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

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

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

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

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

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

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 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

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,

Depending on the life cycles of trophic biota, cascades of biological transition are established between top-down and bottom-up relationship. In South Korea, flows of the large rivers have been regulated with large weirs and estuary barrages construction. The Eurasian otter Lutra lutra is a semi-aquatic carnivore and top predator in freshwater systems. In order to understand the aquatic community variability

Species Distribution Models (SDMs) are useful tools to project potential future species distributions under climate change scenarios. Despite the ability to run SDMs in recent and reliable tools, there are some misuses and proxies that are widely practiced and rarely addressed together, particularly when dealing with marine species. In this paper, we propose an open-source framework that includes (i)

The plot of the frequencies that build soundscapes (power spectrum) has been used to show some of their distinctive characteristics. The application of a discretized sample space is proposed to describe, in a concise manner, the contribution of different frequency signals in a soundscape on a yearly scale. A sample space composed of frequency and intensity combinations was used to describe the shapes

Harmful algal blooms (HABs) are associated with water quality degradation, which damages fisheries, ecosystems, and public health. Consequently, techniques for detecting the occurrence of HABs have been developed globally. However, conventional techniques are unable to discriminate the causative species/genera of HABs objectively. Therefore, the aim of the present study was to develop a technique for

Bees are the main pollinators of most wild plant species and insect-pollinated crops and are essential for both plant ecosystems maintenance and humans food production. Among the crops used for human feeding, 75% depend on pollination. In addition to the fact that uncertainty around the beekeeping activity could jeopardize the economic value of pollination, data on honey bee colony losses exist but

Scales and otoliths (ear stones) from fish are routinely sampled for age estimation and fisheries management purposes. Growth records from scales and otoliths can be used to generate long-term time series data, and in combination with environmental data, can reveal species specific population responses to a changing climate. Additionally, scale and otolith microchemical data can be utilized to investigate

One standard method to capture data for phenological studies is with digital cameras, taking periodic pictures of vegetation. The large volume of digital images introduces the opportunity to enrich these studies by incorporating big data techniques. The new challenges, then, are to efficiently process large datasets and produce insightful information by controlling noise and variability. On these grounds

Biological invasions have increased in recent years, as these are facilitated by traffic networks of human infrastructure. Accidental release from the animal industry is an important cause of biological invasions, and allows for the clear determination of the source of non-native species. Such examples of invasion can be beneficial for studies on the relative importance of local environments by comparing

Wind strongly impacts the hydrodynamic and biogeochemical process of large shallow lakes, therefore wind stress also plays an important role in modeling the hydrodynamics and water quality of shallow lakes. In large shallow lakes, it may be necessary to modify the empirical wind-drag coefficient formula derived from ocean surface experiments because lake current velocities may be seriously underestimated

Automated acoustic recorders can collect long-term soundscape data containing species-specific signals in remote environments. Ecologists have increasingly used them for studying diverse fauna around the globe. Deep learning methods have gained recent attention for automating the process of species identification in soundscape recordings. We present an end-to-end pipeline for training a convolutional

Mathematical models are a powerful tool when used to describe ectotherms' life cycles, above all for their suitability in being utilised for decision support systems. In particular, two models continue to arouse the interest of the scientific community and inspire new developments: the Manetsch-VanSickle Distributed Delay Model and the Von Foerster equation. Even though these models have been widely

Monitoring the ecological status of natural habitats is crucial to the conservation process, as it enables the implementation of efficient conservation policies. Nowadays, it is increasingly possible to automate species identification, given the availability of very large image databases and state-of-the-art computational power which makes the training of automated machine learning-based classification

Determining how resource availability changes daily, seasonally and annually, and how wildlife react to these changes, is valuable for managing wildlife. For vegetative resources phenological information can be used to determine availability and model the distribution of available resources. This study develops a set of annually varying species distribution models using maximum entropy modeling for

With the increase of biological images, how to classify them effectively is a challenging problem, the Convolutional Neural Networks (CNNs) show promise for this problem. The challenges of using CNNs to handle images classification lie in two aspects: (1) How to further improve the classification accuracy? (2) How to make the network more light weight? To address the above challenges, this paper proposed

In this paper, we developed a robust learning method for animal classification from camera-trap images collected in highly cluttered natural scenes and annotated with noisy labels. We proposed two different network structures with and without clean samples to handle noisy labels. We use k-means clustering to divide the training samples into groups with different characteristics, which are then used

Plant phenology is a commonly used indicator representing the impacts on vegetation by the climate and other environmental factors. The use of repeated digital photography provides an opportunity to conduct long-term monitoring of plant phenology and to extract phenological transition dates. Here, we tracked the phenological changes in the flowering tree, Elaeagnus angustifolia L., in the Cherigele

Over the past two decades, bioacoustics and ecoacoustics have attracted a great deal of scholarly attention for their importance in investigating biology and ecology. Consequently, large volumes of academic articles have been published, which make it difficult to clearly understand current research trends. However, global research trends are important for grasping research direction and improving research

Bioclimatic variables are considered as an indispensable data type in species distribution modeling. Such variables are available from the WorldClim database for the entire earth surface and at various spatial resolutions. Moreover, convenient access to real-time satellite data and their products has recently created a new way to produce environmental variables. Therefore, in the present study, it

