropriate technique for continuous animal monitoring in an automated 24/7/52 documentation. This study shows a proof-of-concept for an end-to-end
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Group behavior tracking of Daphnia magna based on motion estimation and appearance models Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Zhitao Wang; Chunlei Xia; JangMyung Lee
Daphnia magna is a widely adopted biological indicator for studying the environmental toxicity in aquatic ecosystems. Due to the tiny size and appearance similarity of Daphnia most behavioral tracking systems can only record the behavioral movement of a single individual. In this work, a novel behavioral tracking scheme is proposed to track the group movement of Daphnia and record the accurate trajectory
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Relationship between multiple ecosystem services and sustainability in three species food chain Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Kanisha Pujaru; T.K. Kar; Prosenjit Paul
The world fisheries are mainly focused on the yield from fishing, and hence maximum sustainable yield (MSY) policy has become the most popular management tool. But this aim of maximum productivity, particularly in the multitrophic system, impacts the resilience, considering the potential of the system to absorb the disturbances applied to the system. Due to harvesting effort used on different individual
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Multi-class fish stock statistics technology based on object classification and tracking algorithm Ecol. Inform. (IF 2.511) Pub Date : 2021-02-06 Tao Liu; Peiliang Li; Haoyang Liu; Xiwen Deng; Hui Liu; Fangguo Zhai
The development of intensive aquaculture has increased the need for video-based underwater monitoring technology to generate statistics on multi-class fish. However, the complex marine environment, e.g., light fluctuations, shape deformations, similar appearance of fish, and occlusions, makes this a challenging task. Therefore, there are relatively few studies in this field. This paper proposes a real-time
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Detecting seasonal transient correlations between populations of the West Nile Virus vector Culex sp. and temperatures with wavelet coherence analysis Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Petros Damos; Pablo Caballero
Cullex sp. is one of the most important mosquito disease vector and climate is considered to be a key factor affecting its population dynamics. In this study we use straightforward techniques based on correlations and wavelet analysis to determine the non-trivial associations between Culex sp. mosquito abundance and weather variables (lagged population abundance, mean temperatures, rain and wind speed)
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An evaluation of feature selection methods for environmental data Ecol. Inform. (IF 2.511) Pub Date : 2021-01-29 Dimitrios Effrosynidis; Avi Arampatzis
We present a comprehensive experimental study of 12 individual as well as 6 ensemble methods for feature selection for classification tasks on environmental data, more specifically on the species distribution modeling domain. The individual methods span all 3 categories, i.e. filter, wrapper, and embedded feature selection. Experiments on 8 environmental datasets show that Shapley Additive Explanations
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Collect and analysis of agro-biodiversity data in a participative context: A business intelligence framework Ecol. Inform. (IF 2.511) Pub Date : 2021-01-30 Sandro Bimonte; Olivier Billaud; Benoît Fontaine; Thomy Martin; Frédéric Flouvat; Ali Hassan; Nora Rouillier; Lucile Sautot
In France and Europe, farmland represents a large fraction of land cover. The study and assessment of biodiversity in farmland is therefore a major challenge. To monitor biodiversity across wide areas, citizen science programs have demonstrated their effectiveness and relevance. The involvement of citizens in data collection offers a great opportunity to deploy extensive networks for biodiversity monitoring
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MLAs land cover mapping performance across varying geomorphology with Landsat OLI-8 and minimum human intervention Ecol. Inform. (IF 2.511) Pub Date : 2021-01-27 Jianbo Tan; Jiaqi Zuo; Xinyao Xie; Meiqing Ding; Zhuokui Xu; Fangbin Zhou
The machine learning algorithms (MLAs) are capable of automatic land cover classification with a huge volume of data and are prevalent in land mapping applications. Minimal human intervention is desired when producing land cover products over a large area and the choice of an algorithm may determine the precision of the map. The study aims to compare the performance of random forest (RF), decision
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Animal reidentification using restricted set classification Ecol. Inform. (IF 2.511) Pub Date : 2021-02-02 Ludmila I. Kunchev
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Evaluation of water quality based on UAV images and the IMP-MPP algorithm Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Hanting Ying; Kai Xia; Xinxi Huang; Hailin Feng; Yinhui Yang; Xiaochen Du; Leijun Huang
