Spatial autocorrelation analysis allows disentangling the balance between neutral and niche processes in metacommunities

Wildland-urban interface typologies prone to high severity fires in Spain

Due to complex interactions between climate and land use changes, large forest fires have increased in frequency and severity over the last decades, impacting dramatically on biodiversity and society. In southern European countries affected by demographic challenges, fire risk and danger play special relevance at the wildland-urban interfaces (WUIs), where decision-making and land management have strong socio-ecological implications. WUIs have been historically typified according to both fire occurrence probability and settlement vulnerability, but those classifications lack generality regarding fire regime components. We aim to develop an integrated and comprehensive scheme for identifying the WUI typologies most at risk to fire severity across large territories. We selected fourteen large wildfires (over than 500 ha) occurred in Spain (2016-2021) containing different WUI scenarios. First, based on a building cartography and a multi-temporal series of Sentinel-2 imagery, each WUI was delimited and spatially characterized according to building density and pre-fire fuel characteristics (type, amount, and structure). Afterwards, a decision tree regression model was applied to identify the most relevant pre-fire vegetation parameters driving burn severity. The combined effect of the selected pre-fire vegetation drivers and the building density patterns on fire severity was evaluated using linear mixed models. Finally, the WUI typologies most prone to high burn severity were recognized using Tukey post-hoc tests. Results indicated that building density, land cover class and vegetation cover fraction determined fire severity in areas close to human settlements. Specifically, isolated, scattered and sparsely clustered buildings enclosed in a high-cover shrub matrix were the WUI typologies most susceptible to high-severity fires. These findings contribute to the development of appropriate strategies to minimize the risk of severe fires in WUIs and avoid potential losses of multiple ecosystem services valuable for society.

Predicting potential wildfire severity across Southern Europe with global data sources

The large environmental and socioeconomic impacts of wildfires in Southern Europe require the development of efficient generalizable tools for fire danger analysis and proactive environmental management. With this premise, we aimed to study the influence of different environmental variables on burn severity, as well as to develop accurate and generalizable models to predict burn severity. To address these objectives, we selected 23 wildfires (131,490 ha) across Southern Europe. Using satellite imagery and geospatial data available at the planetary scale, we spatialized burn severity as well as 20 pre-burn environmental variables, which were grouped into climatic, topographic, fuel load-type, fuel load-moisture and fuel continuity predictors. We sampled all variables and divided the data into three independent datasets: a training dataset, used to perform univariant regression models, random forest (RF) models by groups of variables, and RF models including all predictors (full and parsimonious models); a second dataset to analyze interpolation capacity within the training wildfires; and a third dataset to study extrapolation capacity to independent wildfires. Results showed that all environmental variables determined burn severity, which increased towards the mildest climatic conditions, sloping terrain, high fuel loads, and coniferous vegetation. In general, the highest predictive and generalization capacities were found for fuel load proxies obtained though multispectral imagery, both in the individual analysis and by groups of variables. The full and parsimonious models outperformed all, the individual models, models by groups, and formerly developed predictive models of burn severity, as they were able to explain up to 95%, 59% and 25% of variance when applied to the training, interpolation and extrapolation datasets respectively. Our study is a benchmark for progress in the prediction of fire danger, provides operational tools for the identification of areas at risk, and sets the basis for the design of pre-burn management actions.

Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021

Spatial autocorrelation describes the interdependent relationship between the realizations or observations of a variable that is distributed across a geographical landscape, which may be divided into different units/areas according to natural or political boundaries. Researchers of Geographical Information Science (GIS) always consider spatial autocorrelation. However, spatial autocorrelation research covers a wide range of disciplines, not only GIS, but spatial econometrics, ecology, biology, etc. Since spatial autocorrelation relates to multiple disciplines, it is difficult gain a wide breadth of knowledge on all its applications, which is very important for beginners to start their research as well as for experienced scholars to consider new perspectives in their works. Scientometric analyses are conducted in this paper to achieve this end. Specifically, we employ scientometrc indicators and scientometric network mapping techniques to discover influential journals, countries, institutions, and research communities; key topics and papers; and research development and trends. The conclusions are: (1) journals categorized into ecological and biological domains constitute the majority of TOP journals;(2) northern American countries, European countries, Australia, Brazil, and China contribute the most to spatial autocorrelation-related research; (3) eleven research communities consisting of three geographical communities and eight communities of other domains were detected; (4) hot topics include spatial autocorrelation analysis for molecular data, biodiversity, spatial heterogeneity, and variability, and problems that have emerged in the rapid development of China; and (5) spatial statistics-based approaches and more intensive problem-oriented applications are, and still will be, the trend of spatial autocorrelation-related research. We also refine the results from a geographer’s perspective at the end of this paper.