Plant size diversity or plant size inequality is a much neglected aspect of plant diversity and biodiversity. And yet size diversity plays an important role in understanding the dynamics of plant diversity and natural mechanisms of maintaining plant diversity, which is of great significance in a time of ongoing global changes. In this study, we introduced and investigated a new measure of spatial size

The inherent maintenance capacity of forest ecosystems is an important indicator to evaluate environmental quality. The development of remote sensing time series analysis provides a new perspective and methodology to evaluate such maintenance capacities, that take account of the gradual and subtle changes contained in forest growth over long time periods. This study analyzed forest ecosystem resilience

We present a new, efficient method for extracting three-dimensional animal motion trajectories from thermal stereo video data. Understanding animal behavior in the wild or other unconstrained environments is often based on animal movements. The technology described here is for understanding how bird and bat behavior is affected by the presence of wind turbines, specifically offshore wind turbines which

Bird species that occupy highly specialised ecological niche are susceptible to environmental and climatic change. These species can easily be moved into extinction by small anthropogenic or natural changes to their habitat. It is paramount to understand and assess the uncertainties of the impacts of climate change on the species to adopt adaptation strategies and provide revised management actions

In South Asian countries, Tenualosa ilisha, well known as Hilsa, is considered as one of the most economically important fish species. Production of Hilsa fishes depends on many factors including global warming, water pollution and harvesting. This article proposes a new mathematical model using fuzzy inferences to investigate the impacts of global warming, water pollution and harvesting of juvenile

Natural calamities like drought cause misery to human lives as well as environment in a variety of ways. The huge adverse consequences and globally predicted climate change accentuate the importance of an effective drought assessment and management system. Lack of universality of available drought indices emphasize the need of an automated system that works globally. Internet of Things (IoT) is highly

Benthic vegetation is arguably one of the most important indicators of the state of marine environment. Assessment of the status of eelgrass (Zostera marina) is commonly done using various remote sensing methods such as aerial photography or satellite images. These methods often fail to capture the true scenario beneath the surface of the water if the water is turbid or the satellite image is masked

In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Networks (CNNs). The specific animal audio classification problems are i) birds and ii) cat sounds, whose datasets are freely available. We train five different CNNs on the original datasets and on their versions augmented by

Deep learning (DL) algorithms are the state of the art in automated classification of wildlife camera trap images. The challenge is that the ecologist cannot know in advance how many images per species they need to collect for model training in order to achieve their desired classification accuracy. In fact there is limited empirical evidence in the context of camera trapping to demonstrate that increasing

Insect pests recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods and convolutional neural networks. Saliency methods are famous image processing algorithms that highlight the most relevant pixels of an image. In this paper, we use three different saliency methods as image preprocessing

An Increase in the study of cetaceans' sounds has motivated the development of different automated sound detection and classification techniques. Passive acoustic monitoring (PAM) is extensively used to study these cetaceans' sounds over a period to understand their daily activities within their ecosystem. Using PAM, the gathered sound datasets are usually large and impractical to manually analyse

It is important for marine scientists and conservationists to frequently estimate the relative abundance of fish species in their habitats and monitor changes in their populations. As opposed to laborious manual sampling, various automatic computer-based fish sampling solutions in underwater videos have been presented. However, an optimal solution for automatic fish detection and species classification

Species distribution models that only require presence data provide potentially inaccurate results due to sampling bias and presence data scarcity. Methods have been proposed in the literature to minimize the effects of sampling bias, but without explicitly considering the issue of sample size. A new method developed to better take into account environmental biases in a context of data scarcity is

Passive acoustic monitoring (PAM), particularly autonomous platforms, offers many advantages in monitoring phonating deep-diving marine mammals in oceanic environment. Relevant data can be obtained day and night continuously over long durations and in any weather conditions. It provides a cost-efficient solution with greater detection ranges when compared to traditional large research vessel and aerial

The identification and mitigation of sampling biases is commonly overseen in species distribution modeling, even though bias can seriously compromise the validity of modeling outcomes. Here we propose methods to 1) detect and mitigate spatial and detection sampling biases in the use of machine learning methods for modeling species occurrence and 2) assess the magnitude of bias and the effectiveness

Bathymetric proxies tend to indicate the relationships between the distribution of cetaceans and the seafloor in species distribution models (SDMs). Usually, seafloor features are described arbitrary to explain these relationships. Currently, a global seafloor geomorphic features dataset is available, with an objective identification and full representation for each seafloor geomorphic feature. Models

The surveillance of mosquito populations is paramount in the fight against mosquito-borne diseases that affect millions of people every year. Evaluating the efficiency of mitigation methods requires extensive and long-term surveys which can be costly and time consuming. The recent development of optical sensors give access to alternative methods for entomological monitoring but require efficient classification

The use of camera traps is now widespread and their importance in wildlife studies well understood. Camera trap studies can produce millions of photographs and there is a need for software to help manage photographs efficiently. In this paper, we describe a software system that was built to successfully manage a large behavioral camera trap study that produced more than a million photographs. We describe

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper