Broad Scale Research Articles

Herbarium data accurately predict the timing and duration of population‐level flowering displays

Forecasting the impacts of changing climate on the phenology of plant populations is essential for anticipating and managing potential ecological disruptions to biotic communities. Herbarium specimens enable assessments of plant phenology across broad spatiotemporal scales. However, specimens are collected opportunistically, and it is unclear whether their collection dates – used as proxies of phenological stages – are closest to the onset, peak, or termination of a phenophase, or whether sampled individuals represent early, average, or late occurrences in their populations. Despite this, no studies have assessed whether these uncertainties limit the utility of herbarium specimens for estimating the onset and termination of a phenophase. Using simulated data mimicking such uncertainties, we evaluated the accuracy with which the onset and termination of population‐level phenological displays (in this case, of flowering) can be predicted from natural‐history collections data (controlling for biases in collector behavior), and how the duration, variability, and responsiveness to climate of the flowering period of a species and temporal collection biases influence model accuracy. Estimates of population‐level onset and termination were highly accurate for a wide range of simulated species' attributes, but accuracy declined among species with longer individual‐level flowering duration and when there were temporal biases in sample collection, as is common among the earliest and latest‐flowering species. The amount of data required to model population‐level phenological displays is not impractical to obtain; model accuracy declined by less than 1 day as sample sizes rose from 300 to 1000 specimens. Our analyses of simulated data indicate that, absent pervasive biases in collection and if the climate conditions that affect phenological timing are correctly identified, specimen data can predict the onset, termination, and duration of a population's flowering period with similar accuracy to estimates of median flowering time that are commonplace in the literature.

Unraveling biogeographical patterns and environmental drivers of soil fungal diversity at the French national scale

Abstract. The fungal kingdom is among the most diversified kingdoms on Earth, with estimations of up to 12 million species. However, it remains poorly understood, with only 150 000 fungal species currently described. Given the major ecological role of fungi in ecosystem functioning, these numbers stress the importance of investigating fungal diversity description across different ecosystem types. Here, we explored the spatial distribution of the soil fungal diversity on a broad geographical scale, using the French Soil Quality Monitoring Network that covers the whole French territory (2171 soils sampled along a systematic grid). Fungal alpha diversity was assessed directly from soil DNA using a meta-barcoding approach by targeting the 18S rDNA gene. The total accumulated fungal diversity across France included 136 219 operational taxonomic units (OTUs), i.e., about 1 % of worldwide soil fungal diversity (based on a maximum diversity estimate of 12 million) for a territory representing only 0.3 % of the terrestrial surface on Earth. Based on this dataset, the first extensive map of fungal alpha diversity was drawn and showed a heterogeneous and spatially structured distribution in large biogeographical patterns of 231 km radius for richness (Hill diversity of order 0) and smaller patterns of 36 km radius for dominant fungi (Hill diversity of order 2). As related to other environmental parameters, the spatial distribution of fungal diversity (Hill numbers based on different orders of diversity) was mainly influenced by local filters such as soil characteristics and land management and also by global filters such as climate conditions with various relative influences. Interestingly, cropped soils exhibited the highest pool of fungal diversity relative to forest and vineyard soils. To complement this, soil fungal OTU network interactions were calculated for the different land uses across France. They varied hugely and showed a loss of 75 % of the complexity in crop systems and grasslands compared to forests and up to 83 % in vineyard systems. Overall, our study revealed that a nationwide survey with a high spatial-resolution approach is relevant for deeply investigating the spatial distribution and determinism of soil fungal diversity. Our findings provide novel insights for a better understanding of soil fungal ecology across the 18S rDNA gene and upgrade biodiversity conservation policies by supplying representative repositories dedicated to soil fungi.

Tensions Between Local Embeddedness and Scaling up: Insights from Grassroots Sustainability Initiatives in the Renewable Energy Transition

Although grassroots initiatives in the renewable energy transition are flourishing, their embeddedness in local contexts challenges their capacity to spread their impact on a broader scale. Certainly, while scaling up has been described as difficult to combine with local embeddedness, little is known on the specific nature of the tensions involved in combining the two. Studying a federation of citizen renewable energy (RE) cooperatives in the south of France, we show that the engagement in a scaling-up process at a regional level generates three main kinds of tensions associated with specific dimensions of local embeddedness: natural, cultural, and political. We emphasize how these dimensions are likely to be threatened when the federation engages the cooperatives in a rapid scaling-up dynamic in which the drive to industrialize projects and find funding is dominant. We acknowledge the effects of these tensions on grassroots sustainability initiatives and collective organizing processes.

