Camera traps, which are widely used in wildlife surveys, often produce massive images, and many of them are empty images not contain animals. Using the deep learning model to automatically identify the empty camera trap images can reduce the workload of manual classification significantly. However, the performance of deep learning models is easily affected by the class imbalance problem of training

Honey bees are not only the fundamental producers of honey but also the leading pollinators in nature. While honey bees play such a vital role in the ecosystem, they also face a variety of threats, including parasites, ants, hive beetles, and hive robberies, some of which could even lead to the collapse of colonies. Therefore, early and accurate detection of abnormalities at a beehive is crucial to

The recurrent forest fires have been a serious management concern in southern Western Ghats, India. This study investigates the applicability of various geospatial data, machine learning techniques (MLTs) and spatial statistical tools to demarcate the forest fire susceptible regions of the forested landscape of the Wayanad district in the southern Western Ghats (Kerala, India). The inventory map of

Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveillance of crops, coastal changes, flood risk assessment, and urban sprawl. In this paper, time-series satellite images are used to predict urban expansion. As the ground truth is not available in time-series satellite images, an unsupervised

A core issue in temporal ecology is the concept of trajectory—that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because

Ecological research on the Qinghai–Tibet Plateau (QTP) has become a hotspot in recent years, and a great number of related research papers have been published. Here, a bibliometrics method was utilized to analyze 4315 papers from the Web of Science published from 1990 to 2019, and a comprehensive overview based on metrics, including the publication growth, first author’ institutions and countries,

Recently, image based plant phenotyping is used extensively for plant trait estimation, plant accession classification and selection, and plant stress analysis. Plant accession classification and selection helps in identification of plants tolerant to local climatic conditions. Over the past few years, convolutional neural network (CNN) models were predominantly used for classification of plants and

Land change simulation for highly water-demanding crops may prove a valuable tool to guide integrated land-and-water management in semi-arid regions facing water scarcity. We explored this premise by mapping and modelling past (1985–2015) and future (2015–2030) orchard development relative to water resources and other factors in Iran. We employed a hybrid GEOMOD-Markov Chain model whereby both the

Lake Urmia –a hypersaline lake in northwest of Iran- has lost perceptible amount of its waterbody since mid ‘90s. Reasons behind this catastrophe could be categorized as climatic factors and anthropogenic activities. To investigate pattern changes and interactions of the components involved in this dramatic water loss, precipitation (P), discharge (Q), temperature (T), sea surface temperature (SST)

Monitoring wildlife in geographical areas is essential for the conservation of the biological heritage. At present, the strenuous task of animal observation on-site has been mitigated with the use of camera traps. The information gathered by these devices comprises a sequence of images, which are triggered by motion or heat, and enables the monitoring of several geographical areas at the same time

Bioacoustic monitoring with machine learning (ML) models can provide valuable insights for informed decision-making in conservation efforts. In this study, the team built deep convolutional neural networks to analyze field recordings and classify calls of Yellow-vented warbler (Phylloscopus cantator) and Rufous-throated wren-babbler (Spelaeornis caudatus), both of which are regionally rare in Nepal

Frog population has been experiencing rapid decreases worldwide, which is regarded as one of the most critical threats to global biodiversity. Therefore, large volumes of frog recordings have been collected for assessing this decline. Most previous studies focused on the syllable segmentation based frog call classification, which is sensitive to the background noise. Our recent study has used 1D-CNN

The detection and classification of Mysticetes' vocalisations have evoked the attention of researchers over the years because of their relevance to the marine ecosystem. These vocalisations are gathered employing different passive acoustic monitoring techniques. The vocalisation datasets are accumulated over a period; thus, they are large and cannot be easily analysed manually. Consequently, efficient

Migration is an essential ecological process, and is usually used to describe the seasonal movements between the breeding grounds and wintering areas. During migration, bird populations often disperse in groups and aggregate together due to geographical barriers, topography, seasonal climatic changes, species specific physiology, or other factors. Recording and reconstructing these diverse migratory

For several fish species, age and other important biological information is manually inferred from visual scrutinization of scales, and reliable automatic methods are not widely available. Here, we apply Convolutional Neural Networks (CNN) with transfer learning on a novel dataset of 9056 images of Atlantic salmon scales for four different prediction tasks. We predicted fish origin (wild/farmed), spawning

Excluding the polar areas, the Hindu Kush–Himalayan (HKH) Plateau and its surroundings have the most glaciers. The Himalayan Plateau and its surroundings have declined gradually over the past 100 years. However, unexpected shifts have taken place since the 1990s in the condition of the glaciers. The massive loss of the small glaciers, in particular, was more extreme than the failures of the gigantic

Houttuynia cordata Thunb is an important medicinal and edible plant widely distributed in East Asia. To understand the potential distribution characteristics of H. cordata and its response to future climatic change, the Maxent model is used to simulate potential distribution under current climatic condition, and predict changes in its distribution under three different future climate scenarios, and

