Levels Of Spatial Heterogeneity Research Articles

Effect of curing condition on the piezoresistivity of multi-walled carbon nanotube/epoxy nanocomposites

Non-monotonic electrical resistance changes have been observed in multi-walled carbon nanotube (MWNT)/epoxy nanocomposites subjected to tensile loading, but the mechanism is not fully understood. While most previous works investigated the effect of nanofiller content on the piezoresistivity of MWNT/epoxy nanocomposites, this work evaluates the impact of curing condition. The results indicate that the monotony of the piezoresistivity could be enhanced by using a higher curing temperature. It is therefore proposed that when cured under a different temperature, an epoxy matrix would exhibit a different level of spatial heterogeneity in molecular structure and hence in mechanical properties, causing the MWNT network inside the matrix to undergo a different movement trend under tensile loading. This study will facilitate future research on elucidating the underlying mechanism of the non-monotonic piezoresistivity of CNT/epoxy nanocomposites through considering the contribution of the molecular structure of the polymer matrix.

Fractal Dimension Analysis and Earthquake Repeated Period Estimation in the Southern Part of Sumatra Subduction Zone (Bengkulu-Lampung-Sunda Segment)

DOI:10.17014/ijog.9.3.383-394An earthquake seismicity parameter study has been conducted in the southern part of the Sumatra Subduction Zone (SSZ) for the period of 1919 to 2019 with a hypocentre depth of 120 km and magnitude of ≥4Mw. The researched area is divided into three segments, Bengkulu, Lampung, and Sunda segments, respectively. This study aims to evaluate the seismicity parameters in the form of a-value, b-value, fractal dimension, and probabilities of earthquake recurrence period. Frequency-magnitude correlation statistical approach introduced by Gutenberg-Richter was used to calculate the seismicity parameters based on catalogue data obtained from USGS. An a-value variation of 5.11±1.84 to 5.85±2.46 obtained indicates the most dominant seismic intensity. b-value of 0.69±0,.17 to 0.83±0.35 correlates with high-stress levels, the level of spatial heterogeneity on the SFZ, and the probabilities of bigger earthquakes in the future. The calculation of fractal dimension of 1.4±0.33 to 1.7±0.71 indicates an earthquake distribution pattern was caused by a single fracture zone along the southern part of SSZ. The fracture zone is located on the left and right sides of the southern part of SSZ, which may not have been connected. In addition, the calculation result of earthquake recurrence period with magnitude ≥6.5Mw indicates that there will be an 5 - 6 earthquake event in the Bengkulu segment, 2 - 3 event in the Lampung segment, and 1 - 2 event in the Sunda segment, while the probabilities of an earthquake with magnitude ≥7.5Mw along the southern part of SSZ is 1 - 2 times.

Patterns of exposure to SARS-CoV-2 carriers manifest multiscale association between urban landscape morphology and human activity

The outbreak of the Coronavirus disease 2019 (COVID-19), and the drastic measures taken to mitigate its spread through imposed social distancing, have brought forward the need to better understand the underlying factors controlling spatial distribution of human activities promoting disease transmission. Focusing on results from 17,250 epidemiological investigations performed during early stages of the pandemic outbreak in Israel, we show that the distribution of carriers of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, is spatially correlated with two satellite-derived surface metrics: night light intensity and landscape patchiness, the latter being a measure to the urban landscape’s scale-dependent spatial heterogeneity. We find that exposure to SARS-CoV-2 carriers was significantly more likely to occur in “patchy” parts of the city, where the urban landscape is characterized by high levels of spatial heterogeneity at relatively small, tens of meters scales. We suggest that this spatial association reflects a scale-dependent constraint imposed by the city’s morphology on the cumulative behavior of the people inhabiting it. The presented results shed light on the complex interrelationships between humans and the urban landscape in which they live and interact, and open new avenues for implementation of multi-satellite data in large scale modeling of phenomena centered in urban environments.