Rethinking Biological Invasions as a Metacommunity Problem

Perhaps more than any other ecological discipline, invasion biology has married the practices of basic science and the application of that science. The conceptual frameworks of population regulation, metapopulations, supply-side ecology, and community assembly have all to some degree informed the regulation, management, and prevention of biological invasions. Invasion biology needs to continue to adopt emerging frameworks and paradigms to progress as both a basic and applied science. This need is urgent as the biological invasion problem continues to worsen. The development of metacommunity theory in the last two decades represents a paradigm-shifting approach to community ecology that emphasizes the multi-scale nature of community assembly and biodiversity regulation. Work on metacommunities has demonstrated that even relatively simple processes at local scales are often heavily influenced by regional-scale processes driven primarily by the dispersal of organisms. Often the influence of dispersal interacts with, or even swamps, the influence of local-scale drivers like environmental conditions and species interactions. An emphasis on dispersal and a focus on multi-scale processes enable metacommunity theory to contribute strongly to the advancement of invasion biology. Propagule pressure of invaders has been identified as one of the most important drivers facilitating invasion, so the metacommunity concept, designed to address how dispersal-driven dynamics affect community structure, can directly address many of the central questions of invasion biology. Here we revisit many of the important concepts and paradigms of biological invasions—propagule pressure, biotic resistance, enemy release, functional traits, neonative species, human-assisted transport,—and view those concepts through the lens of metacommunity theory. In doing so, we accomplish several goals. First, we show that work on metacommunities has generated multiple predictions, models, and the tools that can be directly applied to invasion scenarios. Among these predictions is that invasibility of a community should decrease with both local controls on community assembly, and the dispersal rates of native species. Second, we demonstrate that framing biological invasions in metacommunity terms actually unifies several seemingly disparate concepts central to invasion biology. Finally, we recommend several courses of action for the control and management of invasive species that emerge from applying the concepts of metacommunity theory.

The Spatial-Temporal Characteristics of Cultivated Land and Its Influential Factors in The Low Hilly Region: A Case Study of Lishan Town, Hubei Province, China

Cultivated land is a basic resource that is related to the sustainable development of the global economy and society. Studying the spatial and temporal distribution of cultivated land and its influential factors at the township scale is an important way to improve its sustainable use. Based on the land use data in 2009 and 2015, this paper comprehensively uses kernel density estimation, spatial autocorrelation analysis, and the spatial autoregressive model to analyze the spatial distribution characteristics and influential factors of cultivated land. The results show that in 2009 and 2015, the maximum kernel density of cultivated land in Lishan Town was 31/km2 and 38/km2, respectively, and there is an increasing tendency for it in the future. The global spatial autocorrelation Moran’s I of the proportion of cultivated land area in the administrative villages of Lishan Town in 2009 and 2015 was 0.5251 and 0.3970, respectively. Cultivated land has significant spatial self-positive correlation agglomeration characteristics in spatial distribution. Based on spatial error model (SEM) analysis, the regression coefficients of the village were 0.236 and 0.196 in 2009 and 2015, respectively. The regression coefficients of the road were 0.632 and 0.630, respectively. The regression coefficients of the water system were 0.481 and 0.290, respectively. The regression coefficients of the topographic position index were −0.817 and −0.672, respectively. By comparing 2015 with 2009, the regression coefficients of each influential factor have been reduced to varying degrees.