Unraveling microbial community structure–function relationships in the horizontal and vertical spatial dimensions in extreme environments

A fundamental challenge in soil macroecology is to understand how microbial community structure shapes ecosystem function along environmental gradients of the land surface at broad spatial scales (i.e. the horizontal dimension). However, little is known about microbial community structure–function relationships in extreme environments along environmental gradients of soil depth at finer spatial scales (i.e. the vertical dimension). Here, we propose a general spatial dimension partitioning approach for assessing the patterns and drivers of soil microbial community structure–function relationships across horizontal and vertical spatial gradients simultaneously. We leveraged a 200‐km desert soil salinity gradient created by a 12‐year saline‐water irrigation in the Tarim basin of Taklamakan Desert. Specifically, using a general linear model, hierarchical variance partitioning, and a path model, we assessed the patterns and key ecological processes controlling spatial turnover in microbial community structure (i.e. β‐diversity) and enzymatic activity relevant to carbon, nitrogen, and phosphorus cycling along soil salinity gradients across study sites (horizontal dimension) and soil depths (vertical dimension). We found a decoupled relationship between soil microbial β‐diversity and enzymatic activity. Differences in soil depth (on the scale of meters) were as important as geographic distance (on the scale of kilometers) in shaping bacterial and fungal β‐diversity. However, the vertical and horizontal turnover in enzymatic activity was largely attributed to an increase in the heterogeneity of soil properties, such as soil texture, water content, and pH. Our findings suggest that dispersal limitation controls microbial community β‐diversity and that environmental heterogeneity, rather than soil salinization, controls enzymatic activity. Taken together, this work highlights that in the face of ongoing environmental alterations, soil depth is an under‐explored spatial dimension that must be considered in soil conservation efforts as a critical factor in determining microbial community structure and function in extreme environments.

Application of Electrical Resistivity Tomography in Geotechnical and Geoenvironmental Engineering Aspect

Electrical resistivity tomography (ERT) has turned out to be one of the most applied and user-friendly geophysical methods in geotechnical and geoenvironmental research. ERT is an emerging technology that is becoming popular nowadays for investigating subsurface conditions. Multiple attributes of the technology using various electrode configurations significantly reduce measurement time and are suitable for applications even in hardly accessible mountain areas. It is a noninvasive test for subsurface characterization and a very sensitive method used to determine geophysical properties, i.e., structural integrity, water content, fluid composition, etc. This paper aimed to elucidate the ERT technique’s main features and applications in geotechnical and geoenvironmental engineering through four case studies. The first case study investigated the possible flow paths and areas of moisture accumulation after leachate recirculation in a bioreactor landfill. The second case study attempted to determine the moisture variation along highway pavement. The third case study explored the slope failure investigation by ERT. The fourth case study demonstrated the efficiency of the ERT method in the landfill evapotranspiration (ET) cover to investigate moisture variation on a broader scale and performance monitoring. In all of the four cases, ERT exhibited promising performance.

The ASAM.ID Application as a Learning Media for Environmental Literacy

Garbage is a crucial problem that must be resolved by all parties, including universities. Universities can contribute to efforts to increase community literacy in waste management through the development of learning applications. This paper aimed to describe the ASAM.ID application development process and its feasibility to be used as an alternative learning media in increasing students’ environmental literacy in waste management. The application development process began with making flowcharts and developing application designs and content. The validation results of content and media experts showed that the ASAM.ID application can be used as an alternative learning media to increase students’ environmental literacy. This study recommends this application be tested in science learning to see its effectiveness and implemented on a broader scale.
 Keywords: ASAM.ID Application, Learning Media, Environmental Literacy

Invasion by common reed in peri-urban wetlands does not affect insectivorous bat activity, despite a negative effect on prey abundance

