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

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

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

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

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

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

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

The distinction of landscapes based on their sound patterns is useful for several analyses. For instance, comparisons of audio files from different periods enable the detection of changes over time in a particular habitat, signaling events of importance, such as modifications in the balance between species and presence of new ones. The handling of a large number of different sound recordings in wild

It is currently widely recognized that automated methods are crucial to help processing long-term recordings of marine bioacoustics. To evaluate the efficiency of such methods, it is essential to develop large-scale annotated datasets. However, besides being laborious and resource intensive, recent studies have suggested that such a task could also be highly subjective with the generation of annotator

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

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

The impacts of invasive plant species on the environment and economy make effectively detecting and mapping them crucial. Using leaf spectral reflectance and the advantages of continuous wavelet analysis (CWA), we aimed to utilize the CWA and features reduction techniques (principal component analysis (PCA), regularized random forest (RRF), and guided regularized random forest (GRRF)) and two famous

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

Most plant diseases show visible symptoms, and the technique which is accepted today is that an experienced plant pathologist diagnoses the disease through optical observation of infected plant leaves. The fact that the disease diagnosis process is slow to perform manually and another fact that the success of the diagnosis is proportional to the pathologist's capabilities makes this problem an excellent

New methodologies based on photonic sensors to survey flying insect populations in their natural habitat have gained traction over the last decade. Instruments such as entomological lidars, radars, or standoff sensors using active or passive light sources can observe large numbers of insects transiting through their field of view. While counting transits can inform on the relative change in insect

In this study remote sensing was used as an early alert tool for chlorophyll-a changes in a continental aquatic ecosystem in central Chile. To this end, 14 LANDSAT images of Laja Lake (36° 54 'S, 71° 05' W, 1,360 meters above sea level) were taken over a 6-year period. Eleven spectral indexes were calculated. At the same time, chlorophyll-a concentrations were determined experimentally. Correlations

The development of vegetation communities is typically influenced by their response to variation in environmental, geographic and as well as physiographic gradients. The current study was planned to evaluate the influence of environmental gradients upon the structure of plant communities and to highlight their respective indicators in subalpine pastures of the Himalaya, Pakistan. In relation to this

Regarding the effects of warming on phenology, essentially that climate warming is advancing vegetation phenology around the globe, and case studies of specific species/regions work to gauge this impact. Thus, we attempted to demonstrate phenological patterns of selected 4 alpine/sub-alpine and 4 temperate medicinal herbs based on higher medicinal value of Indian Himalayan Region (IHR) using total

The quantity and nutritional quality of forage are key drivers for ungulate populations, including mule deer (Odocoileus hemionus) and Rocky Mountain elk (Cervus elaphus nelsoni), in the western U.S., but current vegetation maps are too coarse spatially and temporally to effectively characterize fine-scale habitat. To address some of these gaps, we tested a novel approach using existing vegetation

In aquatic ecosystems, anthropogenic activities disrupt nutrient fluxes, thereby promoting harmful algal blooms that could directly impact economies and human health. Within this framework, the forecasting of the proxy of chlorophyll a in coastal areas is the first step to managing these algal blooms. The primary goal was to analyze how phytoplankton bloom forecasts are impacted by different sampling

Since 2007, green tide disasters have occurred in Chinese Yellow Sea for 11 consecutive years. The continuous outbreak of green tides has caused various degrees of economic loss and ecological damage to coastal areas. In this study, we use GF1-WFV (GaoFen No.1-Wide Field of View) high resolution images and GOCI (Geostationary Ocean Color Imager) images comprehensively. NDVI (Normalized vegetation index)

Nutrient leakage due to modern agriculture and disposal of untreated urban wastewater results in the eutrophication of freshwater lakes and reservoirs, with elevated levels of cyanobacteria algal blooms often resulting in toxic conditions for animals and humans. A unique dataset of 22 coincident nutrient, phytoplankton, meteorological, and reservoir condition variables were recorded or calculated monthly

The comparison between spatial or temporal patterns is often needed for model evaluation and change detection in ecological studies. The statistics developed for image quality assessment, such as the structural similarity index (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), have been introduced for comparing ecological patterns.