Spatial Genomic Heterogeneity in Multiple Myeloma: Surrogate Markers and Its Significance for Understanding the Evolutionary Processes Leading to Relapse

IntroductionMultiple Myeloma (MM) frequently presents with genomic variation between individual cancer foci, which is also termed spatial heterogeneity (SH). However, the extent of SH differs considerably between patients with some having extensive SH while others show homogeneous mutational profiles within the bone marrow (BM). Since SH poses a significant challenge to personalized therapy, surrogate markers for its extent would be useful clinically. In this respect an understanding of the spatial evolutionary patterns taking place during MM treatment and the clonal architecture they give rise to at relapse would also be of fundamental importance. To address these important points we have analyzed paired samples from multiple BM sites collected at baseline (n=52 patients) or in a longitudinal fashion during treatment (n=30).Patients and MethodsA genomic analysis was performed on iliac crest BM and CT-guided fine needle aspirates from distinct anatomical sites from 52 newly diagnosed patients. The impact of treatment on SH was analyzed in 30 patients with a median number of 6 samples per patient (range 4-14). Plasma cells were CD138-enriched and underwent whole exome sequencing using an Illumina HiSeq 2500. Sequencing data were aligned using BWA. Mutations were identified using MuTect. Copy number aberrations were derived from Illumina HumanOmni 2.5 bead chip data using ASCAT. Subclonal reconstruction was performed using SciClone. Statistical analyses were carried out using R packages.ResultsWe investigated whether the extent of SH was associated with clinical features and found that age, an adverse prognostic factor, was positively correlated with SH (P=0.01). Patients over the age of 60 had a 3-fold higher proportion of private mutations compared to younger patients (median 15 vs. 5%). Hypothetically, this could reflect the longer evolutionary time period to MM arising at older ages. The size of the biopsied FLs was also significantly associated with the proportion of private mutations (P <0.001), with large FLs (>2.5 cm) having the highest values. Of note, a linear model including age and FL size explained 49% of the variation in SH. In contrast, neither the ISS, adverse molecular markers identified at the iliac crest, the number of FLs, nor the anatomical distance between investigated sites correlated with SH. In summary, we show that SH is increased in elderly patients and/or patients with large FLs.Next, we investigated the impact of treatment on the spatial clonal architecture. For patients who had achieved a CR, we expected relapse to be dominated by a limited number of selected clones and as a result less SH. Indeed, analyzing samples derived from multiple sites at baseline and at subsequent relapses, the predominant pattern was characterized by sweeps of advanced clones resulting in BM-wide replacement of previous clones. Of note, evidence for clonal sweeps was also seen in patients with stable disease according to IMWG criteria, which is a perplexing observation since stable serological parameters do not seem to be consistent with major sub-clonal changes.In contrast to cases with clonal sweeps, there were also patients with multiple site-specific clones at relapse. On the one hand, “mixed responses” of FLs, which are frequently seen in medical imaging after multiple relapses, could be related to this pattern of pronounced SH. On the other hand, we found private mutations affecting the same genes or pathways in patients with site-specific subclones, indicating parallel evolution. In this context, one patient showed distinct KRAS mutations which affected the exact same amino acid (Gly12Val vs. Gly12Asp), emphasizing that treatment could select for multiple clones with similar or even identical features. This observation may explain, why individual patients still respond to MAPK pathway inhibitors despite pronounced SH.ConclusionsWe provide new insights into the biology of MM progression, which has implications for diagnostic and treatment strategies. The strong association of private mutations with age and FL size may allow for the identification of patients with increased levels of SH for whom treatment approaches can be modified. The existence of clonal sweeps of aggressive clones, which replace previously dominant clones, even in poorly responding patients, highlights the need for regular molecular testing, especially if personalized treatment is intended. DisclosuresBarlogie:Millenium Pharmaceuticals: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Davies:Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Takeda: Consultancy, Honoraria; Bristol Myers: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding.

Backfitting Estimation for Geographically Weighted Regression Models with Spatial Autocorrelation in the Response

Geographically weighted regression model with a spatially autoregressive term of the response variable (GWR‐Lag model for short) is a useful tool to simultaneously model spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. The profile maximum likelihood estimation has been developed to calibrate the model, where a single bandwidth is used for all of the spatially varying coefficients. However, due to different spatial scales at which the explanatory variables might operate, the coefficients may have different levels of spatial heterogeneity and cannot be effectively estimated by a single bandwidth. To deal with such multiscale problem, a backfitting method is proposed to calibrate the GWR‐Lag model, where the optimal bandwidth size is separately selected for each coefficient and the autoregressive parameter is estimated by maximizing the log‐likelihood function. A simulation study with a comparison of the GWR‐based profile maximum likelihood (GWR‐ML) estimation is conducted to assess the performance of the backfitting approach. The empirical results show that the backfitting method not only provides useful scale information for each explanatory variable, but also yields more accurate estimators of the coefficients. Furthermore, a real‐life data set is analyzed to demonstrate the effectiveness of the backfitting approach.