Long-term monitoring of natural resources is imperative for increasing the understanding of ecosystem processes, services, and how to manage those ecosystems to maintain or improve function. Challenges with using these data may occur because methods of monitoring changed over time, multiple organizations collect and manage data differently, and monetary resources fluctuate, affecting many aspects of

Volunteer citizen scientists are an invaluable resource for classifying large numbers of images that are used for species monitoring. Citizen science projects often rely on the “wisdom of the crowd” through majority vote methods to produce accurate classifications and assume all volunteer citizen scientists have equal ability. We use a Bayesian framework to estimate iNaturalist NZ user accuracies and

Underwater cameras are widely used to monitor marine biodiversity, and the trend is increasing due to the availability of cheap action cameras. The main bottleneck of video methods now resides in the manual processing of images, a time-consuming task requiring trained experts. Recently, several solutions based on Deep Learning (DL) have been proposed to automatically process underwater videos. The

Trait-based approaches offer complementary views to the classic taxonomic approach, which is a crucial step forward to unveil mechanisms of community assembly, species interactions, ecosystem functioning, and tackling important conservation issues. These approaches require an enormous sampling effort to provide complete trait datasets, consequently, missing data are very common. We evaluated the performance

By adopting an analogical ecosystem perspective and by pursuing coexistence and coprosperity of the overall spatial development of urban agglomerations can avoid disorder and vicious competition that occur among cities. However, this perspective has not been applied to the research on the Yangtze River urban agglomeration. Thus, this paper attempts to construct a more comprehensive and scientific index

Incorporating user experience (UX) testing when creating research cyberinfrastructure is often overlooked, but if left too late, the cost of retrofitting is considerable, and the very clients the cyberinfrastructure was built to serve may be lost. Successfully integrating UX testing into the product development cycle can be difficult but rewarding. This paper describes how UX evaluations were incorporated

Satellite images taken on the earth's surface are analyzed to identify the spatial and temporal changes that have occurred naturally or manmade. Real-time prediction of change provides an understanding related to the land cover, environmental changes, habitat fragmentation, coastal alteration, urban sprawl, etc. In the current study, various digital change detection approaches and their constituent

Identifying and understanding limiting conditions is at the centre of ecology and biogeography. Traditionally, associations between climate and occurrences of organisms are inferred from observational data using regression analysis, correlation analysis or clustering. Those methods extract patterns and relationships that hold throughout a dataset. We present a computational methodology called redescription

Climate change is one of the main threats to many vulnerable species, including the Asiatic black bear (Ursus thibetnus), due to disruption in biological and ethological responses, hibernation, reproduction, and intraspecific and interspecific interactions. To assess the current and future distribution of the Asiatic black bear in the Hindu Kush Himalaya (HKH), we used maximum entropy modeling (MaxEnt)

Human interventions have largely influenced morphological, ecological and aesthetic quality of many rivers across the world. Scientists and researchers have developed several indices to measure ecological quality, ecological health and pollution status using physical, biological and chemical parameters. But the physical biological and chemical status of rivers are controlled by the terrain over which

Monitoring the size of key indicator species of fish is important to understand ecosystem functions, anthropogenic stress, and population dynamics. Standard methodologies gather data using underwater cameras, but are biased due to the use of baits, limited deployment time, and short field of view. Furthermore, they require experts to analyse long videos to search for species of interest, which is time

Accurate estimation of the spatial-temporal variation of the maximum gross primary productivity (GPPmax) of vegetation is of great significance for predicting carbon fluxes and vegetation-climate feedback. In this study, the GPPmax estimation models were constructed based on driving variables derived from the MODIS enhanced vegetation index (EVI) and land surface temperature (LST) time series data

This article focuses on a statistical analysis of the corona virus disease 2019 (COVID-19) data that appeared until November 31, 2020 in Poland. The studied database, expressed in terms of both population and air pollution (particulate) indicators, is provided mainly by the Airly company, the Central Statistical Office (GUS) and the Rogalski project. The particular measured factors, which underwent

Ecological inferences need structurally flexible statistical models to accommodate complex ecological phenomena. PyMC3 is a Probabilistic Programming Language (PPL) and allows for custom statistical distributions to build complex statistical models. This study used PyMC3 to implement Bayesian generalized Poisson (GP), zero-inflated GP, and hurdle GP regression models for over- and under-dispersed counts

Males of several fish species aggregate and vocalize together, increasing the detection range of the sounds and their chances of mating. In the Lusitanian toadfish (Halobatrachus didactylus), breeding males build nests under rocks in close proximity and produce hundreds of boatwhistles (BW) an hour to attract females to lay their demersal eggs on their nests. Chorusing behaviour includes fine-scale

With the progress and innovation of science and technology, virtual reality technology emerges as the times require, and its application in digital media art creation is more and more extensive. The application of virtual reality technology in digital media art creation helps to enrich artistic creation means and improve the quality of artistic creation process. This paper first gives the application