In recent years, UAV remote sensing has been used to estimate water quality parameters, such as suspended solids (SS), turbidity (TUB), and chlorophyll-a (chl-a) levels, due to its low cost, convenience, and high resolution. The matching pixel-by-pixel (MPP) algorithm is one of the methods to find the optimal regression equation for retrieving water quality parameters from UAV images. However, MPP
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Review of machine learning techniques for mosquito control in urban environments Ecol. Inform. (IF 2.511) Pub Date : 2021-01-26 Ananya Joshi; Clayton Miller
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A reporting format for leaf-level gas exchange data and metadata Ecol. Inform. (IF 2.511) Pub Date : 2021-01-24 Kim S. Ely; Alistair Rogers; Deborah A. Agarwal; Elizabeth A. Ainsworth; Loren P. Albert; Ashehad Ali; Jeremiah Anderson; Michael J. Aspinwall; Chandra Bellasio; Carl Bernacchi; Steve Bonnage; Thomas N. Buckley; James Bunce; Angela C. Burnett; Florian A. Busch; Amanda Cavanagh; Lucas A. Cernusak; Robert Crystal-Ornelas; Dedi Yang
Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these
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Multispecies discrimination of whales (cetaceans) using Hidden Markov Models (HMMS) Ecol. Inform. (IF 2.511) Pub Date : 2021-01-23 Marek B. Trawicki
Hidden Markov Models (HMMs) were developed and implemented for the discrimination of 11 available Whales (Cetaceans). The primarily aims of the experiments were to explore the impact frame size and step size, feature vector size, and number of states for feature extraction and acoustic models on classification accuracy. Through the experiments using Mel-Frequency Cepstral Coefficients (MFCCs) extracted
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A quantitative detection algorithm based on improved faster R-CNN for marine benthos Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Yong Liu; Shengnan Wang
In order to realize the accurate quantitative detection of marine benthos and solve the problems in detecting small and densely distributed benthic organisms under overlapping and occlusion image, a quantitative detection algorithm for marine benthos based on Faster R-CNN is proposed. A convolution kernel adaptive selection unit is embedded in the backbone to enhance the feature extraction ability
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Evolutionary overview of water resource management (1990–2019) based on a bibliometric analysis in Web of Science Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Tiangui Lv; Li Wang; Hualin Xie; Xinmin Zhang; Yanwei Zhang
Globally, only 3% of water is fresh water that can be directly used by people. Limited water resources threaten fields, which are closely associated with social and economic development. Water resource management is an effective way to measure the supply and demand of water resources and improve the efficiency of water resource use and equalize spatial allocation. This article retrieved 1430 water
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Spatial transferability of expert opinion models for American beaver habitat Ecol. Inform. (IF 2.511) Pub Date : 2021-01-23 Isidro A. Barela; Leslie M. Burger; Guiming Wang; Kristine O. Evans; Qingmin Meng; Jimmy D. Taylor
Species distribution models and habitat suitability models (HSMs) have become a popular tool in the conservation of biodiversity. However, the ability to predict species spatial distributions at sites beyond the data source sites (i.e., spatial transferability) is critical for the applications of HSMs in the management and conservation of rare or endangered species. The main objective of our study
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Development of a predictive model for soil temperature and its application to species distribution modeling of ant species in South Korea Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Jae-Min Jung; Heung-Sik Lee; Jong-Ho Lee; Sunghoon Jung; Wang-Hee Lee
Soil temperature is an important factor for determining ant species inhabitation, but it has rarely been applied in species distribution modeling due to limited data sources. Hence, this study aimed to develop a predictive model for soil temperature and evaluate the potential distribution of a few ant species in South Korea. The monthly maximum and minimum soil temperatures were predicted by linear
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Modeling alterations in flow regimes under changing climate in a Mediterranean watershed: An analysis of ecologically-relevant hydrological indicators Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Youssef Brouziyne; Anna Maria De Girolamo; Aziz Aboubdillah; Lahcen Benaabidate; Lhoussaine Bouchaou; Abdelghani Chehbouni
The potential response of flow regimes to future climate has crucial importance for a variety of practical applications, such as sustainable water management and ecological asset preservation. For this study, multi-site investigations of alterations in flow regimes under projected climate change were performed for one of the largest watersheds in Morocco, the Bouregreg Watershed (BW). Future daily