RolWinMulCor estimates the rolling window correlation for bi- and multi-variate cases between regular time series, with particular emphasis on ecological data. It is based on the concept of rolling, running or sliding window correlation, being useful for evaluating the evolution and stability of correlation over time. RolWinMulCor contains six functions to estimate and to plot the correlation coefficients

Phytoplankton sinking is an important property that can determine community composition, affecting nutrient and light absorption in the photic zone, and influencing biogeochemical cycling via material loss to the deep ocean. To date, the difficulty in exploring the sinking processes is partly due to methodological limitations in measuring phytoplankton sinking rate. However, in the last decade, works

This research explores the data selection problem in acoustic recognition of two co-existing sibling frog species from long-duration field recordings. This study explores the data selection problem in species recognition with machine learning, including instance selection and acoustic index feature selection. Our target species are two co-existing frog species. The Wallum Sedgefrog (Litoria olongburensis)

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology, and melissopalynology. This paper presents a new public annotated image dataset for the Brazilian Savanna called POLLEN73S composed of 2523 images from 73 pollen types. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide a baseline for pollen

Mobile survey tools (MSTs) are helpful for the collection and management of massive field information for the surveying and planning of township land resources. Willingness and consciousness information, which are derived from residents, are critical for land resources business at the rural scale. However, existing MSTs focus on surrounding environmental information collection (locations, environmental

Balancing the representation of marine ecosystems and threatened resources is critical for optimizing total rent (fishing+ecotourism), achieving the convention on sustainable use of resources. Comprehensive marine zoning provides a useful tool with which these essential concepts are addressed. A bioeconomic model is constructed, allowing three different aims to reserve, fishing, ecotourism, and total

Landmarks are one of the important concepts in morphometry analysis. They are anatomical points that can be located consistently (e.g., corner of the eyes) and used to establish correspondence or divergence among morphologies of biological or non-biological specimens. Currently, the landmarks are mostly positioned manually by entomologists on numerical images. In this work, we propose a method to automatically

The French Alps is an important area for several species of crayfish; some species are native in this region and several were introduced by humans. The present study aimed at modelling the distributions of these two categories of species and find the significant environmental variables to explain these distributions. Field sampling was performed in this region in 2014 resulting in enough data for modelling

In the recent years, several citizen science platforms for biodiversity monitoring have emerged. These platforms represent a powerful tool for collecting biodiversity data for researchers and increasing the knowledge of participants. Typical biodiversity data are species names observed at a given time and place by numerous participants. The use of photos to document observations allows data validation

Information related to research sites is essential when describing the context of observations. It is a key element of site-based research infrastructures (RIs) and their catalogues. This paper is dedicated to the comparison of (meta)data models describing research sites in the ecosystem domain. A special focus is on sites in terrestrial and freshwater ecosystems. This should provide the basis for

Providing clean drinking water for society is an important priority and a growing challenge in many regions. Increasing food production to feed a growing world population can lead to intensification of agricultural management systems and land use (sometimes appropriate, sometimes not), and the potential for pollution of surface and ground water bodies may be is drastically increased. In order to meet

The goal of this paper is to present a methodology for professional evaluation, comparison and selection of alternatives in conservation planning and related areas. Our decision methodology is based on soft computing and consistent with observable properties of human reasoning. Main topics that we cover include the presentation of the LSP (Logic Scoring of Preference) decision method, the analysis

Habitat and reproduction surveys of raptors are common components of environmental impact assessments. Raptors are few in number and widely dispersed; therefore, raptor surveys require greater survey frequency compared to surveys for many other bird species, which, in turn, may result in extra costs as well as negative impact on raptors caused by observers. In this research, we propose a new, efficient

Deep learning algorithms have improved predictive model performance in a variety of disciplines because of their ability to approximate complex functions. However, the amount of data and depth of the neural network needed to improve model performance is not well understood and may depend on many factors associated with the specific field of research. In ecology, ecologists rely on accurate species