Exploring spatial heterogeneity in accessibility and transit mode choice

Since accessibility is an important goal for sustainable transportation planning, this study analyzes the impact of accessibility on transit mode choice to investigate the potential reasons for the differences in the travel behavior of urban and suburban residents. To attain this purpose, two levels of spatial heterogeneity are considered. The first level is residential spatial heterogeneity, which represents the variations in the impacts of the factors between urban and suburban regions. The second level is the OD spatial heterogeneity, which represents whether the travel is between urban and suburban areas. Taking Xiamen city as a case study and considering the relationships between three OD characteristics (OD transit accessibility, OD travel distance, and OD spatial heterogeneity), this paper uses a joint analysis of the probit model and the structural equation model for multi-group samples. The results indicate that the low transit accessibility in suburban areas is related to lower public transportation use by suburban residents and less inter-regional bus travel by urban residents. The findings provide empirical support for the importance of suburban infrastructure development.

Coupling the Macroscale to the Microscale in a Spatiotemporal Context to Examine Effects of Spatial Diffusion on Disease Transmission.

There are many challenges to coupling the macroscale to the microscale in temporal or spatial contexts. In order to examine effects of an individual movement and spatial control measures on a disease outbreak, we developed a multiscale model and extended the semi-stochastic simulation method by linking individual movements to pathogen’s diffusion, linking the slow dynamics for disease transmission at the population level to the fast dynamics for pathogen shedding/excretion at the individual level. Numerical simulations indicate that during a disease outbreak individuals with the same infection status show the property of clustering and, in particular, individuals’ rapid movements lead to an increase in the average reproduction number R_0, the final size and the peak value of the outbreak. It is interesting that a high level of aggregation the individuals’ movement results in low new infections and a small final size of the infected population. Further, we obtained that either high diffusion rate of the pathogen or frequent environmental clearance lead to a decline in the total number of infected individuals, indicating the need for control measures such as improving air circulation or environmental hygiene. We found that the level of spatial heterogeneity when implementing control greatly affects the control efficacy, and in particular, an uniform isolation strategy leads to low a final size and small peak, compared with local measures, indicating that a large-scale isolation strategy with frequent clearance of the environment is beneficial for disease control.

Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

AbstractNumerical simulation of flow and transport in heterogeneous formations has long been studied, especially for uncertainty quantification and risk assessment. The high computational cost associated with running large‐scale numerical simulations in a Monte Carlo sense has motivated the development of surrogate models, which aim to capture the important input‐output relations of physics‐based models but require only a fraction of the cost of full model runs. In this work, we formulate a conditional deep convolutional generative adversarial network (cDC‐GAN) surrogate model to learn the dynamic functional mappings in multiphase models. The cDC‐GAN belongs to a class of semisupervised learning methods that can be used to learn the data generation processes. Like the original GAN, a main strength of the cDC‐GAN is that it includes a self‐training scheme for improving the quality of generative modeling in a game theoretic framework, without requiring extensive statistical knowledge and assumptions on input data distributions. In particular, our cDC‐GAN model is designed to learn cross‐domain mappings between high‐dimensional input (e.g., permeability) and output (e.g., phase saturations) pairs, with the ability to incorporate conditioning information (e.g., prediction time). As a use case, we demonstrate the performance of cDC‐GAN for predicting the migration of carbon dioxide (CO2) plume in heterogeneous carbon storage reservoirs, which has both numerical and practical significance because of the safe storage requirements now mandated in many countries. Results show that cDC‐GAN achieves high accuracy in predicting the spatial and temporal evolution patterns of the injected CO2 plume, as compared to the original results obtained using a compositional reservoir simulator. The performance of cDC‐GAN models, trained using the same number of training samples, stays relatively robust when the level of spatial heterogeneity is increased. Our cDC‐GAN is pattern based and is not limited by the underlying physics. Thus, it provides a general framework for developing surrogate models, and for conducting uncertainty analyses for a wide range of physics‐based models used in both groundwater and subsurface energy exploration applications.

Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes.

Motivated by a study exploring differences in glycemic control between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes, we aim to address a type of confounding that arises in spatially referenced observational studies. Specifically, we develop a spatial doubly robust propensity score estimator to reduce bias associated with geographic confounding, which occurs when measured or unmeasured confounding factors vary by geographic location, leading to imbalanced group comparisons. We augment the doubly robust estimator with spatial random effects, which are assigned conditionally autoregressive priors to improve inferences by borrowing information across neighboring geographic regions. Through a series of simulations, we show that ignoring spatial variation results in increased absolute bias and mean squared error, while the spatial doubly robust estimator performs well under various levels of spatial heterogeneity and moderate sample sizes. In the motivating application, we construct three global estimates of the risk difference between race groups: an unadjusted estimate, a doubly robust estimate that adjusts only for patient-level information, and a hierarchical spatial doubly robust estimate. Results indicate a gradual reduction in the risk difference at each stage, with the inclusion of spatial random effects providing a 20% reduction compared to an estimate that ignores spatial heterogeneity. Smoothed maps indicate poor glycemic control across Alabama and southern Georgia, areas comprising the so-called "stroke belt." These results suggest the need for community-specific interventions to target diabetes in geographic areas of greatest need.

Multiscale Geographically Weighted Regression (MGWR)

Scale is a fundamental geographic concept, and a substantial literature exists discussing the various roles that scale plays in different geographical contexts. Relatively little work exists, though, that provides a means of measuring the geographic scale over which different processes operate. Here we demonstrate how geographically weighted regression (GWR) can be adapted to provide such measures. GWR explores the potential spatial nonstationarity of relationships and provides a measure of the spatial scale at which processes operate through the determination of an optimal bandwidth. Classical GWR assumes that all of the processes being modeled operate at the same spatial scale, however. The work here relaxes this assumption by allowing different processes to operate at different spatial scales. This is achieved by deriving an optimal bandwidth vector in which each element indicates the spatial scale at which a particular process takes place. This new version of GWR is termed multiscale geographically weighted regression (MGWR), which is similar in intent to Bayesian nonseparable spatially varying coefficients (SVC) models, although potentially providing a more flexible and scalable framework in which to examine multiscale processes. Model calibration and bandwidth vector selection in MGWR are conducted using a back-fitting algorithm. We compare the performance of GWR and MGWR by applying both frameworks to two simulated data sets with known properties and to an empirical data set on Irish famine. Results indicate that MGWR not only is superior in replicating parameter surfaces with different levels of spatial heterogeneity but provides valuable information on the scale at which different processes operate.

The Role of Spatial Statistics in the Control and Elimination of Neglected Tropical Diseases in Sub-Saharan Africa: A Focus on Human African Trypanosomiasis, Schistosomiasis and Lymphatic Filariasis.

Disease control and elimination programmes can benefit greatly from accurate information on the spatial variability of disease risk, particularly when risk is highly spatially heterogeneous. Due to advances in statistical methodology, coupled with the increased availability of geospatial technology, this information is becoming increasingly accessible. In this chapter we describe recent advancements in spatial methods associated with the analysis of disease data measured at the point-leveland demonstrate their application to the control and elimination of neglected tropical diseases (NTDs). We further provide information on spatially referenced data sources and software that can be used to create NTD risk maps, concentrating on those that can be freely obtained. Examples relating to three NTDs affecting populations in sub-Saharan Africa are presented throughout the chapter, i.e., human African trypanosomiasis, schistosomiasis and lymphatic filariasis. These three diseases, with differing routes of transmission, control methods and level of spatial heterogeneity, demonstrate the flexibility and applicability of the methods described.

