Effects of landscape heterogeneity and breeding habitat diversity on rice frog abundance and body condition in agricultural landscapes of Yangtze River Delta, China

Reclamation history and land use types across multiple spatial scales shape anuran communities in the coastal land reclamation region

Land reclamation is a widely adopted method for managing land shortage and promoting coastal economic development globally. However, its impacts on biodiversity vary based on distinct reclamation histories and land use management strategies in different regions. This study aims to examine the effects of reclamation history and land use types at different spatial scales on anuran communities in coastal reclaimed land, which are an important taxon in the coastal ecosystem. We used visual and acoustic encounter methods to survey anurans in 2016 and 2017 across 20 1-km radius coastal land reclamation landscapes with different reclamation histories (10, 20, and 60 y after reclamation) in Nanhui Dongtan of Shanghai, an important coastal land reclamation region along the Yangtze River Estuary. Landscape variables (farmlands, woodlands, and impermeable surface covers, and the landscape Shannon diversity index) at four different spatial scales (250 m, 500 m, 750 m and 1000 m) and water salinity in each landscape were measured. Our findings reveal differences in anuran communities between study sites with 10, 20, and 60 years of reclamation history. Abundances of the ornamented pygmy frog (Microhyla fissipes) and Beijing gold-striped pond frog (Pelophylax plancyi) in landscapes with a 10-year reclamation history were significantly lower compared to those with histories of 20 and 60 years. Zhoushan toad (Bufo gargarizans) abundance was significantly negatively related to farmland cover at the 1000 m scale and impermeable surface cover at the 250 m scale; Hong Kong rice-paddy frog (Fejervarya multistriata) abundance was significantly positively related to farmland cover at the 1000 m scale; ornamented pygmy frog abundance was positively related to farmland cover at the 1000 m scale; and Beijing gold-striped pond frog abundance was significantly positively and negatively related to the landscape Shannon diversity index at the 1000 m scale and to water salinity, respectively. Amphibians quickly migrated and colonized coastal reclaimed land from older natural lands. However, two anuran species with specific habitat requirements tended to avoid areas with shorter reclamation histories. The single-species models revealed different responses to various land uses at the various scales, which indicated that land use management was important to amphibian conservation in coastal reclamation regions.

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring

Soundscape ecology is a promising area that studies landscape patterns based on their acoustic composition. It focuses on the distribution of biotic and abiotic sounds at different frequencies of the landscape acoustic attribute and the relationship of said sounds with ecosystem health metrics and indicators (e.g., species richness, acoustic biodiversity, vectors of structural change, gradients of vegetation cover, landscape connectivity, and temporal and spatial characteristics). To conduct such studies, researchers analyze recordings from Acoustic Recording Units (ARUs). The increasing use of ARUs and their capacity to record hours of audio for months at a time have created a need for automatic processing methods to reduce time consumption, correlate variables implicit in the recordings, extract features, and characterize sound patterns related to landscape attributes. Consequently, traditional machine learning methods have been commonly used to process data on different characteristics of soundscapes, mainly the presence-absence of species. In addition, it has been employed for call segmentation, species identification, and sound source clustering. However, some authors highlight the importance of the new approaches that use unsupervised deep learning methods to improve the results and diversify the assessed attributes. In this paper, we present a systematic review of machine learning methods used in the field of ecoacoustics for data processing. It includes recent trends, such as semi-supervised and unsupervised deep learning methods. Moreover, it maintains the format found in the reviewed papers. First, we describe the ARUs employed in the papers analyzed, their configuration, and the study sites where the datasets were collected. Then, we provide an ecological justification that relates acoustic monitoring to landscape features. Subsequently, we explain the machine learning methods followed to assess various landscape attributes. The results show a trend towards label-free methods that can process the large volumes of data gathered in recent years. Finally, we discuss the need to adopt methods with a machine learning approach in other biological dimensions of landscapes.

Design of Protected Area by Tracking and Excluding the Effects of Climate and Landscape Change: A Case Study Using Neurergus derjugini

This study aimed to use the applications of Ensemble Species Distribution Modelling (eSDM), Geographical Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA) for the design of a protected area (PA) for the critically endangered yellow-spotted mountain newt, Neurergus derjugini, by tracking and excluding the effects of climate and landscape changes in western Iran and northeastern Iraq. Potential recent and future distributions (2050 and 2070) were reconstructed by eSDM using eight algorithms with MRI-CGCM3 and CCSM4 models. The GIS-based MCDA siting procedure was followed inside habitats with high eSDM suitability by eliminating the main roads, cities, high village density, dams, poor vegetation, low stream density, agricultural lands and high ridge density. Then, within the remaining relevant areas, 10 polygons were created as “nominations” for PAs (NPAs). Finally, for 10 different NPAs, the suitability score was ranked based on ratings and weights (analytical hierarchy process) of the number of newt localities, NPA connectivity, NPA shape, NPA habitat suitability in 2070, NPA size, genetic diversity, village density and distance to nearest PAs, cities, and main roads. This research could serve as a modern realistic approach for environmental management to plan conservation areas using a cost-effective and affordable technique.