Recently, there has been an increasing interest in model-based approaches for the statistical modelling of the joint distribution of multi-species abundances. The Dirichlet-multinomial distribution has been proposed as a suitable candidate distribution for the joint species distribution of pin-point plant cover data and is here applied in a model-based ordination framework. Unlike most model-based

In recent years the European Alps experienced higher temperatures, more heatwaves, and more severe wildfires. Improving fire danger assessment for these sensitive ecosystems is a core element of future-oriented fire management strategies in the face of climate change. While meteorological systems are common to predict fire danger in many countries, other factors such as vegetation, topography, lightning

Understanding the relationship between the geographical distribution of taxa and their environmental conditions is a key concept in ecology and conservation. The use of ensemble modelling methods for species distribution modelling (SDM) have been promoted over single algorithms such as Maximum Entropy (MaxEnt). Nevertheless, we suggest that in cases where data, technical support or computational power

Describing the behaviors of free-living animals is broadly useful for ecological and physiological research, but obtaining accurate records for difficult-to-observe species presents a considerable challenge. Tri-axial accelerometers are increasingly used for this purpose by exploiting behavioral observations from accelerometer-carrying animals to predict behaviors of unobserved conspecifics. We developed

Wildlife tracking data allow monitoring of how organisms respond to spatio-temporal changes in resource availability. Remote sensing data can be used to quantify and qualify these variations to understand how movement is related to these changes. The use of remote sensing data with concurrent high levels of spatial and temporal detail may hold potential to improve our understanding of habitat selection

Environmental change and fishing activity can produce directional trends in exploited fish populations with consequences for stock productivity. For herring in the Celtic Sea, size at age has been in steady decline since the mid 1980's. In the neighbouring herring stock off the Northwest coast of Ireland, reductions in size at age are noted after 1990. Here, gradient boosting regression trees were

Current daily AQI forecasting results do not help people making decision to undertake outdoor activities in the coming hours effectively, because the air quality index (AQI) varies greatly at different times of the day. In this study, an approach to providing a rolling forecast of the next six-hour AQI in the seven air quality areas of Taiwan by using big data analysis is proposed. The segmented fitted

The introduction of alien species represents the second most dramatic cause of biodiversity loss all over the world as they may negatively affect the genetic diversity of native species and represent a major problem to both conservation strategies, and functionality of the ecosystems. In the last centuries, sika deer (Cervus nippon) have been widely spread from their native areal (China, Japan, Viet

Aquatic ecosystem modelling has been a challenging task for scientists partly due to limited tools/software to support. This study attempted to develop a Python toolkit (PyAEM) that was able to support multiple procedures in aquatic ecosystem modelling. PyAEM included improved algorithms for sensitivity analysis, parameter optimization, data assimilation and model visualization. In PyAEM, one-at-a-time

Classification of specimens is the important first step to characterize populations and species assemblages. Although species-level classification has been a popular goal, the sex difference and sex ratio are also an important property in ecology and pest control. Here we focus on the images of mixed sex specimens of a stored product pest beetle (Callosobruchus chinensis) and its parasitoids (parasitic

Deep learning networks have become increasingly popular in recent years due to promising breakthroughs achieved in several areas. The importance of deep learning lies in the localisation and classification of an object based on frames. This study focuses on fish recognition methods in underwater videos and addresses the underlying challenges of these methods. It is important to develop effective methods

No systematic approach has yet been adopted to reliably reference and provide access to digital biodiversity datasets. Based on accumulated evidence, we argue that location-based identifiers such as URLs are not sufficient to ensure long-term data access. We introduce a method that uses dedicated data observatories to evaluate long-term URL reliability. From March 2019 through May 2020, we took periodic

Passive acoustic telemetry data are used to study animal movement in aquatic environments, but tools to process the data are limited. In areas that are too large to be fully covered by the limited detection ranges of receivers or acoustic listening stations, researchers generally assume that animals are residing in an area when they are detected frequently at specific receivers. There is, however,