Common reed (Phragmites australis), an exotic invasive plant in North America, alters wetlands and thus affects the abundance, development and behavior of many animal species. We evaluated the effect of common reed invasion on 1) the abundance of nocturnal flying insects, and 2) the activity of one of their predators, insectivorous bats. As bats are highly mobile, they can be affected by parameters at a broad scale. Therefore, we considered 3) the effect of habitats and roads around invaded and non-invaded sites on bat activity. We selected 28 paired sites located in wetlands in southwestern Quebec: 14 control sites and 14 common reed-dominated sites. Nocturnal flying insects were collected in some sites, using light traps. The activity of bats was measured in all sites using acoustic detectors. Habitats and high-traffic roads around the sampled sites were characterized over several spatial scales, using ArcGISPro. Our results showed that the number of nocturnal flying insects was reduced by 53% in common reed-dominated sites, but there was no influence on the overall activity of insectivorous bats. Instead, a larger relative area of aquatic and woodland habitats, within a respective radius of 1 and 2km in the vicinity of our study sites, favoured the overall activity of bats. Moreover, bat activity decreased with an increase in the length of high-traffic roads within a radius of 200m around the studied wetland sites. Our results suggest that invasive plants despite reducing insect prey may not represent a major threat to insectivorous bats. They also highlight the importance of protecting forests and aquatic habitats, as well as the need to implement road mitigation measures for the conservation of insectivorous bats.

Fabrication and characterization of binary composite MgO/CuO nanostructures for the efficient photocatalytic ability to eliminate organic contaminants: A detailed spectroscopic analysis

Design and eco-friendly fabrication of affordable and sustainable materials for the treatment of wastewater consisting of dyes, antibiotics, and other harmful substances has always been demanding. Untreated wastewater being released from industries imposes serious threats to our ecosystem, seeking convenient approaches to diminish this alarming issue. Here in this work, we synthesized MgO/CuO nanocomposites from a plant extract of Ammi visnaga L. and then employed these nanocomposites for the treatment of organic dye (methylene blue). We characterized the synthesized nanocomposites by dynamic light scattering (DLS), zeta potential, scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and X-ray photoelectron microscopy (XPS). DLS presented information about the explicit size of nanocomposites, while the surface charge was examined by zeta potential. XRD provided detailed information about the crystalline behavior and the information regarding surface morphology and size was extracted by SEM, TEM, and AFM. Moreover, the fabricated nanocomposites were used as a photocatalyst in the treatment of methylene blue. The overall catalytic reaction took an hour to complete, and the value of percentage degradation was 98 %. Substantially, a detailed account of the kinetics, rate of reaction, and mechanism is also fostered in the context. The presented study can assist scientists and researchers around the world to reproduce the results and use them to apply them on a broader scale.

Illuminating patterns of firefly abundance using citizen science data and machine learning models

As insect populations decline in many regions, conservation biologists are increasingly tasked with identifying factors that threaten insect species and developing effective strategies for their conservation. One insect group of global conservation concern are fireflies (Coleoptera: Lampyridae). Although quantitative data on firefly populations are lacking for most species, anecdotal reports suggest that some firefly populations have declined in recent decades. Researchers have hypothesized that North American firefly populations are most threatened by habitat loss, pesticide use, and light pollution, but the importance of these factors in shaping firefly populations has not been rigorously examined at broad spatial scales. Using data from >24,000 surveys (spanning 2008–16) from the citizen science program Firefly Watch, we trained machine learning models to evaluate the relative importance of a variety of factors on bioluminescent firefly populations: pesticides, artificial lights at night, land cover, soil/topography, short-term weather, and long-term climate. Our analyses revealed that firefly abundance was driven by complex interactions among soil conditions (e.g., percent sand composition), climate/weather (e.g., growing degree days), and land cover characteristics (e.g., percent agriculture and impervious cover). Given the significant impact that climactic and weather conditions have on firefly abundance, there is a strong likelihood that firefly populations will be influenced by climate change, with some regions becoming higher quality and supporting larger firefly populations, and others potentially losing populations altogether. Collectively, our results support hypotheses related to factors threatening firefly populations, especially habitat loss, and suggest that climate change may pose a greater threat than appreciated in previous assessments. Thus, future conservation of North American firefly populations will depend upon 1) consistent and continued monitoring of populations via programs like Firefly Watch, 2) efforts to mitigate the impacts of climate change, and 3) insect-friendly conservation practices.