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Principal component analysis of incomplete data – A simple solution to an old problem Ecol. Inform. (IF 2.511) Pub Date : 2021-01-23 János Podani; Tibor Kalapos; Barbara Barta; Dénes Schmera
A long-standing problem in biological data analysis is the unintentional absence of values for some observations or variables, preventing the use of standard multivariate exploratory methods, such as principal component analysis (PCA). Solutions include deleting parts of the data by which information is lost, data imputation, which is always arbitrary, and restriction of the analysis to either the
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Advances in image acquisition and processing technologies transforming animal ecological studies Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Sajid Nazir; Muhammad Kaleem
Images and videos have become pervasive in ecological research and the ease of acquiring image data and its subsequent processing can provide answers in research areas such as species recognition, animal behaviour, and population studies which are critical for animal conservation and biodiversity. Technological advances in imaging are enabling data collection from new areas such as from underwater
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Learning niche features to improve image-based species identification Ecol. Inform. (IF 2.511) Pub Date : 2021-01-23 Congtian Lin; Xiongwei Huang; Jiangning Wang; Tianyu Xi; Liqiang Ji
Species identification is a critical task of ecological research. Having accurate and intelligent methods of species identification would improve our ability to study and conserve biodiversity with saving much time and effort. But image-based deep learning methods have rarely taken account of domain knowledge, and perform poorly on imbalanced training dataset and similar species. Here, we propose NicheNet
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Effects of abiotic factors on plant diversity and species distribution of alpine meadow plants Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Qi-peng Zhang; Jian Wang; Qian Wang
The alpine meadow is a type of vegetation with significant ecological and economic values. Plant diversity and species distribution are affected by ecological environment. To study the influence of abiotic factors (topographic factors and soil chemical and physical properties), it can understand plant communities' formation and the adaptation of plants to abiotic factors. Here, we explored the influence
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Application of modeling techniques for the identification the relationship between environmental factors and plant species in rangelands of Iran Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Javad Esfanjani; Ardavan Ghorbani; Mehdi Moameri; Mohammad Ali Zare Chahouki; Abazar Esmali Ouri; Zohre Sadat Ghasemi
The objective of the present research was to compare Ecological Niche Factor Analysis (ENFA) and Artificial Neural Networks (ANN) to determine the optimum threshold of plant species (Thymus kotschyanus Boiss and Hohen. and Dactylis glomerata L.) in rangelands of Ardabil province. Systematic random sampling of vegetation was performed, and an overall 111 sites were considered and divided into two groups
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A forest management optimization model based on functional zoning: A comparative analysis of six heuristic techniques Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Elizabeth Serrano-Ramírez; José René Valdez-Lazalde; Héctor Manuel de los Santos-Posadas; Roman Anselmo Mora-Gutiérrez; Gregorio Ángeles-Pérez
Current approaches to forest management must be capable of specifying where, when, and how much can be harvested from the forest to procure wood production while dealing with multiple operational and conservation considerations. To accomplish such a task, in this work, we present a multi-objective optimization model based on the “triad zoning” approach, which proposes dividing a forest under management
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Identification of plant species in an alpine steppe of Northern Tibet using close-range hyperspectral imagery Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Enqin Liu; Hui Zhao; Shuhui Zhang; Jing He; Xin Yang; Xiangming Xiao
The identification of plant species in alpine steppes of Northern Tibet is of great significance for revealing community structures and for monitoring vegetation degradation and restoration from remote sensing images. Plants in the alpine steppe of Northern Tibet are short, sparse, and highly heterogeneous in spatial distribution. This peculiarity makes the plant species identification here much more
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Developing a new disturbance index for tracking gradual change of forest ecosystems in the hilly red soil region of southern China using dense Landsat time series Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Lu Ye; Meiling Liu; Xiangnan Liu; Lihong Zhu
