The field of acoustic communication needs tools that facilitate the annotation and labeling of animal calls. Bat acoustic libraries gathered over the past few decades have primarily focused on compiling echolocation calls, which have been leveraged to develop machine learning algorithms capable of classifying bat species. However, because these classification methods require large training datasets, they have not yet been generalized to classify types of bat communication calls. Communication call repertoires in bats are wide, and distinct syllables occur with varying frequency, with some call types being recorded only rarely. Furthermore, collecting Classification communication calls poses greater technical challenges, making these calls more difficult to capture reliably. Here, we present BattyCoda, an open-access, customizable tool to categorize and label bat communication call types within the repertoire of a species using small training datasets (tens to hundreds of labeled calls). In this work, we compiled an initial training dataset of 11 types of big brown bat () calls, tested the performance of various candidate classifiers, and assessed the final classifier's training sample size sensitivity. We found that the best performing classifier achieved a balanced accuracy of ~50 %, with common call types achieving classification accuracies over 70 %. Our tool can greatly facilitate annotating bat calls in recordings by providing accurate labels for common call types, while also assisting researchers in categorizing rarer communication calls. BattyCoda has the potential to build research capacity in the field of acoustic communication by expanding the availability of libraries including a wider range of bat calls and species, thereby enabling the exploration of new hypotheses.

Most coral reef studies focus on scleractinian (stony) corals to indicate reef condition, but there are other prominent assemblages that play a role in ecosystem structure and function. In Puerto Rico these include fish, gorgonians, and sponges. The U.S. Environmental Protection Agency conducted unique surveys of coral reef communities across the southern coast of Puerto Rico that included simultaneous measurement of all four assemblages. Evaluating the results from a community perspective demands endpoints for all four assemblages, so patterns of community structure were explored by probabilistic clustering of measured variables with Bayesian networks. Most variables were found to have stronger associations within than between taxa, but unsupervised structure learning identified three cross-taxa relationships with potential ecological significance. Clusters for each assemblage were constructed using an expectation-maximization algorithm that created a factor node jointly characterizing the density, size, and diversity of individuals in each taxon. The clusters were characterized by the measured variables, and relationships to variables for other taxa were examined, such as stony coral clusters with fish variables. Each of the factor nodes were then used to create a set of meta-factor clusters that further summarized the aggregate monitoring variables for the four taxa. Once identified, taxon-specific and meta-clusters represent patterns of community structure that can be examined on a regional or site-specific basis to better understand risk assessment, risk management and delivery of ecosystem services.

The Massachusetts Bays National Estuary Partnership is one of 28 programs in the United States Environmental Protection Agency's National Estuary Program (NEP) charged with developing and implementing comprehensive plans for protecting and restoring the biological integrity and beneficial uses of their estuarine systems. The Partnership has recently updated their comprehensive management plan to include restoration targets for coastal habitats, and as part of this effort, the program explored how to better demonstrate that recovery of ecological integrity of degraded ecosystems also provides ecosystem services that humans want and need. An essential step was to identify key stakeholders and understand the benefits important to them. The primary objective of the study presented here was to evaluate variability in beneficial uses of estuarine habitats across coastal communities in Massachusetts Bays. We applied a text mining approach to extract ecosystem services concepts from over 1400 community planning documents. We leveraged a Final Ecosystem Goods and Services (FEGS) classification framework and related scoping tool to identify and prioritize the suite of natural resource users and ecosystem services those users care about, based on the relative frequency of mentions in documents. Top beneficiaries included residents, experiencers and viewers, property owners, educators and students, and commercial or recreational fishers. Beneficiaries had a surprising degree of shared interests, with top ecosystem services of broad relevance including for naturalness, fish and shellfish, water movement and navigability, water quality and quantity, aesthetic viewscapes, availability of land for development, flood mitigation, and birds. Community-level priorities that emerged were primarily related to regional differences, the local job industry, and local demographics. Identifying priority ecosystem services from community planning documents provides a starting point for setting locally-relevant restoration goals, designing projects that reflect what stakeholders care about, and supporting post-restoration monitoring in terms of accruing relevant benefits to local communities.

The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation.

Sticky trap catches of agricultural pests can be employed for early hotspot detection, identification, and estimation of pest presence in greenhouses or in the field. However, manual procedures to produce and analyze catch results require substantial time and effort. As a result, much research has gone into creating efficient techniques for remotely monitoring possible infestations. A considerable number of these studies use Artificial Intelligence (AI) to analyze the acquired data and focus on performance metrics for various model architectures. Less emphasis, however, was devoted to the testing of the trained models to investigate how well they would perform under practical, in-field conditions.