Geographic drivers more important than landscape composition in predicting bee beta diversity and community structure

AbstractThe importance of microhabitat traits such as floral availability is well known; however, forest bee spatial dynamics have been variably studied across local to broad geographic scales. Past literature suggests that landscape factors from proximate to distal are important in determining forest bee community metrics, including richness, abundance, and taxonomic composition. Leveraging the interest and assistance of citizen science volunteers, we employed standard bee bowl trap transects across Maryland, Delaware, northern Virginia, and the District of Columbia and identified correlations between bee community composition, local and regional land cover, and broader geospatial patterns. We also identified the partial contributions of both specific species and sampling sites to total beta diversity. Various land cover metrics were significantly related to bee community structure, with bee abundance positively and negatively correlated with forest and wetland cover, respectively. In general, land cover metrics within 1000‐m buffer exhibited stronger correlations with bee communities; however, broader geographic variation, using Cartesian coordinates north and east as indices, was most significantly correlated with the bee community. Specifically, bee communities were less rich in the east and south of the study area. We also identified similar correlations with the bee community as categorized by both trophic and nesting behaviors, with both geographic northing and easting proving to be most strongly correlated with the forest bee community. Results of beta diversity and cluster analyses showed that the most species‐depauperate sites exhibited the highest contributions to beta diversity and that species‐poor sites consisted of a reduced subset of the greater community. Our results show that successful bee conservation must consider, beyond local‐scale resource availability, broad geospatial considerations and forest habitat connectivity across political and administrative boundaries.

Assessment of Climate Change Impact on Reservoir Inflow

Abstract: Evaluating the effects of climate change is crucial in regions grappling with water scarcity. The Ujjani dam, a significant structure in Maharashtra, India, erected on the Bhima river in 1980, serves as a vital water source for the agricultural lands downstream in Solapur and Pune districts. Assessing climate change's impact involves analysing data from General Circulation Models (GCMs), which project climate parameters under various emission scenarios but often at a broader scale. Since hydrological models necessitate finer-scale climate data, a downscaling technique is employed to derive localized variables from GCM outputs. This study employs Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques for downscaling. By utilizing projected temperature and rainfall data, three distinct models are developed to predict reservoir inflow: multiple linear regression, artificial neural network, and wavelet neural network. The findings indicate significant alterations in rainfall distribution patterns, with a decline during the monsoon season and an increase post-monsoon

Precipitation change, functional traits and stand structure jointly control the spatiotemporal variability of radial growth in alpine treeline ecotones across northern China

Tree growth and survival at alpine (arctic) treelines are generally limited by temperature, but it is increasingly observed that hotter drought is threatening tree growth at treelines. How treeline growth variability in response to climate change varies at broad scale, and how it is jointly modulated by functional traits, stand structure and climate factors remains unclear. We sampled tree-ring cores from 11 treeline sites along longitude (precipitation) gradients across northern China, and examined two new aspects of radial growth response to climate change: temporal growth variability and spatial growth synchrony. We explored how climate conditions, climate trends and temporal variability, functional traits [specific leaf area (SLA), leaf nitrogen concentration (LN)], and stand factors affect growth variability and synchrony, and assessed the major drivers with mixed-effects models. 1) Contrary to our expectation, growth variability decreased from humid to arid regions. This pattern was partly because water availability increased in the west (arid) while decreased in the east (humid) regions since 1950. 2) Hotter drought and higher climate variability led to higher growth variability and synchrony. 3) Species with fast-growing strategies (higher SLA and LN) showed higher growth variability and synchrony due to experiencing drastic hotter drought and having lower drought tolerance. 4) Taller and denser stands revealed higher growth variability and synchrony due to stronger hydraulic limitation and competition, respectively. 5) Functional traits and stand factors are indispensable modulators of growth spatiotemporal variability even at broad scales, because their variable importance was often higher than climate factors. These results caution against predicting forest vulnerability solely from climate factors. We conclude that forests at many treeline sites were threatened by hotter drought, especially in humid regions of northern China. But the importance of stand factors and functional traits found also suggests forest management as an effective way to alleviate climate change vulnerability.