Gradual change is prevalent across the forest landscape and generates long-lasting effects for the landscape surface; thus, tracking long-term gradual change can effectively characterize forest change processes. The objective of this study was to establish a vegetation index for change monitoring so as to determine long-term gradual change processes of forest ecosystems in typical red soil areas. The
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Exploring the potential to use low cost imaging and an open source convoluted neural network detector to support stock assessment of the king scallop (Pecten maximus) Ecol. Inform. (IF 2.511) Pub Date : 2021-01-20 Katja Ovchinnikova; Mark A. James; Tania Mendo; Matthew Dawkins; Jon Crall; Karean Boswarva
King Scallop (Pecten maximus) is the third most valuable species landed by UK fishing vessels. This research assesses the potential to use a Convolutional Neural Network (CNN) detector to identify P. maximus in images of the seabed, recorded using low cost camera technology. A ground truth annotated dataset of images of P. maximus captured in situ was collated. Automatic scallop detectors built into
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Bayesian and frequentist approaches to multinomial count models in ecology Ecol. Inform. (IF 2.511) Pub Date : 2020-12-23 Guiming Wang
Multinomial count data are common in trophic ecology, spatial ecology, and community ecology. Compared to generalized linear models (GLMs) for univariate data, fewer statistical programs have been developed for the multivariate counterpart of GLMs. Studies of spatiotemporal community dynamics and spatial ecology on large spatial scales may generate large or big data on compositional counts or proportions
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Importance of benthic-pelagic coupling in food-web interactions of Kakinada Bay, India Ecol. Inform. (IF 2.511) Pub Date : 2020-12-19 Swagata Sinha; Arnab Banerjee; Nabyendu Rakshit; Akkur V. Raman; Punyasloke Bhadury; Santanu Ray
Benthic components occupy the sediment layer of aquatic ecosystems and play a definitive role in overall system functioning and maintenance. The exchange of inorganic and organic materials between the sediment and water column through benthic-pelagic coupling plays a very important role especially in shallow water ecosystems. It is facilitated mainly by trophic interactions between the benthic and
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Individual tree crown delineation from high-resolution UAV images in broadleaf forest Ecol. Inform. (IF 2.511) Pub Date : 2020-12-24 Mojdeh Miraki; Hormoz Sohrabi; Parviz Fatehi; Mathias Kneubuehler
Unmanned aerial vehicles (UAVs) paired with a structure from motion (SfM) algorithm (UAV-SfM) can be used to derive canopy height models (CHMs) for individual tree crown delineation (ITCD). ITCD algorithms normally perform well in coniferous forests, but their capabilities in broadleaf or mixed forests are still challenging. In this study, we investigated the application of three ITCD algorithms using
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VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant Ecol. Inform. (IF 2.511) Pub Date : 2020-11-10 Rakesh Chandra Joshi; Manoj Kaushik; Malay Kishore Dutta; Ashish Srivastava; Nandlal Choudhary
Various viral diseases affect the growth of the plants that causes a huge loss to farmers. If the viral infection could be noticed at earlier stages, then recovery procedures and respective action can be taken on time. Thus, there is a need for developing automatic viral infection detection methods for monitoring of crops analysing symptoms at different parts of plants. This paper proposes an automatic
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Validation and uncertainty analysis of the match climates regional algorithm (CLIMEX) for Pest risk analysis Ecol. Inform. (IF 2.511) Pub Date : 2020-11-09 Mariona Roigé; Craig B. Phillips
Pest risk analysis (PRA) is conducted by plant health authorities to identify and implement phytosanitary measures to limit the accidental introduction and spread of species that are, or may become, pests. The ‘Match Climates Regional’ (MCR) algorithm is part of the CLIMEX species distribution modelling package and is a simple climate matching method that has often been applied in PRA to estimate the
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Critical windows: A method for detecting lagged variables in ecological time series Ecol. Inform. (IF 2.511) Pub Date : 2020-11-22 Jean-Sébastien Pierre; Maurice Hullé; Jean-Pierre Gauthier; Claude Rispe
Many developmental processes in the life sciences, ecology and even in economics depend strongly on the environmental conditions occurring in a bounded time interval, the results occurring often far later. Examples are as diverse as plant phenology, grapewine maturation, diapause induction and so on. The method proposed here, aims at detecting quickly such effects. The basic idea is to regress the
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Study on the temporal and spatial distribution of chlorophyll a in Erhai Lake based on multispectral data from environmental satellites Ecol. Inform. (IF 2.511) Pub Date : 2020-11-12 Xingmin Wang; Yun Deng; Youcai Tuo; Rui Cao; Zili Zhou; Yao Xiao