Conceptual and empirical advances in Neotropical biodiversity research

The unparalleled biodiversity found in the American tropics (the Neotropics) has attracted the attention of naturalists for centuries. Despite major advances in recent years in our understanding of the origin and diversification of many Neotropical taxa and biotic regions, many questions remain to be answered. Additional biological and geological data are still needed, as well as methodological advances that are capable of bridging these research fields. In this review, aimed primarily at advanced students and early-career scientists, we introduce the concept of "trans-disciplinary biogeography," which refers to the integration of data from multiple areas of research in biology (e.g., community ecology, phylogeography, systematics, historical biogeography) and Earth and the physical sciences (e.g., geology, climatology, palaeontology), as a means to reconstruct the giant puzzle of Neotropical biodiversity and evolution in space and time. We caution against extrapolating results derived from the study of one or a few taxa to convey general scenarios of Neotropical evolution and landscape formation. We urge more coordination and integration of data and ideas among disciplines, transcending their traditional boundaries, as a basis for advancing tomorrow's ground-breaking research. Our review highlights the great opportunities for studying the Neotropical biota to understand the evolution of life.

The complex roles of space and environment in structuring functional, taxonomic and phylogenetic beta diversity of frogs in the Atlantic Forest

Ecological communities are complex entities that can be maintained and structured by niche-based processes such as environmental conditions, and spatial processes such as dispersal. Thus, diversity patterns may be shaped simultaneously at different spatial scales by very distinct processes. Herein we assess whether and how functional, taxonomic, and phylogenetic beta diversities of frog tadpoles are explained by environmental and/or spatial predictors. We implemented a distance-based redundancy analysis to explore variation in components of beta diversity explained by pure environmental and pure spatial predictors, as well as their interactions, at both fine and broad spatial scales. Our results indicated important but complex roles of spatial and environmental predictors in structuring phylogenetic, taxonomic and functional beta diversities. The pure fine-scales spatial fraction was more important in structuring all beta diversity components, especially to functional and taxonomical spatial turnover. Environmental variables such as canopy cover and vegetation structure were important predictors of all components, but especially to functional and taxonomic beta diversity. We emphasize that distinct factors related to environment and space are affecting distinct components of beta diversity in different ways. Although weaker, phylogenetic beta diversity, which is structured more on biogeographical scales, and thus can be represented by spatially structured processes, was more related to broad spatial processes than other components. However, selected fine-scale spatial predictors denoted negative autocorrelation, which may be revealing the existence of differences in unmeasured habitat variables among samples. Although overall important, local environmental-based processes explained better functional and taxonomic beta diversity, as these diversity components carry an important ecological value. We highlight the importance of assessing different components of diversity patterns at different scales by spatially explicit models in order to improve our understanding of community structure and help to unravel the complex nature of biodiversity.

Model uncertainties do not affect observed patterns of species richness in the Amazon

BACKGROUND

Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale-patterns of species richness and species vulnerability to climate change-are affected by the inputs used to model and project species distribution.

METHODS

We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors.

RESULTS

The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses.

CONCLUSIONS

From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.

Recent advances in metacommunities and meta-ecosystem theories

Metacommunity theory has provided many insights into the general problem of local versus regional control of species diversity and relative abundance. The metacommunity framework has been extended from competitive interactions to whole food webs that can be described as spatial networks of interaction networks. Trophic metacommunity theory greatly contributed to resolving the community complexity-stability debate by predicting its dependence on the regional spatial context. The meta-ecosystem framework has since been suggested as a useful simplification of complex ecosystems to apply this spatial context to spatial flows of both individuals and matter. Reviewing the recent literature on metacommunity and meta-ecosystem theories suggests the importance of unifying theories of interaction strength into a meta-ecosystem framework that captures how the strength of spatial, species, and ecosystem fluxes are distributed across location and trophic levels. Such integration predicts important feedback between local and regional processes that drive the assembly of species, the stability of community, and the emergence of ecosystem functions, from limited spatial fluxes of individuals and (in)organic matter. These predictions are often mediated by the maintenance of environmental or endogenous fluctuations from local to regional scales that create important challenges and opportunities for the validation of metacommunity and meta-ecosystem theories and their application to conservation.