SAMPLING THE SPATIAL HETEROGENEITY OF THE HONEYCOMB MODEL IN MAIZE AND WHEAT BREEDING TRIAS: ANALYSIS OF SECONDARY DATA COMPARED TO POPULAR CLASSICAL DESIGNS

SUMMARYThe systematic rather than random entry arrangement of honeycomb designs (HDs) has been deployed to sample the spatial heterogeneity. This hypothesis was studied in fairly homogeneous populations, assuming that their phenotypic variability stemmed absolutely from spatial rather than genetic heterogeneity. It was based on single plant performance in two separate trials of a maize hybrid and a wheat cultivar reflecting different level of spatial heterogeneity. In general, the HDs counteracted spatial heterogeneity well, particularly when the number of evaluated entries was limited. There was a suggestion that they do well even in a high number of entries with many replicates per entry. Distribution and layout of spatial heterogeneity across the experimental area did not affect the precision of the HDs. Standardized configuration, which ensures implementation of essential principles met in other experimental models such as blocking, replication and nearest-neighbour (NN) adjustment on the same baseline, renders the honeycomb experimental pattern advantageous over the classical experimental designs like the randomized complete block (RCB), the NN method and the lattice model.

Assessing post-storm forest dynamics in the pyrenees using high-resolution LIDAR data and aerial photographs

We evaluated how historical storm events have shaped the current forest landscape in three Pyrenean subalpine forests (NE Spain). For this purpose we related forest damage estimations obtained from multi-temporal aerial photographic comparisons to the current forest typology generated from airborne LiDAR data, and we examined the role of past natural disturbance on the current spatial distribution of forest structural types. We found six forest structural types in the landscape: early regeneration (T1 and T2), young even-aged stands (T3), uneven-aged stands (T4) and adult stands (T5 and T6). All of the types were related to the timing and severity of past storms, with early-regeneration structures being found in areas markedly affected in recent times, and adult stands predominating in those areas that had suffered lowest damage levels within the study period. In general, landscapes where high or medium levels of damage were recurrent also presented higher levels of spatial heterogeneity, whereas the opposite pattern was found in the less markedly affected landscape, characterized by the presence of large regular patches. Our results show the critical role that storm regimes in terms of timing and severity of past storms can play in shaping current forest structure and future dynamics in subalpine forests. The knowledge gained could be used to help define alternative forest management strategies oriented toward the enhancement of landscape heterogeneity as a measure to face future environmental uncertainty.

Historic and future increase in the global land area affected by monthly heat extremes

Climatic warming of about 0.5 ° C in the global mean since the 1970s has strongly increased the occurrence-probability of heat extremes on monthly to seasonal time scales. For the 21st century, climate models predict more substantial warming. Here we show that the multi-model mean of the CMIP5 (Coupled Model Intercomparison Project) climate models accurately reproduces the evolution over time and spatial patterns of the historically observed increase in monthly heat extremes. For the near-term (i.e., by 2040), the models predict a robust, several-fold increase in the frequency of such heat extremes, irrespective of the emission scenario. However, mitigation can strongly reduce the number of heat extremes by the second half of the 21st century. Unmitigated climate change causes most (>50%) continental regions to move to a new climatic regime with the coldest summer months by the end of the century substantially hotter than the hottest experienced today. We show that the land fraction experiencing extreme heat as a function of global mean temperature follows a simple cumulative distribution function, which depends only on natural variability and the level of spatial heterogeneity in the warming.

Why Is CA3 More Vulnerable Than CA1 in Experimental Models of Controlled Cortical Impact-Induced Brain Injury?

One interesting finding of controlled cortical impact (CCI) experiments is that the CA3 region of the hippocampus, which is positioned further from the impact than the CA1 region, is reported as being more injured. The current literature has suggested a positive correlation between brain tissue stretch and neuronal cell loss. However, it is counterintuitive to assume that CA3 is stretched more during CCI injury. Recent mechanical studies of the brain have reported on a level of spatial heterogeneity not previously appreciated-the finding that CA1 was significantly stiffer than all other regions tested and that CA3 was one of the most compliant. We hypothesized that mechanical heterogeneity of anatomical structures could underlie the proposed heterogeneous mechanical response and hence the pattern of cell death. As such, we developed a three-dimensional finite element (FE) rat brain model representing detailed hippocampal structures and simulated various CCI experiments. Four groups of material properties based on recent experiments were tested. In group 1, hyperelastic material properties were assigned to various hippocampal structures, with CA3 more compliant than CA1. In group 2, linear viscoelastic material properties were assigned to hippocampal structures, with CA3 more compliant than CA1. In group 3, the hippocampus was represented by homogenous linear viscoelastic material properties. In group 4, a homogeneous nonlinear hippocampus was adopted. Simulation results demonstrated that for CCI with a 5-mm diameter, flat shape impactor, CA3 experienced increased tensile strains over a larger area and to a greater magnitude than did CA1 for group 1, which best explained why CA3 is more sensitive to CCI injury. However, for groups 2-4, the total volume with high strain (>30%) in CA3 was smaller than that in CA1. The FE rat brain model, with detailed hippocampal structures presented here, will help to engineer desired experimental neurotrauma models by virtually characterizing brain biomechanics before testing.

南方小花蝽在不同空间及笼罩条件下对西花蓟马的控制作用

在室内研究了5种空间异质条件下南方小花蝽对西花蓟马的捕食作用，并研究了笼罩条件下4种比例释放南方小花蝽对西花蓟马的控制效果。结果表明，不同空间异质下，南方小花蝽对西花蓟马的功能反应均符合Holling Ⅱ圆盘方程；以瞬时攻击率和寻找效应为评价指标，发现南方小花蝽在小容器中的瞬时攻击率（<em>a</em>’）最高，为0.9204，寻找效应最好；在大容器且空间异质最复杂的处理中的瞬时攻击率（<em>a</em>’）最低，为0.6303，寻找效应最差。在笼罩条件下，以3：120和6：120比例释放南方小花蝽和西花蓟马，其控制效果差；以9：120的比例释放，5周后对西花蓟马成虫和若虫的控制效果分别达到74.70%和81.01%；以15：120比例释放，5周后对西花蓟马成虫和若虫的控制效果分别达到95.71%和89.02%。结果显示空间越小或异质性越简单，越利于南方小花蝽捕食西花蓟马。若实际生产中采用9：120的益害比例释放南方小花蝽，既可以达到对西花蓟马较好的控制效果，又相对节约成本。

Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings.

BackgroundMalaria incidence is in decline in many parts of SE Asia leading to a decreasing proportion of febrile illness that is attributable to malaria. However in the absence of rapid, affordable and accurate diagnostic tests, the non-malaria causes of these illnesses cannot be reliably identified. Studies on the aetiology of febrile illness have indicated that the causes are likely to vary by geographical location within countries (i.e. be spatially heterogeneous) and that national empirical treatment policies based on the aetiology measured in a single location could lead to inappropriate treatment.MethodsUsing data from Vientiane as a reference for the incidence of major febrile illnesses in the Lao People's Democratic Republic (Laos) and estimated incidences, plausible incidence in other Lao provinces were generated using a mathematical model for a range of national and local scale variations. For a range of treatment protocols, the mean number of appropriate treatments was predicted and the potential impact of a spatially explicit national empirical treatment protocol assessed.FindingsThe model predicted a negative correlation between number of appropriate treatments and the level of spatial heterogeneity. A spatially explicit national treatment protocol was predicted to increase the number of appropriate treatments by 50% for intermediate levels of spatial heterogeneity.ConclusionsThe results suggest that given even only moderate spatial variation, a spatially explicit treatment algorithm will result in a significant improvement in the outcome of undifferentiated fevers in Laos and other similar resource poor settings.

Distribution of microbial biomass and activity of extracellular enzymes in a hardwood forest soil reflect soil moisture content

Forest soils show a considerable level of spatial heterogeneity. It was previously reported that soil moisture content affects microbial biomass and respiration in the soil as well as enzyme-catalyzed processes; as such it can be one of the potential drivers of heterogeneity of microbial distribution and decomposition in forest soils. The moisture mass content in samples of Quercus petraea forest soil varied substantially and ranged between 0.35–0.70 g g −1 soil wet mass in the litter horizon and 0.10–0.60 g g −1 in the humus horizon within a single sampling of a 144 m 2 area as well as of a 0.11 m 2 area. Soil moisture content positively and significantly correlated with microbial (bacterial and fungal) biomass, explained up to 60% of the total variability in microbial biomass and a significant difference in the ratio of fungal to bacterial biomass was found among the samples with high and low moisture content. Also the activity of several extracellular enzymes involved in decomposition (i.e., laccase, Mn-peroxidase, endo-1,4-β-glucanase, endo-1,4-β-xylanase, cellobiohydrolase, β-glucosidase, β-xylosidase, chitinase and acid phosphatase) correlated with soil moisture, although the response varied among soil horizons and sampling dates. The effect of moisture on enzyme activities was probably partially mediated by the changes in biomass content. Forest soil was demonstrated to be a mosaic of small dry and moist patches where the differences in microbial biomass content or enzyme activities vary by tens of percents due to differential moisture content.