Wildlife-vehicle collision - WVC is a phenomenon that arises from the fragmentation of ecosystems by roads, limiting the mobility of individuals and putting at risk the stability of populations by increasing mortality. Colombia is not unaware of the problem of the WVC, evidenced in different scientific publications that describe the WVC in the roads of the country. Although the rise of artificial intelligence

The use of unmanned aerial vehicles (UAVs) as a useful tool in forestry studies is increasing rapidly. The level of accuracy in UAV-supported measurement data has been increasing in the field of forestry. Recently, biophysical and morphological tree information has been calculated using the three-dimensional (3D) point cloud data. The accuracy of studies on different types of trees (i.e. coniferous

Evaluation from landscape pattern change can comprehensively reflect the impact of ecological policies on the ecosystem service function. However, previous assessments based on landscape patterns only considered land cover type and ignored the quality of vegetation cover, which could hardly reflect regional differences in restoration durability and sustainability. Based on the temporal phase characteristics

The commercialization of Brazil nuts, seeds of Bertholletia excelsa Bonpl. (Lecythidaceae), represents one of the main income-generation activities for local and indigenous people from the Brazilian Amazon region. Because trees of B. excelsa grow and bear fruit almost exclusively in natural forests, information on their spatial distribution is crucial for nut harvest planning. However, this information

Bioconcentration factors (BCFs) are indicators of the accumulation of chemical substances in organisms; they play an important role in the environmental risk assessment of various chemical substances. Experiments to obtain BCFs are expensive and time consuming; hence, it is desirable to predictively determine BCF during the early stage of chemical development. In this study, we developed a quantitative

Species distribution models (SDMs) are efficient tools for modeling species geographic distribution under climate change scenarios. Due to differences among predictions of these models, their results are combined using consensus methods to form an ensemble model. This paper provides an optimal combination of the common SDMs according to accuracy and correlation to model the climatic suitability of

Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging

In order to ensure the rationality of landscape design, it is of great practical significance to simulate the planning effect. Aiming at the problems of two traditional landscape planning effect simulation systems, a new landscape planning effect simulation system was designed based on virtual reality technology. The overall framework of the system is composed of user layer, application layer and display

Food security is an important factor in maintaining the livelihood of people around the world. Plant biosecurity mainly deals with analyzing and managing the health of the plant. The biosecurity measures help in reducing the transmission of disease in plants. Environmental factors have a direct influence on determining the growth, stability, and resistance over a variety of diseases. Plants are highly

In the study of animal behaviour, annotation and analysis is largely done manually either directly in the field or from recordings. An emerging field, computational ethology, is challenging this approach by using machine learning to automate the process. However, the use of such methods in general is complicated by a lack of modularity, leading to high cost and long development times. At the same time

Deep learning has shown to achieve dramatic results in image classification tasks. However, deep learning models require large amounts of data to train. Most of the real-world datasets, generally insect classification data does not have large number of training dataset. These images have a large amount of noise and various differences. The paper proposes a novel architectural model which removes the

Field observations of the soil-to-atmosphere CO2 flux—soil respiration, RS—are a prime example of ‘long tail’ data that historically have had neither centralized databases nor an agreed-upon reporting format. This has hindered scientific transparency, analytical reproducibility, and syntheses with respect to this globally-important component of the carbon cycle. Here we propose a new data and metadata

Deforestation remains a major concern with regard to climate change and the maintenance of biodiversity. Meanwhile, the development of new image processing techniques and the broad availability of high spatiotemporal resolution satellite imagery provide an unprecedented setup for the development of effective and scalable forest cover change monitoring systems. This is especially relevant in regions

Continuous audio recordings are playing an ever more important role in conservation and biodiversity monitoring, however, listening to these recordings is often infeasible, as they can be thousands of hours long. Automating analysis using machine learning algorithms requires a feature representation. In this paper we propose a technique for learning a general feature representation from unlabelled

Narwhal (Monodon monoceros) is one of the most elusive marine mammals, due to its isolated habitat in the Arctic region. Tagging is a technology that has the potential to explore the activities of this species, where behavioral information can be collected from instrumented individuals. This includes accelerometer data, diving and acoustic data as well as GPS positioning. An essential element in understanding

Small Island Developing States are under significant threat from global change and unsustainable local practices. These issues are generally exacerbated by an inherent vulnerability to social, economic and ecological changes consequential to their unique island characteristics (e.g. limited resources), which is often described as “islandness”. These unique island characteristics or “islandness” have

Forest census allows getting precise data for logging planning and elaboration of the forest management plan. Species identification blunders carry inadequate forest management plans and high risks inside forest concessions. Hence, an identification protocol prevents the exploitation of non-commercial or endangered timber species. The current Peruvian legislation allows the incorporation of non-technical