Predators regulate prey species sorting and spatial distribution in microbial landscapes

The role of predation in determining the metacommunity assembly model of prey communities is understudied relative to that of interspecific competition among prey. Previous work on metacommunity dynamics of competing species has shown that sorting by habitat patch type and spatial patterning can be affected by disturbances. Microcosms offer a useful model system to test the effect of multi-trophic interactions and disturbance on metacommunity dynamics. Here, we investigated the potential role of predators in enhancing or disrupting sorting and spatial pattern among prey in experimental landscapes. We exposed multi-trophic protist microcosm landscapes with one predator, two competing prey, two patch resource types, and localized dispersal to three disturbance regimes (none, low, and high). Then, we used variation partitioning and spatial clustering analysis to analyse the results. In contrast with previous experiments that did not manipulate predators, we found that patch type did not structure prey communities very well. Instead, we found that it was the distribution of the predator that most strongly predicted the composition of the prey community. The predator impacted species sorting by (1) preferentially consuming one prey, thereby acting as a strong local environmental driver, and by (2) indirectly magnifying the impact of patch food resources on the less preferred prey. The predator also enhanced spatial signal in the prey community because of its limited dispersal. Our results indicate that predators can strongly influence prey species sorting and spatial patterning in metacommunities in ways that would otherwise be attributed to stochastic effects, such as dispersal limitation or demographic drift. Therefore, whenever possible, predators should be explicitly included as separate explanatory factors in variation partitioning analyses.

Making sense of metacommunities: dispelling the mythology of a metacommunity typology

Metacommunity ecology has rapidly become a dominant framework through which ecologists understand the natural world. Unfortunately, persistent misunderstandings regarding metacommunity theory and the methods for evaluating hypotheses based on the theory are common in the ecological literature. Since its beginnings, four major paradigms-species sorting, mass effects, neutrality, and patch dynamics-have been associated with metacommunity ecology. The Big 4 have been misconstrued to represent the complete set of metacommunity dynamics. As a result, many investigators attempt to evaluate community assembly processes as strictly belonging to one of the Big 4 types, rather than embracing the full scope of metacommunity theory. The Big 4 were never intended to represent the entire spectrum of metacommunity dynamics and were rather examples of historical paradigms that fit within the new framework. We argue that perpetuation of the Big 4 typology hurts community ecology and we encourage researchers to embrace the full inference space of metacommunity theory. A related, but distinct issue is that the technique of variation partitioning is often used to evaluate the dynamics of metacommunities. This methodology has produced its own set of misunderstandings, some of which are directly a product of the Big 4 typology and others which are simply the product of poor study design or statistical artefacts. However, variation partitioning is a potentially powerful technique when used appropriately and we identify several strategies for successful utilization of variation partitioning.

Inferring the chemical form of 137Cs deposited by the Fukushima Dai-ichi Nuclear Power Plant accident by measuring (137)Cs incorporated into needle leaves and male cones of Japanese cedar trees

We hypothesized that the water-soluble (ionic) and water-insoluble (stable) radiocesium from the initial fallout of the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident was distributed in various proportions in the surrounding areas and that this distribution was reflected in the trees that suffered deposition from the initial fallout. This study attempted to evaluate local variations in the chemical form of (137)Cs derived from the initial fallout of the FDNPP accident and whether its chemical form affected the radiocesium concentration in the tissues currently growing in trees, even after the initial fallout ceased. For these estimations, the ratio between the (137)Cs concentration in Cryptomeria japonica needle leaves in the tree crown, which existed before the FDNPP accident and subsequently directly exposed to the initial fallout ((137)Cs pre-accident N), and the amount of (137)Cs in the initial fallout itself ((137)Cs fallout) was determined ((137)Cs pre-accident N/(137)Cs fallout) at 66 sites. In addition, the (137)Cs ratios between the male cones produced in 2012 ((137)Cs male cone) and needle leaves that had elongated in the spring of 2011 ((137)Cs 2011N) was determined at 82 sites ((137)Cs male cone/(137) Cs 2011N). Most of the sites with lower (137)Cs pre-accident N /(137)Cs fallout ratios were distributed in eastern Fukushima, relatively close to the Pacific Ocean coastline. Lower (137)Cs pre-accident N/(137)Cs fallout and higher (137)Cs malecone/(137)Cs 2011N were found to be associated with higher proportions of (137)Cs in ionic forms. These observations are consistent with the hypothesis, and likely reflect regional variations in the chemical form of the deposited radiocesium.