Variable importance and scale of influence across individual scottish wildcat hybrid habitat models

Understanding the scale dependence of species-habitat relationships is an important area of research in species distribution modeling. There has been little research focused on how habitat selection may depend on individual variation among organisms, geographical location and ecological context of that location. Furthermore, little is known about the extent and drivers of heterogeneity of scale dependence among individuals of a species inhabiting different ecological contexts, and few studies have compared scale dependence and variable importance in a spatially replicated framework. Two of the most important factors for interpreting habitat relationships models include: (1) the relative importance of variables in the model and (2) the spatial scale at which each variable has the largest influence. Based on the existing evidence we hypothesize a priori that landcover variables will generally be the most important predictors, followed by topography, then soil type (which influence both vegetation and prey), Normalized Difference Vegetation Index (NDVI) as an indicator of total vegetation density and perhaps a proxy for prey density, vegetation cover and rabbit abundance. We also expected a priori that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. We expected topographical features to be selected at broad scales, as they influence broad-scale climatic and ecological conditions. We also expected that land cover classes and vegetation cover density to be selected at relatively broad scales given past research showing land cover generally influences habitat selection at relatively broad scales. We expected NDVI and soil type to be selected at finer scales, as their variation influences the distribution of resources and limiting conditions within landscapes. Finally, we expected that rabbit abundance and linear features would affect wildcat hybrid occurrence at the finest scales, given these are resources and conditions that vary over short distances and strongly influence wildcat and wildcat hybrid behavior and habitat use. Our results were consistent with the hypothesis that there may be consistency regarding which variables or variable groups are most important as predictors of wildcat hybrid occurrence in Scotland. Based on previous research we expected that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. Finally, our results identify a clear and consistent trend of increasing frequency of inclusion of variables at increasingly broad scales. This is a linear trend in frequency of variables retained increasing as the scale increased. This suggests a consistent and monotonic pattern of more frequent retention of variables at increasingly broad scales.

Longitudinal Sequencing and Variant Detection of SARS-CoV-2 across Southern California Wastewater

Wastewater-based epidemiology (WBE) is useful for detecting pathogen prevalence and may serve to effectively monitor diseases across broad scales. WBE has been used throughout the COVID-19 pandemic to track disease burden through quantifying SARS-CoV-2 RNA present in wastewater. Aside from case load estimation, WBE is being used to assay viral genomic diversity and emerging potential SARS-CoV-2 variants. Here, we present a study in which we sequenced RNA extracted from sewage influent obtained from eight wastewater treatment plants representing 16 million people in Southern California from April 2020 to August 2021. We sequenced SARS-CoV-2 with two methods: Illumina Respiratory Virus-Enriched metatranscriptomic sequencing (N = 269), and QIAseq SARS-CoV-2-tiled amplicon sequencing (N = 95). We classified SARS-CoV-2 reads into lineages and sublineages that approximated named variants and identified single nucleotide variants (SNVs), of which many are putatively novel SNVs and SNVs of unknown potential function and prevalence. Through our retrospective study, we also show that several SARS-CoV-2 sublineages were detected in wastewater before clinical detection, which may assist in the prediction of future variants of concern. Lastly, we show that sublineage diversity was similar across Southern California and that diversity changed over time, indicating that WBE is effective across megaregions. As the COVID-19 pandemic moves to new phases, and SARS-CoV-2 variants emerge, monitoring wastewater is important to understand local- and population-level dynamics of the virus. These results will aid in our ability to monitor the evolutionary potential of SARS-CoV-2 and help understand circulating SNVs to further combat COVID-19.

The impact of glacial meltwater on the integrated bioenergetic condition of two key antartic benthic polychaetes (Maldane sarsi antarctica, Notomastus latericeus)

