Spatial and Temporal Dynamics of Birch-Mining Eriocrania Moths in an Urban Landscape over Four Decades

Urban transmission of arthropod-vectored disease has increased in recent decades. Understanding and managing transmission potential in urban landscapes requires integration of sociological and ecological processes that regulate vector population dynamics, feeding behavior, and vector-pathogen interactions in these unique ecosystems. Vectorial capacity is a key metric for generating predictive understanding about transmission potential in systems with obligate vector transmission. This review evaluates how urban conditions, specifically habitat suitability and local temperature regimes, and the heterogeneity of urban landscapes can influence the biologically-relevant parameters that define vectorial capacity: vector density, survivorship, biting rate, extrinsic incubation period, and vector competence.Urban landscapes represent unique mosaics of habitat. Incidence of vector-borne disease in urban host populations is rarely, if ever, evenly distributed across an urban area. The persistence and quality of vector habitat can vary significantly across socio-economic boundaries to influence vector species composition and abundance, often generating socio-economically distinct gradients of transmission potential across neighborhoods.Urban regions often experience unique temperature regimes, broadly termed urban heat islands (UHI). Arthropod vectors are ectothermic organisms and their growth, survival, and behavior are highly sensitive to environmental temperatures. Vector response to UHI conditions is dependent on regional temperature profiles relative to the vector's thermal performance range. In temperate climates UHI can facilitate increased vector development rates while having countervailing influence on survival and feeding behavior. Understanding how urban heat island (UHI) conditions alter thermal and moisture constraints across the vector life cycle to influence transmission processes is an important direction for both empirical and modeling research.There remain persistent gaps in understanding of vital rates and drivers in mosquito-vectored disease systems, and vast holes in understanding for other arthropod vectored diseases. Empirical studies are needed to better understand the physiological constraints and socio-ecological processes that generate heterogeneity in critical transmission parameters, including vector survival and fitness. Likewise, laboratory experiments and transmission models must evaluate vector response to realistic field conditions, including variability in sociological and environmental conditions.

Trees play a vital role in urban landscapes, yet long-term dynamics in tree populations across different levels of urbanization remain poorly understood. We examined whether current spatial patterns of native tree populations predict future changes by monitoring two native birch species (Betula pendula and B. pubescens) in six residential areas of St. Petersburg, Russia. Birch density declined toward the city centre by 1.87 trees ha−1 km−1. From 2002 to 2022, birch populations in sparsely built-up areas (6–8 km from the centre) declined by 0.15 trees ha−1 year−1 due to ageing and urban development, while populations in densely built-up areas near the centre increased by 0.02 trees ha−1 year−1 due to limited tree cutting and greater planting efforts. These trends challenge the assumption that spatial patterns reliably predict temporal changes, emphasizing the complex interplay between ecological and societal factors in urban tree dynamics. We anticipate the continued decline in birch populations in sparsely built-up areas of St. Petersburg over the next 10–20 years until residents recognize the value of their declining greenery and either pressure the city government to intensify planting efforts or begin planting trees themselves.

Aim Urbanization is associated with strong changes in biodiversity, but the diversity of plant and animal assemblages varies among urban habitats. We studied effects of urban habitats on the diversity of vascular plants and land snails in 32 large cities. Location Central Europe, Belgium and the Netherlands. Methods The species composition of all vascular plants that had not been planted by humans, and all land snails, was recorded in seven 1-ha plots within each city. Each plot contained one urban habitat type representing a different disturbance regime: historical city square, boulevard, residential area with compact building pattern, residential area with open building pattern, park, early successional and mid-successional site. For each plot, we obtained temperature and precipitation data. The effects of climate and habitat types on species composition were quantified using ordination methods with an adjusted variation partitioning algorithm. Differences in species composition among urban habitats were described using statistically determined diagnostic species, and differences in alpha, beta and gamma diversity were quantified. Results A total of 1196 plant and 87 snail species were recorded. Habitat type explained higher proportions of the total variation in both plant and snail species composition (11.2 and 8.2%, respectively) than did climate (4.6 and 6.3%). For both taxa, the main differences in species composition were observed between strongly urbanized sites in city centres and early successional and mid-successional sites. For vascular plants, the number of species was lowest in city squares and boulevards, and highest at successional sites and in residential areas with compact building patterns. Beta diversity of vascular plants calculated for the same habitat types among cities was highest for squares and successional sites. The number of snail species was lowest in city squares and at early successional sites, and highest at mid-successional sites. The highest beta diversity of snail assemblages among cities was observed within the city square and early successional habitat types, and the lowest within residential area habitat types. Main conclusions Urban habitats differ notably in the diversity of their vascular plant flora and land snail fauna. Understanding the habitat-related biodiversity patterns in urbanized landscapes will allow projections of future impacts of urban land-use changes on the biota.