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns.

Spatial distribution patterns of ammonia-oxidizing archaea abundance in subtropical forests at early and late successional stages

Characterizing the spatial distribution patterns of soil microorganisms is helpful in understanding the biogeochemical processes they perform, but has been less studied relative to those of macroorganisms. In this study, we investigated and compared the spatially explicit distribution patterns of ammonia-oxidizing archaea (AOA) abundance and the influential factors between an early (ES) and a late successional (LS) subtropical forest stand. The average AOA abundance, vegetational attributes, and soil nutrient contents were mostly greater in the LS than the ES stand (P = 0.085 or smaller), but their spatial variations were more pronounced in the ES than the LS stand. The spatial distribution patches of AOA abundance were smaller and more irregular in the ES stand (patch size <50 m) than in the LS stand (patch size about 120 m). Edaphic and vegetational variables contributed more to the spatial variations of AOA abundance for the ES (9.3%) stand than for LS stand, whereas spatial variables (MEMs) were the main contributors (62%) for the LS stand. These results suggest that environmental filtering likely influence the spatial distribution of AOA abundance at early successional stage more than that at late successional stage, while spatial dispersal is dominant at late successional stage.

Dispersal ability determines the role of environmental, spatial and temporal drivers of metacommunity structure

Recently, community ecologists are focusing on the relative importance of local environmental factors and proxies to dispersal limitation to explain spatial variation in community structure. Albeit less explored, temporal processes may also be important in explaining species composition variation in metacommunities occupying dynamic systems. We aimed to evaluate the relative role of environmental, spatial and temporal variables on the metacommunity structure of different organism groups in the Upper Paraná River floodplain (Brazil). We used data on macrophytes, fish, benthic macroinvertebrates, zooplankton, periphyton, and phytoplankton collected in up to 36 habitats during a total of eight sampling campaigns over two years. According to variation partitioning results, the importance of predictors varied among biological groups. Spatial predictors were particularly important for organisms with comparatively lower dispersal ability, such as aquatic macrophytes and fish. On the other hand, environmental predictors were particularly important for organisms with high dispersal ability, such as microalgae, indicating the importance of species sorting processes in shaping the community structure of these organisms. The importance of watercourse distances increased when spatial variables were the main predictors of metacommunity structure. The contribution of temporal predictors was low. Our results emphasize the strength of a trait-based analysis and of better defining spatial variables. More importantly, they supported the view that “all-or- nothing” interpretations on the mechanisms structuring metacommunities are rather the exception than the rule.

Better environmental data may reverse conclusions about niche- and dispersal-based processes in community assembly

Variation partitioning of species composition into components explained by environmental and spatial variables is often used to identify a signature of niche- and dispersal-based processes in community assembly. Such interpretation, however, strongly depends on the quality of the environmental data available. In recent studies conducted in forest dynamics plots, the environment was represented only by readily available topographical variables. Using data from a subtropical broad-leaved dynamics plot in Taiwan, we focus on the question of how would the conclusion about importance of niche- and dispersal-based processes change if soil variables are also included in the analysis. To gain further insight, we introduced multiscale decomposition of a pure spatial component [c] in variation partitioning. Our results indicate that, if only topography is included, dispersal-based processes prevail, while including soil variables reverses this conclusion in favor of niche-based processes. Multiscale decomposition of [c] shows that if only topography was included, broad-scaled spatial variation prevails in [c], indicating that other as yet unmeasured environmental variables can be important. However, after also including soil variables this pattern disappears, increasing importance of meso- and fine-scaled spatial patterns indicative of dispersal processes.