In coastal Antarctic environments, glacial meltwater changes the nutrients and physicochemical parameters in the water column. Consequently, top-down cascading effects are triggered throughout the food web, which can affect the bioenergetic condition of benthic invertebrates and their coupling processes between energy levels and flows throughout the marine food web. In this study, two benthic polychaetes (Maldane sarsi antarctica and Notomastus latericeus), exposed to the impact of glacial melting over a broad time scale, were used to evaluate the effect of glacial meltwater on their bioenergetic condition through the integrated analysis of: i) their biochemical composition; ii) fatty acid profiles and iii) total energy contents. The findings indicate that glacial meltwater has a direct effect on the bioenergetic condition of polychaetes. In areas where glacial meltwater has a significant impact, N. latericeus showed higher levels of proteins and glucose, but lower levels of lipids. On the other hand, M. sarsi antarctica exhibited decreased protein content with increasing glacial meltwater impact. M. sarsi antarctica presented varying levels of lipids across different sites, with the highest concentrations observed in areas with moderate impact. Both species showed a reduction in fatty acids with increasing glacial meltwater impact. Additionally, M. sarsi antarctica individuals from highly impacted areas had lower energy levels than those from less impacted areas, while N. latericeus had higher energy levels in the most impacted site. This information enables the development of a framework for assessing the impact of climate change using glacial meltwater and the integrated bioenergetics of key benthic polychaeta as a proxy. Therefore, identifying how glacial meltwater affects their bioenergetic condition helps us understand how climate change could affect trophic interactions, structure, and energy flows in the Antarctic marine food web.

Impedance Response and Phase Angle Determination of Metal-Semiconductor Structure with N-Doped Diamond Like Carbon Interlayer

With their superior properties over p-n barriers, Schottky Barrier Diodes have a wide usage area, especially as a test tool to produce better-performance devices. The main performance parameter of these devices is measured by their conduction, which can develop with an interlayer addition through the sandwich design. Regarding the DLC, which also has outstanding specifications under thermal, chemical, and physical conditions, is a good candidate for interlayer tailoring, specifically when used with doping atoms. Thus, this study investigates the impedance response of the fabricated device with an N-doped DLC interlayer by employing the electrochemical technique as a combination of electrolysis, RF magnetron sputtering, and thermal evaporation. The measurements were conducted for broad scales of voltage and frequency corresponding between (-3V) and (+4V) and 1kHz and 1MHz, respectively. According to the impedance analysis, complex impedance decreases by rising bias and frequency, from 1.8 MΩ to 2 k Ω at 1MHZ due to the additional insulating layer. At the same time, the phase angle indicates the quality of the dielectric layer with an average of 81.36  for the sample logarithmic frequency values with an almost constant-like trend in the inversion stage. In comparison, it reduces to an average of 30.25  after the depletion stage by showing the rising conductivity. Moreover, it has some unexpected rising values at the strong accumulation stage, possibly due to the deposited thin film's unique structure. The supported results by phase angle changes, showing frequency-adjustable working conditions, may offer that selective electrical conduction can be tuned.

Simulation of neutron dark-field data for grating-based interferometers

Hierarchical structures and heterogeneous materials are found in many natural and engineered systems including additive manufacturing, alternative energy, biology and polymer science. Though the structure–function relationship is important for developing more advanced materials, structural characterization over broad length scales often requires multiple complementary measurements. Neutron far-field interferometry aims to enable multi-scale characterization by combining the best of neutron imaging with small-angle neutron scattering (SANS) via dark-field imaging. The microstructure, nominally from 1 nm to 10 µm, is averaged over each volume element ∼(50 µm)3 in the sample, resulting in a `tomographic SANS' measurement. Unlike in small-angle scattering, there are few analytical models to fit dark-field imaging data to extract properties of the microstructure. Fortunately, the dark field and SANS are related through a single Hankel transform. In this work, we discuss the development of a Python-based library, correlogram-tools, that makes use of existing small-angle scattering models and a numerical implementation of the Hankel transform to simulate dark-field interferometry data. We demonstrate how this software can be used to inform researchers of viable sample sets for interferometry experiments, analyze interferometry data, and simulate raw and reconstructed interferometry images for the training of more advanced segmentation models and analysis protocols.

Dynamic height growth models for highly productive pedunculate oak (Quercus robur L.) stands: explicit mapping of site index classification in Serbia

Key messageWe applied the generalized algebraic difference approach (GADA) to develop dynamic models of height growth for pedunculate oak (Quercus robur L.) in Serbia. According to the dominant heights, the studied region comprises some of Europe’s most productive sites for pedunculate oak. Therein, we have generated a map showing the current site index class of stands. Such a map could be used to enhance forest management and evaluate climate change impacts.ContextAlthough sustainable forest management requires reliable prediction of forest site productivity, such indicators are currently unavailable for pedunculate oak sites in Serbia. The site index (SI) curves represent the most commonly used indirect scale for site productivity classification. The dynamic equations derived by the Generalized Algebraic Difference Approach (GADA) are the state-of-the-art approach in growth modeling, but they have not been widely applied for studying the height dynamics of pedunculate oak.AimsThe main objectives of this study were to develop the first dynamic site index curves for pedunculate oak in Serbia and subsequently to provide stand-level maps with predicted site indices.MethodsWe have tested five flexible polymorphic equations with variable asymptotes derived by the GADA approach. Models were calibrated using artificially established growth trajectories obtained from 3636 detailed temporary sample plots. The selection of the most suitable model was accomplished according to (1) quantitative measures of goodness of fit, (2) the analysis of residual scattering, and (3) the biological plausibility of obtained height growth curves.ResultsAfter correcting the error terms with a continuous first-order autoregressive structure and conducting a three-stage performance analysis, the GADA dynamic site index model derived from the Hossfeld base equation shows the best overall properties. Insight into the oscillations of relative error suggested that 100 years is the most suitable age for site index referencing. Comparison with existing height growth models revealed greater flexibility and a considerably better representation of the height growth dynamic of pedunculate oak in the studied region. Additionally, we have produced a spatially explicit map showing the expected SI100 for 1907 stands with pedunculate oak within 22 management units.ConclusionDynamic SI-curves based on GADA will serve forest practitioners to update management plans and serve as a reference point for benchmarking the impact of climate change and for developing adaptation strategies. The utilized approach allowed unbiased estimation of SI100 across all age classes so that the results could be mapped at a broader scale. This study provides the second known application of the dynamic model for pedunculate oak in Europe but the first that includes some of the most productive sites in the species distribution range.

Using a Handful of Transcriptomes to Detect Sex-Linked Markers and Develop Molecular Sexing Assays in a Species with Homomorphic Sex Chromosomes.

To understand the biology of a species, it is often crucial to be able to differentiate males and females. However, many species lack easily identifiable sexually dimorphic traits. In those that possess sex chromosomes, molecular sexing offers a good alternative, and molecular sexing assays can be developed through the comparison of male and female genomic sequences. However, in many nonmodel species, sex chromosomes are poorly differentiated, and identifying sex-linked sequences and developing sexing assays can be challenging. In this study, we highlight a simple transcriptome-based procedure for the detection of sex-linked markers suitable for the development of sexing assays that circumvents limitations of more commonly used approaches. We apply it to the spotted snow skink Carinascincus ocellatus, a viviparous lizard with homomorphic XY chromosomes that has environmentally induced sex reversal. With transcriptomes from three males and three females alone, we identify thousands of putative Y-linked sequences. We confirm linkage through alignment of assembled transcripts to a distantly related lizard genome and readily design multiple single locus polymerase chain reaction primers to sex C. ocellatus and related species. Our approach also facilitates valuable comparisons of sex determining systems on a broad taxonomic scale.

A marine heatwave changes the stabilizing effects of biodiversity in kelp forests.

Biodiversity can stabilize ecological communities through biological insurance, but climate and other environmental changes may disrupt this process via simultaneous ecosystem destabilization and biodiversity loss. While changes to diversity-stability relationships (DSRs) and the underlying mechanisms have been extensively explored in terrestrial plant communities, this topic remains largely unexplored in benthic marine ecosystems that comprise diverse assemblages of producers and consumers. By analyzing two decades of kelp forest biodiversity survey data, we discovered changes in diversity, stability, and their relationships at multiple scales (biological organizational levels, spatial scales, and functional groups) that were linked with the most severe marine heatwave ever documented in the North Pacific Ocean. Moreover, changes in the strength of DSRs during/after the heatwave were more apparent among functional groups than both biological organizational levels (population vs. ecosystem levels) and spatial scales (local vs. broad scales). Specifically, the strength of DSRs decreased for fishes, increased for mobile invertebrates and understory algae, and were unchanged for sessile invertebrates during/after the heatwave. Our findings suggest that biodiversity plays a key role in stabilizing marine ecosystems, but the resilience of DSRs to adverse climate impacts primarily depends on the functional identities of ecological communities.