Landscape heterogeneity affects the use of sampling methods: a case study of bird communities in mountains of Central Italy

The point count method (PCM) and time species count (TSC) are thought to be applicable to specific bird assemblages and habitat types and to give different kind of outputs: quantitative (abundances) and qualitative (scores), respectively. In this work, we tested if the faster TSC method can be used instead of the time-consuming PCM. We compared results obtained from the application of both methods to bird assemblages in three different habitat types in mountains of Central Italy with different levels of spatial heterogeneity (oak and beech woods, and open-forest mosaics). The application of both methods to the forest ecosystems revealed the absence of a direct correlation between PCM abundance values and TSC mean scores, highlighting that the latter method did not explain the variation in species abundance. The opposite was true in the mosaic habitats. In the Apennine forest ecosystems, species sampled first in the TSC sampling sessions did not correlate with species abundance values evaluated by means of PCM. The species with the highest PCM values (i.e., the more abundant species) were randomly distributed in all of the TSC 10-min sampling subsessions. Therefore, we rejected the hypothesis that the species sampled first (e.g., in the first 10 min) in TSC are more abundant than those sampled in the next 10 min subsession, and so on. This result differs greatly from that found for non-Mediterranean habitat types that are richer in species number (e.g., tropical forests and savannahs), where scores obtained for single bird species when applying the TSC method correlate well with the species abundance parameters obtained using quantitative methods. In the mountainous complex of the Mediterranean area, the TSC method should only be applied in mosaic habitats, which are richer in species, more spatially heterogeneous, and possess a high γ-diversity at the landscape scale, and thus show a direct correlation between species abundance and TSC scores.

Habitat heterogeneity and fish behavior: Units of heterogeneity as a resource and as a source of information

A review of studies, mainly experimental, on modifications of fish behavior caused by microscale habitat heterogeneity. Elements or units of heterogeneity influence on decision making in fish either as contestable physical resources, or as information cues or signals. Habitat heterogeneity arises from abiotic physical objects, aggregations of prey, and grouping fish. Feeding behavior of fish including food search, choice, and consumption are significantly dependent on the structure of heterogeneity of the habitat, where fish are foraging. Depending on the parameters of heterogeneity, prey characteristics and a predator foraging mode, heterogeneous habitats can either facilitate feeding behavior, or makes it more difficult. Habitat heterogeneity plays significant and, as a rule, positive role providing various refuges for fish hiding from predators. Landmarks help fish to find the shortest route to shelters. If a habitat is rather homogeneous or in a novel habitat, which appears to be homogeneous, shoaling of fish makes surroundings of each individual in the school structured providing fish with a substitute of shelters and landmarks. Recent experimental and field results convincingly demonstrate that the effects of main biotic and abiotic factors can be significantly modified by the structure (level of spatial heterogeneity) of habitats. When a habitat is physically structured, tendencies to disperse and establish individual territories prevail. In uniform, poorly structured habitats, fish tend to gather in schools or shoals and maintain larger aggregations. Food is considered the major contestable resource, but fish often demonstrate interference competition not for food, but for heterogeneous sites in the habitat, where they vigorously fight either for a shelter or just for visually non-uniform area. Visually heterogeneous sites can be used by fish as a template of a future individual territory, where fish can find not only food but also a refuge from predators. Fish use individual territories for much longer period than food patches. Just the presence of either physical refuge or “social refuge” neutralized the inhibiting effect of kairomons and allowed fish to feed more intensively despite the potential danger. We suggest that the decision-making was influenced only by available information of possibility to use a refuge. Habitat complexity is almost always accompanied by visual and other types of heterogeneity. Adaptive significance of fish attraction to the units of heterogeneity is probably related to the fact that under natural situations vital for fish objects are often tightly coupled with heterogeneous sites. Thus, units of habitat heterogeneity can be reliable signals or information cues in uncertain, i.e. changeable and poorly predictable, habitats.