Mantel test in population genetics

The comparison of genetic divergence or genetic distances, estimated by pairwise FST and related statistics, with geographical distances by Mantel test is one of the most popular approaches to evaluate spatial processes driving population structure. There have been, however, recent criticisms and discussions on the statistical performance of the Mantel test. Simultaneously, alternative frameworks for data analyses are being proposed. Here, we review the Mantel test and its variations, including Mantel correlograms and partial correlations and regressions. For illustrative purposes, we studied spatial genetic divergence among 25 populations of Dipteryx alata ("Baru"), a tree species endemic to the Cerrado, the Brazilian savannas, based on 8 microsatellite loci. We also applied alternative methods to analyze spatial patterns in this dataset, especially a multivariate generalization of Spatial Eigenfunction Analysis based on redundancy analysis. The different approaches resulted in similar estimates of the magnitude of spatial structure in the genetic data. Furthermore, the results were expected based on previous knowledge of the ecological and evolutionary processes underlying genetic variation in this species. Our review shows that a careful application and interpretation of Mantel tests, especially Mantel correlograms, can overcome some potential statistical problems and provide a simple and useful tool for multivariate analysis of spatial patterns of genetic divergence.

Distinguishing the importance between habitat specialization and dispersal limitation on species turnover

Understanding what governs community assembly and the maintenance of biodiversity is a central issue in ecology, but has been a continuing debate. A key question is the relative importance of habitat specialization (niche assembly) and dispersal limitation (dispersal assembly). In the middle of the Loess Plateau, northwestern China, we examined how species turnover in Liaodong oak (Quercus wutaishanica) forests differed between observed and randomized assemblies, and how this difference was affected by habitat specialization and dispersal limitation using variation partitioning. Results showed that expected species turnover based on individual randomization was significantly lower than the observed value (P < 0.01). The turnover deviation significantly depended on the environmental and geographical distances (P < 0.05). Environmental and spatial variables significantly explained approximately 40% of the species composition variation at all the three layers (P < 0.05). However, their contributions varied among forest layers; the herb and shrub layers were dominated by environmental factors, whereas the canopy layer was dominated by spatial factors. Our results underscore the importance of synthetic models that integrate effects of both dispersal and niche assembly for understanding the community assembly. However, habitat specialization (niche assembly) may not always be the dominant process in community assembly, even under harsh environments. Community assembly may be in a trait-dependent manner (e.g., forest layers in this study). Thus, taking more species traits into account would strengthen our confidence in the inferred assembly mechanisms.

Pseudoreplication in tropical forests and the resulting effects on biodiversity conservation

Tropical forest ecosystems are threatened by habitat conversion and other anthropogenic actions. Timber production forests can augment the conservation value of primary forest reserves, but studies of logging effects often yield contradictory findings and thus inhibit efforts to develop clear conservation strategies. We hypothesized that much of this variability reflects a common methodological flaw, simple pseudoreplication, that confounds logging effects with preexisting spatial variation. We reviewed recent studies of the effects of logging on biodiversity in tropical forests (n = 77) and found that 68% were definitively pseudoreplicated while only 7% were definitively free of pseudoreplication. The remaining proportion could not be clearly categorized. In addition, we collected compositional data on 7 taxa in 24 primary forest research plots and systematically analyzed subsets of these plots to calculate the probability that a pseudoreplicated comparison would incorrectly identify a treatment effect. Rates of false inference (i.e., the spurious detection of a treatment effect) were >0.5 for 2 taxa, 0.3-0.5 for 2 taxa, and <0.3 for 3 taxa. Our findings demonstrate that tropical conservation strategies are being informed by a body of literature that is rife with unwarranted inferences. Addressing pseudoreplication is essential for accurately assessing biodiversity in logged forests, identifying the relative merits of specific management practices and landscape configurations, and effectively balancing conservation with timber production in tropical forests.