Satellite remote sensing technology presents advantages of macroscopicity, timeliness and cost effectiveness and has been increasingly used in lake water quality monitoring. In this paper, an empirical model for the remote sensing inversion of the chlorophyll a (Chl-a) concentration in Erhai Lake was established using ground monitoring Chl-a concentration data and multispectral remote sensing data
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From local spectral species to global spectral communities: A benchmark for ecosystem diversity estimate by remote sensing Ecol. Inform. (IF 2.511) Pub Date : 2020-11-11 Duccio Rocchini; Nicole Salvatori; Carl Beierkuhnlein; Alessandro Chiarucci; Florian de Boissieu; Michael Förster; Carol X. Garzon-Lopez; Thomas W. Gillespie; Heidi C. Hauffe; Kate S. He; Birgit Kleinschmit; Jonathan Lenoir; Marco Malavasi; Vítĕzslav Moudrý; Harini Nagendra; Davnah Payne; Petra Šímová; Michele Torresani; Jean-Baptiste Féret
In the light of unprecedented change in global biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in
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Forest recovery prognostics in conservation units of the Atlantic rainforest Ecol. Inform. (IF 2.511) Pub Date : 2020-11-12 L.A. Richit; J.F. Richit; C. Bonatto; R.V. da Silva; J.M.V. Grzybowski
Forest growth models can provide valuable support tools for forest recovery assessment and forestry management, whether in the form of diagnostic or prognostic. Furthremore, they can be applied to characterize each phytophysiognomy in terms of vegetation growth parameters that and can be applied to gauge the spatiotemporal progress of recovery processes. Up to date, such parameters remain mostly unknown
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Effects of class imbalance on resampling and ensemble learning for improved prediction of cyanobacteria blooms Ecol. Inform. (IF 2.511) Pub Date : 2020-11-09 Jihoon Shin; Seonghyeon Yoon; YoungWoo Kim; Taeho Kim; ByeongGeon Go; YoonKyung Cha
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Statistical comparison of additive regression tree methods on ecological grassland data Ecol. Inform. (IF 2.511) Pub Date : 2020-11-12 Emily Plant; Rachel King; Jarrod Kath
Additive tree methods are widely used in ecology. To date most ecologists have used boosted regression tree (BRT) methods. However, Bayesian additive regression tree (BART) models may offer advantages to ecologists previously unexamined. Here we test whether BART has some benefits over the widely used BRT method. To do this we use two grassland data and 13 hydroclimatic and land use predictor variables
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Exploring the development of scientific research on Marine Protected Areas: From conservation to global ocean sustainability Ecol. Inform. (IF 2.511) Pub Date : 2020-11-10 F. Picone; E. Buonocore; R. Chemello; G.F. Russo; P.P. Franzese
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Quantitative assessment of the importance of bio-physical drivers of land cover change based on a random forest method Ecol. Inform. (IF 2.511) Pub Date : 2020-11-10 Yanrong Meng; Mingxia Yang; Shan Liu; Yuling Mou; Changhui Peng; Xiaolu Zhou
The spatial distribution patterns of land cover greatly influence the ecological balance of the Loess Plateau. Understanding the bio-physical drivers of land cover change is important for ecological restoration in the context of climate change. However, in the analysis of the drivers of land cover change in the Loess Plateau, the role of bio-physical drivers has not been quantitatively evaluated. Using
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Will the yellow fever mosquito colonise Europe? Assessing the re-introduction of Aedes aegypti using a process-based population dynamical model Ecol. Inform. (IF 2.511) Pub Date : 2020-11-03 Daniele Da Re; Diego Montecino-Latorre; Sophie O. Vanwambeke; Matteo Marcantonio
Aedes aegypti are feared invasive mosquitoes as they transmit pathogens which cause debilitating diseases in humans. Although mainland Europe has not yet witnessed re-establishment and dispersal of Ae. aegypti populations, several urban areas along coastlines represent suitable habitats for the species. In addition, European coastal areas are characterised by high exotic species propagule pressure
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Text mining in fisheries scientific literature: A term coding approach Ecol. Inform. (IF 2.511) Pub Date : 2020-11-11 Ioannis Fytilakos
Text mining has not yet been fully explored in fisheries scientific literature and applications in existing studies have been limited. In the present study, quantitative text analysis was used in order to identify various subtopic trends and gaps in the knowledge of the fisheries science field. Several multivariate and descriptive analyses —including word extraction, word association, cluster analysis
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Discrete-event models for conservation assessment of integrated ecosystems Ecol. Inform. (IF 2.511) Pub Date : 2020-11-12 C. Gaucherel; C. Carpentier; I.R. Geijzendorffer; C. Noûs; F. Pommereau
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