Abstract. Land Surface Temperature (LST) is a critical parameter for urban climate analysis, photovoltaic (PV) planning, and environmental monitoring, yet its effective use is hindered by the coarse spatial resolution of thermal sensors like MODIS. This study introduces a two-stage hierarchical Convolutional Neural Network (CNN) framework that integrates multi-sensor satellite data (MODIS, ASTER, Sentinel-2) to generate fine-scale LST products and quantitatively assess scaling effects across heterogeneous landscapes. In the first stage, MODIS LST (1 km) was downscaled to 360 m, 270 m, and 90 m using Sentinel-2–derived scaling factors and validated against ASTER LST. In the second stage, ASTER LST (90 m) was downscaled to 60 m and 30 m and validated with Landsat-based LST. The framework employed spectral indices, topographic parameters, texture features, and sub-pixel land-cover fractions as scaling factors, capturing both spatial and spectral heterogeneity. Comparative evaluation against XGBoost and DisTrad revealed that CNN consistently achieved the highest determination coefficients (R2 = 0.69–0.87) and the lowest RMSE (1.94–2.34 K) and MAE (1.49–1.8 K) values, confirming its superior capacity to model complex nonlinear thermal relationships. Scaling-effect analysis demonstrated that while accuracy naturally decreases with finer resolutions, the CNN model exhibits strong scale stability and resilience to error propagation, outperforming traditional regression and machine-learning approaches. This hierarchical deep-learning design establishes a new paradigm for multi-sensor LST reconstruction, enabling accurate, scalable, and spatially coherent thermal mapping across diverse terrains. The proposed framework offers a generalizable solution for high-resolution thermal monitoring, PV site optimization, and climate-adaptive urban planning.

Understanding the determinants of biodiversity in fragmented habitats is fundamental for informing sustainable landscape development, especially in urban landscapes that substantially fragment natural habitat. However, the relative roles of landscape and habitat characteristics, as emphasized by two competing frameworks (the island biogeography theory and the habitat diversity hypothesis), in structuring species assemblages in fragmented habitats have not been fully explored. This study investigated bird assemblages at 26 habitat patches (ranging in size from 0.3 to 290.4 ha) in an urban landscape, southwest China, among which habitat type composition and woody plant species composition varied significantly. Through 14 bird surveys conducted over six breeding seasons from 2017 to 2022, we recorded 70 breeding bird species (excluding birds recorded only once and fly-overs, such as raptors, swallows and swifts), with an average of 26 ± 10 (SD) species per patch. We found that patch area had significant direct and indirect effects on bird richness, with the indirect effects mediated by habitat richness (i.e., the number of habitat types). Isolation (measured as the distance to the nearest patch), perimeter to area ratio (PAR), and woody plant richness did not significantly predict variation in bird richness. Furthermore, none of these factors significantly sorted bird species based on their functional traits. However, the overall makeup of bird assemblages was significantly associated with the specific habitat types and woody plant species present in the patches. The results suggest that neither the island biogeography theory nor the habitat diversity hypothesis can fully explain the impacts of habitat fragmentation on bird richness in our study system, with their roles primarily being linked to patch area. The findings that habitat and plant compositions were the major drivers of variation in bird assemblage composition offer valuable insights into urban planning and green initiatives. Conservation efforts should focus not only on preserving large areas, but also on preventing urban monocultures by promoting diverse habitats within those areas, contributing to the persistence of meta-communities.

While many studies on insect diversity report declines, others show stable, fluctuating or increasing trends. For a thorough understanding of insect trends and their effects on ecosystem functioning, it is important to simultaneously assess insect richness, abundance and biomass, and to report trends for multiple taxa. We analysed insect richness, abundance and biomass data for all insects and for eight insect taxa (Buprestidae, Cerambycidae, Carabidae, other Coleoptera, Aculeata, other Hymenoptera, Heteroptera and Lepidoptera) from 42 sites across Switzerland from 2000 to 2007, representing three major habitat types in Switzerland (agricultural, unmanaged [open and forested] and managed forest habitats). As potential drivers of temporal patterns, we evaluated weather‐ and land‐use‐related factors. As predictors, we included temperature and precipitation as well as the vegetation index and the habitat type, respectively. We found a consistent pattern of stable or increasing trends for richness, abundance and biomass of insects in total and the eight taxa over 8 years. Both overall patterns and six out of eight taxa (except for Cerambycidae and Lepidotpera) showed the highest values in agricultural habitats. However, when accounting for elevation, there was no difference in open habitats regardless of whether they were used agriculturally. Habitat types were the most important predictors, followed by weather‐ and vegetation‐related factors. Modelled responses to mean temperature were unimodal, whereas the standard deviation of temperature showed positive and precipitation negative effects. Longer time series are needed to draw robust inferences and to investigate potential negative effects of future warming.