Separating the effects of environment and space on tree species distribution: from population to community

Quantifying the relative contributions of environmental conditions and spatial factors to species distribution can help improve our understanding of the processes that drive diversity patterns. In this study, based on tree inventory, topography and soil data from a 20-ha stem-mapped permanent forest plot in Guangdong Province, China, we evaluated the influence of different ecological processes at different spatial scales using canonical redundancy analysis (RDA) at the community level and multiple linear regression at the species level. At the community level, the proportion of explained variation in species distribution increased with grid-cell sizes, primarily due to a monotonic increase in the explanatory power of environmental variables. At the species level, neither environmental nor spatial factors were important determinants of overstory species' distributions at small cell sizes. However, purely spatial variables explained most of the variation in the distributions of understory species at fine and intermediate cell sizes. Midstory species showed patterns that were intermediate between those of overstory and understory species. At the 20-m cell size, the influence of spatial factors was stronger for more dispersal-limited species, suggesting that much of the spatial structuring in this community can be explained by dispersal limitation. Comparing environmental factors, soil variables had higher explanatory power than did topography for species distribution. However, both topographic and edaphic variables were highly spatial structured. Our results suggested that dispersal limitation has an important influence on fine-intermediate scale (from several to tens of meters) species distribution, while environmental variability facilitates species distribution at intermediate (from ten to tens of meters) and broad (from tens to hundreds of meters) scales.

Geographical patterns of turnover and nestedness-resultant components of allelic diversity among populations

The analysis of geographical patterns in population divergence has always been a powerful way to infer microevolutionary processes involved in population differentiation, and several approaches have been used to investigate such patterns. Most frequently, multivariate spatial patterns of population differentiation are analyzed by computing pairwise genetic distances or F(ST) (or related statistics, such as ϕ(ST) from AMOVA), which are then correlated with geographical distances or landscape features. However, when calculating distances, especially based on presence-absence of alleles in local populations, there would be a confounding effect of allelic richness differences in the population differentiation. Moreover, the relative magnitude of these components and their spatial patterns can help identifying microevolutionary processes driving population differentiation. Here we show how recent methodological advances in ecological community analyses that allows partitioning dissimilarity into turnover (turnover) and richness differences, or nestedness-resultant dissimilarity, can be applied to allelic variation data, using an endemic Cerrado tree (Dipteryx alata) as a case study. Individuals from 15 local populations were genotyped for eight microsatellite loci, and pairwise dissimilarities were computed based on presence-absence of alleles. The turnover of alleles among populations represented 69 % of variation in dissimilarity, but only the richness difference component shows a clear spatial structure, appearing as a westward decrease of allelic richness. We show that decoupling richness difference and turnover components of allelic variation reveals more clearly how similarity among populations reflects geographical patterns in allelic diversity that can be interpreted in respect to historical range expansion in the species.

Geographical patterns and partition of turnover and richness components of beta-diversity in faunas from Tocantins river valley

There has been a resurging interest in patterns of β-diversity, especially by the mechanisms driving broad-scale, continental and global patterns, and how partitioning β-diversity into richness (or nestedness) and turnover components can be linked with such mechanisms. Here we compared two recent methodologies to find richness and turnover components of β-diversity, using a large regional scale dataset of mammal, bird, reptiles and amphibian species found in seven regions of Central, North and Northeastern Brazil. As well as a simple comparison of the metrics available, we analyzed spatial patterns (i.e., distance-decay similarity) and the effects of biome type in these components using raw and partial Mantel tests. Our analyses revealed that turnover estimated using Baselga's (2010) approach is slightly higher than the estimate using Carvalho's et al. (2012) approach, but all analyses show consistent spatial patterns in species turnover using both methods. Spatial patterns in β-diversity revealed by Mantel tests are also consistent with expectations based on differential dispersal abilities. Our results also reinforce that spatial patterns in β-diversity, mainly in the turnover components expressing faunal differentiation, are determined by a mix or broad scale environmental effects and short distance spatially-structured dispersal.