The COVID-19 pandemic that has hit the whole world has caused losses in various aspects. Several countries have implemented lockdowns to curb the spread of the SARS-CoV-2 virus that caused death. However, for developing countries such as Indonesia, it is not suitable for lockdown because it considers the economic recession. Instead, the Large-scale Social Restrictions (LSSR) regulation is applied, the same as the partial lockdown. Thus, it is hypothesized that implementing LSSR that limits anthropogenic activities can reduce heat emissions and air pollution. Utilization of remote sensing data such as Terra-MODIS LST and Sentinel-5P images to investigate short-term trends (i.e., comparison between baseline year and COVID-19 year) in surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO, CO, and O in Malang City and Surabaya City, East Java Province. Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. Raster re-gridding was also implemented to refine the Sentinel-5P spatial resolution. The accuracy of LST spatial downscaling results is quite satisfactory in both cities. Surface temperatures in both cities slightly decreased (below 1 °C) during LSSR was applied ( < 0.05). SUHII in both cities experienced a slight increase in both cities during LSSR. NO gas was reduced significantly ( < 0.05) in Malang City (∼38%) and Surabaya City (∼28%) during LSSR phase due to reduced vehicle traffic and restrictions on anthropogenic activities. However, CO and O gases did not indicate anomaly during LSSR. Moreover, this study provides insight into the correlation between SUHII change and the distribution of air pollution in both cities during the pandemic year. Air temperature and wind speed are also added as meteorological factors to examine their effect on air pollution. The proposed models of spatial downscaling LST and re-gridding satellite-based air pollution can help decision-makers control local air quality in the long and short term in the future. In addition, this model can also be applied to other ecological research, especially the input variables for ecological spatial modeling.

With new technological advancements and increasing demands of open data in environmental sciences, the requirements for data are increasing in a variety of ways. Having machine and human readable documentation about environmental research and monitoring sites available online is one of them. The Dynamic Ecological Information Management System - Site and Dataset Registry (DEIMS-SDR, https://www.deims.org/) is a research and monitoring site registry that allows the description of in-situ observation or experimental sites, generating persistent, unique and resolvable identifiers for each site. The aim of DEIMS-SDR is to collect site information in a catalogue describing a wide range of sites across the globe, providing information including each site's location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available. This article describes the outcomes of the revision of its data model, the conceptual considerations behind it and how it is implemented. These conceptual considerations also encompass the definition of what we call the "onion model of site data interoperability" - a fundamental concept for the design of site data models against the backdrop of data interoperability. Furthermore, we illustrate the capabilities of the revised data model and provide an overview of common data formats for the description of sites, current initiatives driving the harmonisation of descriptions and the outlook of future developments.

The COVID-19 pandemic has led to reduced anthropogenic pressure on ecosystems in several world areas, but resulting ecosystem responses in these areas have not been investigated. This paper presents an approach to make quick assessments of potential habitat changes in 2020 of eight marine species of commercial importance in the Adriatic Sea. Measurements from floating probes are interpolated through an advection-equation based model. The resulting distributions are then combined with species observations through an ecological niche model to estimate habitat distributions in the past years (2015-2018) at 0.1° spatial resolution. Habitat patterns over 2019 and 2020 are then extracted and explained in terms of specific environmental parameter changes. These changes are finally assessed for their potential dependency on climate change patterns and anthropogenic pressure change due to the pandemic. Our results demonstrate that the combined effect of climate change and the pandemic could have heterogeneous effects on habitat distributions: three species (, , and ) did not show significant niche distribution change; habitat suitability positively changed for , but negatively for , due to increased temperature and decreasing dissolved oxygen (in the Adriatic) generally correlated with climate change; the combination of these trends with an average decrease in chlorophyll, probably due to the pandemic, extended the habitat distributions of and but reduced distribution. Although our results are based on approximated data and reliable at a macroscopic level, we present a very early insight of modifications that will possibly be observed years after the end of the pandemic when complete data will be available. Our approach is entirely based on Findable, Accessible, Interoperable, and Reusable (FAIR) data and is general enough to be used for other species and areas.

In this study, mean monthly and diurnal variations in fine particulate matters (PM), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NO, O, nitrate (NO ), and sulfate (SO ) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NO concentration due to a decrease in traffic flow under the NO-saturated regime was observed to enhance the secondary NO and O formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NO, O, NO , and SO , respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH, HNO, and HSO, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

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 standardization, were assessed for mutual dependency by means of a Pearson correlation coefficient and analysed by a linear regression. Based on the presented models, our results indicate that air quality (air pollution level) is the most important factor in the context of enabling COVID-19 case load increase in Poland.

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 the software architecture and the design decisions that shaped the evolution of the program over the study's three year period. The software system has the ability to automatically extract metadata from images, and add customized metadata to the images in a standardized format. The software system can be installed as a standalone application on popular operating systems. It is minimalistic, scalable and extendable so that it can be used by small teams or individual researchers for a broad variety of camera trap studies.

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 assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results.

Simulating epidemics in metapopulations is a challenging issue due to the large demographic and geographic scales to incorporate. Traditional epidemiologic models choose to simplify reality by ignoring both the spatial distribution of populations and possible intrapopulation heterogeneities, whereas more recent solutions based on Individual-Based Modeling (IBM) can achieve high precision but are costly to compute and analyze. We introduce here an original alternative to these two approaches, which relies on a novel hybrid modeling framework and incarnates a multiscale view of epidemics. The model relies on a technical fusion of two modeling paradigms: System Dynamics (SD) and Individual-Based Modeling. It features an aggregated representation of local outbreaks rendered in SD, and at the same time a spatially-explicit simulation of the spread between populations simulated in IBM. We first present the design of this deterministic model, show that it can reproduce the dynamics of real resurgent epidemics, and infer from the sensitivity of several spatial factors absent in compartmental models the importance of having large-scale epidemiological processes represented inside of an explicitly disaggregated metapopulation. After discussing the implications of results obtained from simulation runs and the applicability of this model, we conclude that SD-IB hybrid modeling can be an interesting choice to represent epidemics in a spatially-explicit way without necessarily taking into account individual heterogeneities, and therefore it can be considered as a valuable alternative to simple compartmental models suffering from detrimental effects of the well-mixed assumption.