Molecular composition and characteristics of Sediment-adsorbed Dissolved Organic Matter (SDOM) along the coast of China

Sediment-adsorbed Dissolved Organic Matter (SDOM) in coast plays a crucial role in the terrestrial and marine carbon cycle processes of the global environment. However, understanding the transport dynamics of SDOM along the coast of China, particularly its interactions with sediments, remains elusive. In this study, we analyzed the δ13C and δ15N stable isotopic compositions, as well as the molecular characteristics of SDOM collected from coastal areas spanning the Bohai Sea (BS), Yellow Sea (YS), East China Sea (ECS), and South China Sea (SCS), by using isotope ratio mass spectrometry and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS). We identified the predominant sources of carbon and nitrogen in coastal sediments, revealing terrigenous origins for most C and N, while anthropogenic sources dominated in the SCS. Spatial variations in SDOM chemodiversity were observed, with diverse molecular components influenced by distinct environmental factors and sediment sources. Notably, lignins and saturated compounds (such as proteins/amino sugars) were the predominant molecular compounds detected in coastal SDOM. Through Mantel tests and Spearman's correlation analysis, we elucidated the significant influence of spatial environmental factors (temperature, DO, salinity, and depth) and sediment sources on SDOM molecular chemodiversity. These findings contribute to a more comprehensive understanding of the carbon cycle dynamics along the Chinese coast.

Benchmarking the Mantel test and derived methods for testing association between distance matrices

Testing the association between objects is central in ecology, evolution, and quantitative sciences in general. Two types of variables can describe the relationships between objects: point variables (measured on individual objects), and distance variables (measured between pairs of objects). The Mantel test and derived methods have been extensively used for distance variables. Yet, these methods have been criticized due to low statistical power and inflated type I error when spatial autocorrelation is present. Here, we assessed the statistical power between different types of tested variables and the type I error rate over a wider range of autocorrelation intensities than previously assessed, both on univariate and multivariate data. We also illustrated the performance of distance matrix statistics through computational simulations of genetic diversity. We show that the Mantel test and derived methods are not affected by inflated type I error when spatial autocorrelation affects only one variable when investigating correlations, or when either the response or the explanatory variable(s) is affected by spatial autocorrelation while investigating causal relationships. As previously noted, with autocorrelation affecting more variables, inflated type I error could be reduced by modifying the significance threshold. Additionally, the Mantel test has no problem of statistical power when the hypothesis is formulated in terms of distance variables. We highlight that transformation of variable types should be avoided because of the potential information loss and modification of the tested hypothesis. We propose a set of guidelines to help choose the appropriate method according to the type of variables and defined hypothesis.

Heavy metal concentration according to shrimp species and organ specificity: Monitoring and human risk assessment

This study assessed heavy metal levels (lead (Pb), cadmium (Cd), total arsenic (tAs), arsenite (As (III)), arsenate (As (V)), monomethyl arsenic acid (MMA), dimethylarsinic acid (DMA), total mercury (tHg), and methylmercury (MeHg)) in six organs (total portion, head, body, shell, muscle, and intestine) of 11 shrimp species distributed in Korea. Shrimp exhibited significant variability in heavy metal accumulation, with Alaskan pink and dried shrimp (Lesser glass, Southern rough, and Chinese ditch prawn) showing the highest metal concentrations. Notably, the intestine having the highest overall metal content, while Cd was most prominent in the head, tHg was highest in the muscle. The Hazard Quotient values of 11 shrimp species in South Korea were below the European Food Safety Authority's allowable limits for heavy metals. This study illuminates the heavy metal profiles of distributed shrimp in Korea and emphasizes the ongoing need for monitoring heavy metals on seafood to ensure consumer safety.

Interkingdom network analyses reveal microalgae and protostomes as keystone taxa involved in nutrient cycling in large freshwater lake sediment

Few studies have explored the role of interkingdom interactions between bacteria and microeukaryotes in nutrient cycling in lake ecosystems. We conducted sediment sampling from 40 locations covering Hongze Lake and analyzed their chemical properties. Intra- and interkingdom networks were constructed using 16S and 18S rRNA gene amplicon sequencing. Microeukaryotic intranetworks were more complex in spring than in autumn, while no clear variation in the complexity of bacterial intranetworks was found between autumn and spring. Larger and more complex bacterial-microeukaryotic bipartite networks emerged in spring than in autumn, correlated with lower carbon, nitrogen, and phosphorus levels in spring, likely resulting in intense microbial competition. Bacteria and microeukaryotes played different topological roles in interkingdom networks, with microeukaryotes contributing to the networks' greater complexity. Seven keystone modules were identified in spring and autumn nutrient cycling. Importantly, keystone taxa in these modules belonged to photoautotrophic microalgae or predatory protostomes, indicating that these organisms are key drivers in lake sediment nutrient cycling. Our results suggested that nutrient content variation in autumn and spring changes interkingdom networks' topological structure between bacteria and microeukaryotes. Microalgae and protostomes are essential in freshwater lake nutrient cycling and may be targeted to modulate nutrient cycling in large freshwater ecosystems.

Nutrient runoff loss from saline-alkali paddy fields in Songnen Plain of Northeast China via different runoff pathways: effects of nitrogen fertilizer types

The application of nitrogen (N) fertilizer aggravates the nutrient runoff loss from paddy, causing serious agricultural non-point source pollution, and leading to a serious decline in water quality. The global area of saline-alkali paddy has expanded, but the response of nutrient loss via runoff to N-fertilizer applications in saline-alkali paddy is still unclear. This study conducted a 147-day field experiment to evaluate the nutrient runoff loss from saline-alkali paddy with different N-fertilizer application strategies in Songnen Plain of Northeast China. Regardless of N-fertilizer types, the nutrient loss via rainfall runoff in the entire rice-growing season was significantly (p < 0.05) higher than that via artificial drainage. The N and phosphorus (P) concentrations in runoff water were correlated with salinity and alkalinity. Especially, pH had a significant positive correlation with total-P (TP) (r = 0.658, p < 0.01). In the entire rice-growing season, the TN runoff losses in urea (U), microbial fertilizer (MF), and inorganic compound fertilizer (ICF) treatments were significantly (p < 0.05) lower compared with carbon-based slow-release fertilizer (CSF) and organic-inorganic compound fertilizer (OCF), respectively. Meanwhile, the TP runoff losses in OCF and ICF treatments were significantly (p < 0.05) lower than U and MF, respectively. Overall, the application of ICF is a better choice to avoid N and P losses via runoff from saline-alkali paddy fields.

A Comparison of Semilandmarking Approaches in the Analysis of Size and Shape

Often, few landmarks can be reliably identified in analyses of form variation and covariation. Thus, ‘semilandmarking’ algorithms have increasingly been applied to surfaces and curves. However, the locations of semilandmarks depend on the investigator’s choice of algorithm and their density. In consequence, to the extent that different semilandmarking approaches and densities result in different locations of semilandmarks, they can be expected to yield different results concerning patterns of variation and co-variation. The extent of such differences due to methodology is, as yet, unclear and often ignored. In this study, the performance of three landmark-driven semilandmarking approaches is assessed, using two different surface mesh datasets (ape crania and human heads) with different degrees of variation and complexity, by comparing the results of morphometric analyses. These approaches produce different semilandmark locations, which, in turn, lead to differences in statistical results, although the non-rigid semilandmarking approaches are consistent. Morphometric analyses using semilandmarks must be interpreted with due caution, recognising that error is inevitable and that results are approximations. Further work is needed to investigate the effects of using different landmark and semilandmark templates and to understand the limitations and advantages of different semilandmarking approaches.

Putting the Mantel test back together again

The Mantel test has been widely used in ecology and evolution, but over the last two decades it has been frequently critiqued because results were inconsistent with expectations and there were issues with Type I (false-positive) and Type II (false-negative) error rates. Three-matrix extensions of the Mantel test have been challenged for similar reasons. Even the null hypotheses underlying the Mantel test have been questioned. As a result, use of the Mantel test and its variants has been discouraged or limited to special situations. Here, we examine Mantel test criticisms including the lack of agreement between traditional variable-based Pearson correlations (r) and observation-based Mantel correlations (r_m ), and the unusual Type I and Type II error rates. We propose an alternate proximity measure that resolves these issues. We use simulations and examples to contrast Mantel results based on Euclidean distance, squared Euclidean distance, and the simple difference (Diff) with traditional bivariate Pearson correlations. We demonstrate that use of the simple difference in Mantel tests can resolve the underlying problems with poor agreement between bivariate Pearson and Mantel correlations, as well as appropriate Type I and Type II errors (i.e., where r = cor(x,y) and r_m  = cor(d_x ,d_y ), if d_x  = Diff(x) and d_y  = Diff(y), r = r_m ). We also show that the simple difference can provide solutions to issues with partial Mantel tests and distance-based MANOVA. Because our results resolve many of the issues with Mantel tests, we hope that these findings will restore the popularity of the Mantel test.

Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method

Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power consumption, respectively and discusses the influence of weather parameters and schedule parameters on the prediction accuracy. The results demonstrate that using the LSTM algorithm to accurately predict the electricity consumption of air conditioners is more challenging than predicting lighting electricity consumption. To improve the prediction accuracy of air conditioning power consumption, two parameters, relative humidity, and scheduling, must be added to the prediction model.

Associations between geographical environment and systolic pulmonary arterial pressure of Chinese adults: impact analysis and predictive modeling

Since systolic pulmonary arterial pressure (SPAP) is an important diagnostic indicator for various cardiovascular diseases, it is of great significance to determine scientific SPAP reference value in clinical application. However, the SPAP reference values currently have not been applied under a unified standard, and its formulation does not consider the impacts from geographical environment which has proved to be closely associated with SPAP. This study aims to quantify the impacts of geographical factors on SPAP and formulate scientific SPAP reference values, thereby providing support for more accurate diagnosis. Measured SPAP values of 4550 healthy adults were collected from 88 cities across China, and 11 geographical factors were selected. Four geographical factors with significant impacts on SPAP were determined via correlation analysis, including two positive factors (altitude, soil organic matter) and two negative ones (longitude, annual average temperature). Then partial least-squares regression analysis (PLSR) and trend surface analysis were applied to establish predictive models. Through model test using both collected and simulated SPAP data of control points, the PLSR model was determined to have better prediction accuracy and was selected as optimal model to calculate the SPAP reference values of 2322 cities in China. The predictive results ranged from 22.09 to 31.77 mmHg. Finally, hotspot analysis and kriging interpolation method were applied to explore the spatial distribution of SPAP reference values. The result of spatial analysis shows that SPAP reference values of Chinese adults decreased gradually from the West to East in China. This study indicated the significant impacts of geographical environment on SPAP and established predictive model for determining SPAP reference values, which is expected to help enhance clinical diagnostic accuracy.

Intellectual and social similarity among scholarly journals: An exploratory comparison of the networks of editors, authors and co-citations

This paper explores, by using suitable quantitative techniques, to what extent the intellectual proximity among scholarly journals is also proximity in terms of social communities gathered around the journals. Three fields are considered: statistics, economics and information and library sciences. Co-citation networks represent intellectual proximity among journals. The academic communities around the journals are represented by considering the networks of journals generated by authors writing in more than one journal (interlocking authorship: IA), and the networks generated by scholars sitting on the editorial board of more than one journal (interlocking editorship: IE). Dissimilarity matrices are considered to compare the whole structure of the networks. The CC, IE, and IA networks appear to be correlated for the three fields. The strongest correlation is between CC and IA for the three fields. Lower and similar correlations are obtained for CC and IE, and for IE and IA. The CC, IE, and IA networks are then partitioned in communities. Information and library sciences is the field in which communities are more easily detectable, whereas the most difficult field is economics. The degrees of association among the detected communities show that they are not independent. For all the fields, the strongest association is between CC and IA networks; the minimum level of association is between IE and CC. Overall, these results indicate that intellectual proximity is also proximity among authors and among editors of the journals. Thus, the three maps of editorial power, intellectual proximity, and authors communities tell similar stories.

Concordance in wetland physicochemical conditions, vegetation, and surrounding land cover is robust to data extraction approach

Concordance among wetland physicochemical conditions, vegetation, and surrounding land cover may result from the influence of land cover on the sources of plant propagules, on physicochemical conditions, and their subsequent determination of growing conditions. Alternatively, concordance may result if differences in climate, soils, and species pools are spatially confounded with differences in human population density and land conversion. Further, we expect that land cover within catchment boundaries will be more predictive than land cover in symmetrical buffers if runoff is a major pathway. We measured concordance between land cover, wetland vegetation and physicochemical conditions in 48 prairie pothole wetlands, controlling for inter-wetland distance. We contrasted land-cover data collected over a four-year period by multiple extraction approaches including topographically-delineated catchments and nested 30 m to 5,000 m radius buffers. After factoring out inter-wetland distance, physiochemical conditions were significantly concordant with land cover. Vegetation was not significantly concordant with land cover, though it was strongly and significantly concordant with physicochemical conditions. More, concordance was as strong when land cover was extracted from buffers <500 m in radius as from catchments, indicating the mechanism responsible is not topographically constrained. We conclude that local landscape structure does not directly influence wetland vegetation composition, but rather that vegetation depends on 1) physicochemical conditions in the wetland that are affected by surrounding land cover and on 2) regional factors such as the vegetation species pool and geographic gradients in climate, soil type, and land use.

Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia

Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.

Modeling the spatial and seasonal distribution of offshore recreational vessels in the southeast United States

Understanding the distribution and intensity of recreational boating activities is key for managing safety as well as environmental and social impacts. Recreational boating is a very important component of the diverse maritime traffic in the southeastern United States. The seasonal distribution of offshore recreational vessels in waters off the coast of Northeast Florida and Southeast Georgia was modeled using several techniques (Poisson, negative binomial, hurdle and zero inflated modes, generalized additive models, and generalized mixed models) and by combining map-based information provided by recreational boaters with environmental and geographical variables to find the most parsimonious model. Based on model performance, the final model analysis was conducted using a GAM approach with a negative binomial distribution. The best seasonal models explained between 86.1%- 88.6% of the total deviance. For most seasons, a model that included latitude, longitude, interaction between latitude and longitude, chlorophyll a concentration, and abundance of artificial reefs resulted in the best fit. The only exception was the model for the summer season, which did not include chlorophyll a concentration. Given the complexity of the study area, with a number of maritime activities and several marine species co-occurring, these models could provide information to analyze the distribution and overlap of recreational boating trips with other maritime activities (e.g., cargo ships, commercial vessels) and species (e.g., right whales, sea turtles, sharks). These analyses could be used to decrease harmful interactions among these groups and activities.

Model selection with multiple regression on distance matrices leads to incorrect inferences

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

Vertical profiles of pentachlorophenol and the microbial community in a paddy soil: influence of electron donors and acceptors

Vertical variations of pentachlorophenol (PCP) dissipation and microbial community were investigated in a paddy soil with the addition of electron acceptors (NO3(-), SO4(2-)) and donors (crop residues). Crop residues enhanced PCP dissipation by supplying dissolved organic carbon (DOC) as an electron donor, whereas NO3(-) and SO4(2-) inhibited it. The dissipation of PCP in electron donor treatments resulted in the accumulation of 3,4,5-trichlorophenol (3,4,5-TCP) except for wheat residues. The abundance and diversity of phospholipid fatty acids (PLFAs) decreased with increasing soil depth. The succession of predominant PLFAs shifted from aerobic bacteria to anaerobic bacteria when electron acceptors were changed to electron donors. The saturated/monounsaturated fatty acids (S/M) ratio increased with soil depth, which probably implied that nutrient turnover rate declined after the accumulation of 3,4,5-TCP. The results showed that the addition of electron donors and acceptors modified the microbial communities, which then further influenced the degradation pathway of PCP.

Four centuries of change in northeastern United States forests

The northeastern United States is a predominately-forested region that, like most of the eastern U.S., has undergone a 400-year history of intense logging, land clearance for agriculture, and natural reforestation. This setting affords the opportunity to address a major ecological question: How similar are today's forests to those existing prior to European colonization? Working throughout a nine-state region spanning Maine to Pennsylvania, we assembled a comprehensive database of archival land-survey records describing the forests at the time of European colonization. We compared these records to modern forest inventory data and described: (1) the magnitude and attributes of forest compositional change, (2) the geography of change, and (3) the relationships between change and environmental factors and historical land use. We found that with few exceptions, notably the American chestnut, the same taxa that made up the pre-colonial forest still comprise the forest today, despite ample opportunities for species invasion and loss. Nonetheless, there have been dramatic shifts in the relative abundance of forest taxa. The magnitude of change is spatially clustered at local scales (<125 km) but exhibits little evidence of regional-scale gradients. Compositional change is most strongly associated with the historical extent of agricultural clearing. Throughout the region, there has been a broad ecological shift away from late successional taxa, such as beech and hemlock, in favor of early- and mid-successional taxa, such as red maple and poplar. Additionally, the modern forest composition is more homogeneous and less coupled to local climatic controls.

Phylogenetic signal in the acoustic parameters of the advertisement calls of four clades of anurans

BACKGROUND

Anuran vocalizations, especially their advertisement calls, are largely species-specific and can be used to identify taxonomic affiliations. Because anurans are not vocal learners, their vocalizations are generally assumed to have a strong genetic component. This suggests that the degree of similarity between advertisement calls may be related to large-scale phylogenetic relationships. To test this hypothesis, advertisement calls from 90 species belonging to four large clades (Bufo, Hylinae, Leptodactylus, and Rana) were analyzed. Phylogenetic distances were estimated based on the DNA sequences of the 12S mitochondrial ribosomal RNA gene, and, for a subset of 49 species, on the rhodopsin gene. Mean values for five acoustic parameters (coefficient of variation of root-mean-square amplitude, dominant frequency, spectral flux, spectral irregularity, and spectral flatness) were computed for each species. We then tested for phylogenetic signal on the body-size-corrected residuals of these five parameters, using three statistical tests (Moran's I, Mantel, and Blomberg's K) and three models of genetic distance (pairwise distances, Abouheif's proximities, and the variance-covariance matrix derived from the phylogenetic tree).

RESULTS

A significant phylogenetic signal was detected for most acoustic parameters on the 12S dataset, across statistical tests and genetic distance models, both for the entire sample of 90 species and within clades in several cases. A further analysis on a subset of 49 species using genetic distances derived from rhodopsin and from 12S broadly confirmed the results obtained on the larger sample, indicating that the phylogenetic signals observed in these acoustic parameters can be detected using a variety of genetic distance models derived either from a variable mitochondrial sequence or from a conserved nuclear gene.

CONCLUSIONS

We found a robust relationship, in a large number of species, between anuran phylogenetic relatedness and acoustic similarity in the advertisement calls in a taxon with no evidence for vocal learning, even after correcting for the effect of body size. This finding, covering a broad sample of species whose vocalizations are fairly diverse, indicates that the intense selection on certain call characteristics observed in many anurans does not eliminate all acoustic indicators of relatedness. Our approach could potentially be applied to other vocal taxa.

Faunal breaks and species composition of Indo-Pacific corals: the role of plate tectonics, environment and habitat distribution

Species richness gradients are ubiquitous in nature, but the mechanisms that generate and maintain these patterns at macroecological scales remain unresolved. We use a new approach that focuses on overlapping geographical ranges of species to reveal that Indo-Pacific corals are assembled within 11 distinct faunal provinces. Province limits are characterized by co-occurrence of multiple species range boundaries. Unexpectedly, these faunal breaks are poorly predicted by contemporary environmental conditions and the present-day distribution of habitat. Instead, faunal breaks show striking concordance with geological features (tectonic plates and mantle plume tracks). The depth range over which a species occurs, its larval development rate and genus age are important determinants of the likelihood that species will straddle faunal breaks. Our findings indicate that historical processes, habitat heterogeneity and species colonization ability account for more of the present-day biogeographical patterns of corals than explanations based on the contemporary distribution of reefs or environmental conditions.

Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study

The Mantel correlogram is an elegant way to compute a correlogram for multivariate data. However, recent papers raised concerns about the power of the Mantel test itself. Hence the question: Is the Mantel correlogram powerful enough to be useful? To explore this issue, we compared the performances of the Mantel correlogram to those of other methods, using numerical simulations based on random, normally distributed data. For a single response variable, we compared it to the Moran and Geary correlograms. Type I error rates of the three methods were correct. Power of the Mantel correlogram was nearly as high as that of the univariate methods. For the multivariate case, the test of the multivariate variogram developed in the context of multiscale ordination is in fact a Mantel test, so that the power of the two methods is the same by definition. We devised an alternative permutation test based on the variance, which yielded similar results. Overall, the power of the Mantel test was high, the method successfully detecting spatial correlation at rates similar to the permutation test of the variance statistic in multivariate variograms. We conclude that the Mantel correlogram deserves its place in the ecologist's toolbox.

Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist

A recent increase in studies of β diversity has yielded a confusing array of concepts, measures and methods. Here, we provide a roadmap of the most widely used and ecologically relevant approaches for analysis through a series of mission statements. We distinguish two types of β diversity: directional turnover along a gradient vs. non-directional variation. Different measures emphasize different properties of ecological data. Such properties include the degree of emphasis on presence/absence vs. relative abundance information and the inclusion vs. exclusion of joint absences. Judicious use of multiple measures in concert can uncover the underlying nature of patterns in β diversity for a given dataset. A case study of Indonesian coral assemblages shows the utility of a multi-faceted approach. We advocate careful consideration of relevant questions, matched by appropriate analyses. The rigorous application of null models will also help to reveal potential processes driving observed patterns in β diversity.

Drought negatively affects communities on a foundation tree: growth rings predict diversity

Understanding how communities respond to extreme climatic events is important for predicting the impact of climate change on biodiversity. The plant vigor and stress hypotheses provide a theoretical framework for understanding how arthropods respond to stress, but are rarely tested at the community level. Following a record drought, we compared the communities of arthropods on pinyon pine (Pinus edulis) that exhibited a gradient in physical traits related to environmental stress (e.g., growth rate, branch dieback, and needle retention). Six patterns emerged that show how one of the predicted outcomes of climate change in the southwestern USA (i.e., increased drought severity) alters the communities of a foundation tree species. In accordance with the plant vigor hypothesis, increasing tree stress was correlated with an eight to tenfold decline in arthropod species richness and abundance. Trees that were more similar in their level of stress had more similar arthropod communities. Both foliage quantity and quality contributed to arthropod community structure. Individual species and feeding groups differed in their responses to plant stress, but most were negatively affected. Arthropod richness (r(2) = 0.48) and abundance (r(2) = 0.48) on individual trees were positively correlated with the tree's radial growth during drought. This relationship suggests that tree ring analysis may be used as a predictor of arthropod diversity, which is similar to findings with ectomycorrhizal fungi. A contrast of our findings on arthropod abundance with published data on colonization by mutualistic fungi on the same trees demonstrates that at low stress these two communities respond differently, but at high stress both are negatively affected. These results suggest that the effect of extreme climatic events such as drought on foundation tree species are likely to decrease multi-trophic diversity and shift arthropod community composition, which in turn could cascade to affect other associated taxa.

Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data

The Mantel test is widely used to test the linear or monotonic independence of the elements in two distance matrices. It is one of the few appropriate tests when the hypothesis under study can only be formulated in terms of distances; this is often the case with genetic data. In particular, the Mantel test has been widely used to test for spatial relationship between genetic data and spatial layout of the sampling locations. We describe the domain of application of the Mantel test and derived forms. Formula development demonstrates that the sum-of-squares (SS) partitioned in Mantel tests and regression on distance matrices differs from the SS partitioned in linear correlation, regression and canonical analysis. Numerical simulations show that in tests of significance of the relationship between simple variables and multivariate data tables, the power of linear correlation, regression and canonical analysis is far greater than that of the Mantel test and derived forms, meaning that the former methods are much more likely than the latter to detect a relationship when one is present in the data. Examples of difference in power are given for the detection of spatial gradients. Furthermore, the Mantel test does not correctly estimate the proportion of the original data variation explained by spatial structures. The Mantel test should not be used as a general method for the investigation of linear relationships or spatial structures in univariate or multivariate data. Its use should be restricted to tests of hypotheses that can only be formulated in terms of distances.

Statistical performance and information content of time lag analysis and redundancy analysis in time series modeling

Time lag analysis (TLA) is a distance-based approach used to study temporal dynamics of ecological communities by measuring community dissimilarity over increasing time lags. Despite its increased use in recent years, its performance in comparison with other more direct methods (i.e., canonical ordination) has not been evaluated. This study fills this gap using extensive simulations and real data sets from experimental temporary ponds (true zooplankton communities) and landscape studies (landscape categories as pseudo-communities) that differ in community structure and anthropogenic stress history. Modeling time with a principal coordinate of neighborhood matrices (PCNM) approach, the canonical ordination technique (redundancy analysis; RDA) consistently outperformed the other statistical tests (i.e., TLAs, Mantel test, and RDA based on linear time trends) using all real data. In addition, the RDA-PCNM revealed different patterns of temporal change, and the strength of each individual time pattern, in terms of adjusted variance explained, could be evaluated, It also identified species contributions to these patterns of temporal change. This additional information is not provided by distance-based methods. The simulation study revealed better Type I error properties of the canonical ordination techniques compared with the distance-based approaches when no deterministic component of change was imposed on the communities. The simulation also revealed that strong emphasis on uniform deterministic change and low variability at other temporal scales is needed to result in decreased statistical power of the RDA-PCNM approach relative to the other methods. Based on the statistical performance of and information content provided by RDA-PCNM models, this technique serves ecologists as a powerful tool for modeling temporal change of ecological (pseudo-) communities.

Genetic and functional variation in denitrifier populations along a short-term restoration chronosequence

Complete removal of plants and soil to exposed bedrock, in order to eradicate the Hole-in-the-Donut (HID) region of the Everglades National Park, FL, of exotic invasive plants, presented the opportunity to monitor the redevelopment of soil and the associated microbial communities along a short-term restoration chronosequence. Sampling plots were established for sites restored in 1989, 1997, 2000, 2001, and 2003. The goal of this study was to characterize the activity and diversity of denitrifying bacterial populations in developing HID soils in an effort to understand changes in nitrogen (N) cycling during short-term primary succession. Denitrifying enzyme activity (DEA) was detected in soils from all sites, indicating a potential for N loss via denitrification. However, no correlation between DEA and time since disturbance was observed. Diversity of bacterial denitrifiers in soils was characterized by sequence analysis of nitrite reductase genes (nirK and nirS) in DNA extracts from soils ranging in nitrate concentrations from 1.8 to 7.8 mg kg(-1). High levels of diversity were observed in both nirK and nirS clone libraries. Statistical analyses of clone libraries suggest a different response of nirS- and nirK-type denitrifiers to factors associated with soil redevelopment. nirS populations demonstrated a linear pattern of succession, with individual lineages represented at each site, while multiple levels of analysis suggest nirK populations respond in a grouped pattern. These findings suggest that nirK communities are more sensitive than nirS communities to environmental gradients in these soils.

Patterns of community composition in two tropical tree frog assemblages: separating spatial structure and environmental effects in disturbed and undisturbed forests

An on-going controversy in community ecology involves the debate about the many factors that affect the assembly and composition of a given species assemblage. Theory suggests that community composition is influenced by environmental gradients or biotic processes. This study examines patterns of community composition in two tropical tree frog assemblages of primary and exploited lowland rain-forest sites in the Guiana Shield area of central Guyana, South America and the Upper Guinean rain-forest block of south-western Côte d'Ivoire, West Africa. We tested community composition and species abundance data of two adult tree frog communities collected on 21 standardized transects during a period of 5 y for evidence of spatial correlation in community composition. We applied simple and partial Mantel tests to separate the effects of environmental variables, spatial distance and spatial autocorrelation on community composition. Whenever environmental effects were accounted for, we found significant positive spatial correlation of community composition. All assemblages appeared to be spatially structured, i.e. sites in close proximity had similar species assemblages. However, spatially structured environmental variation (autocorrelation) did not account for the spatial structure of species incidence. Environmental factors did not prove to be significant predictors of species incidence in any of the assemblages analysed, even if we controlled for spatial effects. Observed correlation patterns of species composition were consistent within respective realms and disturbance regimes. Moreover, general correlation patterns were consistent between geographic regions. These results are in contrast to previously published results from a study on leaf-litter anurans and indicate that group-specific differences must not be neglected when analysing patterns of species composition in anurans as they may drastically alter the outcome of the analysis.

Permutation tests for the correlation among genetic distances and measures of heterosis

It is often found that heterosis tends to increase with genetic distance of the parents, though the correlation is not usually very close. It is therefore important to test the null hypothesis that the correlation is zero. The present work shows that standard procedures tend to yield too liberal tests, owing to the lack of independence among genetic distances and among heterosis estimates. A valid alternative is to use a permutation test, which was first suggested by Mantel [(1967) Cancer Res 27: 209--220). This test is well-known among plant breeders and geneticists, who often use it to test the correlation among two distance matrices. Its use is not restricted to the comparison of distance matrices. This is demonstrated in the present work, using two published datasets on marker-based genetic distances of maize inbreds or populations and heterosis of their crosses. It is shown that the test is also applicable in the presence of missing data.