Linking landscape patterns with ecological functions: A case study examining the interaction between landscape heterogeneity and carbon stock of urban forests in Xiamen, China

The rapid development of urbanization has changed landscape patterns and biological habitats severely and, therefore, affected biodiversity. In this study, we selected 75 townships in Lishui, a mountainous area in eastern China, to conduct bird surveys for two years. We analyzed the birds' composition characters in townships with different levels of development in order to identify the effects on bird diversity of the urban development level, land cover pattern, landscape pattern, and other factors. In total, 296 bird species from 18 orders and 67 families were recorded between December 2019 and January 2021. A total of 166 species of birds belonged to Passeriformes (56.08%). The seventy-five townships were divided into three grades by K-means cluster analysis. The average number of bird species, richness index, and diversity index were higher in G-H (highest urban development level) compared with the other grades. At the township level, landscape diversity and landscape fragmentation were the key factors that positively affected the bird species number, diversity index, and richness index. Landscape diversity had a greater effect than landscape fragmentation, particularly on the Shannon-Weiner diversity index. The diversity and heterogeneity of urban landscapes could be improved by constructing biological habitats in future urban development planning to maintain and increase biodiversity. The results obtained in this study provide a theoretical basis for urban planning in mountainous areas, and a reference for policymakers to formulate biodiversity conservation strategies, construct reasonable biodiversity patterns, and solve practical biodiversity conservation problems.

Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect E. coli, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and E. coli, lead, zinc, and TSS concentrations. Road length is positively associated with E. coli and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R2 = 0.58), followed by TSS (R2 = 0.54) and nitrate (R2 = 0.46). E. coli was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R2. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling.

The ongoing COVID-19 pandemic is a stark reminder of the devastating consequences of pathogen spillover from wildlife to human hosts, particularly in densely populated urban centers. Prevention of future zoonotic disease is contingent on informed surveillance for known and novel threats across diverse human-wildlife interfaces. Cities are a key venue for potential spillover events because of the presence of zoonotic pathogens transmitted by hosts and vectors living in close proximity to dense human settlements. Effectively identifying and managing zoonotic hazards requires understanding the socio-ecological processes driving hazard distribution and pathogen prevalence in dynamic and heterogeneous urban landscapes. Despite increasing awareness of the human health impacts of zoonotic hazards, the integration of an eco-epidemiological perspective into public health management plans remains limited. Here we discuss how landscape patterns, abiotic conditions, and biotic interactions influence zoonotic hazards across highly urbanized cities (HUCs) in temperate climates to promote their efficient and effective management by a multi-sectoral coalition of public health stakeholders. We describe how to interpret both direct and indirect ecological processes, incorporate spatial scale, and evaluate networks of connectivity specific to different zoonotic hazards to promote biologically-informed and targeted decision-making. Using New York City, USA as a case study, we identify major zoonotic threats, apply knowledge of relevant ecological factors, and highlight opportunities and challenges for research and intervention. We aim to broaden the toolbox of urban public health stakeholders by providing ecologically-informed, practical guidance for the evaluation and management of zoonotic hazards.

Review of "Movement patterns of free-roaming dogs on heterogeneous urban landscapes: implications for rabies control"

Population genetics of the brown rat (Rattus norvegicus) in a heterogeneous urban landscape

Humans are a keystone species in urban ecosystems. Although the impact of human activities is increasingly reported in behavioral and evolutionary ecology, little is known about the effects of physical human presence per se. Of particular relevance is the extent to which human presence (sometimes referred to as human disturbance), is repeatable across the urban mosaic over time. We quantified human presence at fixed locations—here in a 15-m radius of great tit nestboxes—within six urban and suburban study sites. While overall human presence did not differ between urban sites, it was significantly higher than in the suburban village. Moreover, considerable variance between fixed locations was observed within each site. We reported overall high repeatability of human presence for all sites (0.57 < R < 0.88, mean: 0.76, median: 0.77). We further simulated datasets of human presence made of an increasing number of counts, and demonstrated that 10 counts of human presence (each 30 s long) made at each fixed location can generate a highly reliable indicator of human presence (R ≥ 0.6) for any urban site. We thus confirm that human presence is repeatable across the urban mosaic, thereby offering consistent cues to urban wildlife of human presence or absence in space and time. Importantly, our approach of human presence quantification allows for an effort-efficient approach to understand the effects of physical human presence at designated temporal timeframes, thereby allowing to reliably test the effects of human presence on the behavior and ecology of urban wildlife.

Combining multivariable statistics and geostatistics with landscape metrics, we attempted to quantify the spatial pattern of urbanization in the city of Niamey, Niger. Landscape metrics provided local quantification of both landscape composition and physiognomy while the Principal Component Analysis (PCA) yielded a multivariable summary of the main source of landscape metrics variation across the city. We used the variogram (geostatistics) to analyze the spatial pattern of the PCA outcomes and to characterize the associated spatial scales of variation. In Niamey, the main urban structure corresponded to a gradient ranging from highly diversified, fragmented, and both wooded and built-up areas in the city center and along the Niger River, to less green zones gathering steel-roofed houses whose density diminished towards the periphery. This concentric structure centered on the Niger River clearly reflected the history of Niamey. PCA and geostatistics provided appealing quantitative estimates of spatial patterns, scales, anisotropy and intensity of urban structures. Although these different tools are known in landscape ecology, they are rarely used together. The present paper illustrates how they allow characterizing the marked spatial variation of the urban landscape of the fast-growing African city of Niamey (Niger). Such a quantification of the urban landscapes may be extremely useful for future correlative investigations in various fields of research and planning.

Assemblages of breeding birds as indicators of grassland condition