Assembly rules operate only in equilibrium communities: Is it true?

Very little is known of how disturbance affects community assembly rules. We examine this in three disturbance states in each of two ski areas on southern New Zealand mountains. Theory suggests that a community will become progressively more spatially organized during recovery from disturbance. Firstly, different patches of the community should become more similar through time, but this was seen in only one of the two areas and even then only examining species presence/absence. Secondly, it has been suggested that spatial autocorrelation will be stronger in less‐disturbed conditions, that is, there will be a stronger pattern of more distant patches being more dissimilar in species composition. This was generally borne out. However, the method indicated more point randomness in less‐disturbed sites. Assembly rules might be seen in species abundances. Previous work has found maximum evenness of abundances in later successional communities, but the pattern here was the opposite: high evenness in the most disturbed communities. The literature suggests that in undisturbed communities the distribution of species abundances (relative abundance distribution) will be general lognormal, and we further argue that the identity of the species across occupying rank positions in that distribution should be more consistent (rank consistency). Both predictions were borne out in one area, but neither in the other. Many workers suggest that niche‐based assembly rules will be stronger in undisturbed communities. However, there was only weak evidence of constancy in species richness. Local species assemblages tended to contain a relatively constant representation from different morphological/taxonomic guilds (guild proportionality) and this was significant in some tests, but contrary to theory this effect occurred mainly in the most disturbed sites. It is concluded that there is only limited truth in the frequent assumption that community structure is stronger in undisturbed, equilibrium communities.     

Successional trends in standing dead biomass in Mediterranean basin species

Question: Landscape models of fire occurrence in ecosystems assume that the time since the last fire determines vegetation flammability by enabling the accumulation of dead biomass. In this study we ask if Mediterranean basin shrublands respond to these models or, on the contrary, if initial successional stages in these ecosystems could be more flammable than later stages. Location: Mediterranean shrubland in the Valencia region, eastern Spain. Methods: Using different stages of vegetation development (5, 9, 14 and 26 years since the last fire), we first study the structural comiosition of the above‐ground biomass in 375 individuals of nine woody species. Then, we measure how the standing dead biomass varies during succession, taking into account the surface cover of each species and the quantity of total dead biomass accumulated in different successional stages (3, 9, 14 and 26 years since the last fire). Results: The largest amount of standing dead biomass at the plant community level is observed in the middle stages of the succession. Early successional species, such as Cistus spp., Ulex parviflorus and Pinus halepensis, have a higher percentage of standing dead biomass at earlier stages in the succession than species typical of later successional stages, e.g. Juniperus oxycedrus, Quercus coccifera and Quercus ilex. Conclusions: The results suggest that monotonic increase in fire hazard with increasing stand age is not necessarily the rule in Mediterranean basin shrublands, since early successional species may accumulate large amounts of standing dead biomass and thus promote fire at early successional stages.     

Convergence of community composition during secondary succession on Zokor rodent mounds on the Tibetan Plateau

Aims The community succession theory is much debated in ecology. We studied succession on Zokor rodent mounds on the Tibetan Plateau to address several fundamental questions, among them: (i) During secondary succession, does the community composition converge towards one community state or multiple states depending on the initial colonization? (ii) Do mound communities located in different background communities exhibit different assembly trajectories?Methods In a sub-alpine meadow, we investigated a total of 80 mound communities at several successional stages in three different background communities resulting from different management histories and compared their changes in species composition. The distribution of plant communities over time was analyzed with quantitative classification and ordination methods. The co-occurrence patterns of species were evaluated at each successional stage, and the degree of convergence/divergence among communities was obtained by calculating two beta-diversity indices.Important findings During secondary succession, species richness of mound communities changed over time, and this change was dependent on the background community. Five life-form groups exhibited different dynamic patterns in species richness and plant cover. Community composition and the degree of species co-occurrence between communities increased over time since disturbance. There was much variation in species composition at earlier stages of succession, but communities on older mounds became more similar to each other and to their surrounding vegetation over the course of secondary succession. Post-disturbance succession of Zokor mound communities transitioned from 'multiple alternative states' to 'background-based deterministic community assembly' over time. Tradeoffs between competition and colonization, as well as the characteristics of different life-forms and mass effects within a limited species pool are the mechanisms responsible for convergence of mound communities.     

Dynamic species co‐occurrence networks require dynamic biodiversity surrogates

In conservation it is inevitable that surrogates be selected to represent the occurrence of hard‐to‐find species and find priority locations for management. However, species co‐occurrence can vary over time. Here we demonstrate how temporal dynamics in species co‐occurrence influence the ability of managers to choose the best surrogate species. We develop an efficient optimisation formulation that selects the optimal set of complementary surrogate species from any co‐occurrence network. We apply it to two Australian datasets on successional bird responses to disturbances of revegetation and fire. We discover that a surprisingly small number of species are required to represent the majority of species co‐occurrences at any one time. Because co‐occurrence patterns are temporally dynamic, the optimal set of surrogates, and the number of surrogates required to achieve a desired surrogacy power, depend on sampling effort and the successional state of a system. Overlap in optimal sets of surrogates for representing 70% of co‐occurring species ranges from zero to 57% depending on when the surrogacy decision is made. Surrogate sets representing early successional communities over‐estimate the power of surrogacy decisions at later times. Our results show that in dynamic systems, optimal surrogates might be selected in different ways: 1) use short‐term monitoring to choose a larger number of static less‐informative surrogates; 2) use long‐term monitoring to choose a smaller number of static high‐power surrogates that may poorly represent early successional co‐occurrence; 3) develop adaptive surrogate selection frameworks with high short‐term and long‐term surrogacy power that update surrogate sets and capture temporal dynamics in species co‐occurrence. Our results suggest vigilance is needed when selecting surrogates for other co‐occurring species in dynamic landscapes, as selected surrogates from one time may have reduced effectiveness at a different time. Ultimately, decisions that fail to acknowledge dynamic species co‐occurrence will lead to uninformative or redundant surrogates.     

Markov models for covariate dependence of binary sequences

Suppose that a heterogeneous group of individuals is followed over time and that each individual can be in state 0 or state 1 at each time point. The sequence of states is assumed to follow a binary Markov chain. In this paper we model the transition probabilities for the 0 to 0 and 1 to 0 transitions by two logistic regressions, thus showing how the covariates relate to changes in state. With p covariates, there are 2(p + 1) parameters including intercepts, which we estimate by maximum likelihood. We show how to use transition probability estimates to test hypotheses about the probability of occupying state 0 at time i (i = 2, ..., T) and the equilibrium probability of state 0. These probabilities depend on the covariates. A recursive algorithm is suggested to estimate regression coefficients when some responses are missing. Extensions of the basic model which allow time-dependent covariates and nonstationary or second-order Markov chains are presented. An example shows the model applied to a study of the psychological impact of breast cancer in which women did or did not manifest distress at four time points in the year following surgery.     

A queueing model for chronic recurrent conditions under panel observation

In many chronic conditions, subjects alternate between an active and an inactive state, and sojourns into the active state may involve multiple lesions, infections, or other recurrences with different times of onset and resolution. We present a biologically interpretable model of such chronic recurrent conditions based on a queueing process. The model has a birth-death process describing recurrences and a semi-Markov process describing the alternation between active and inactive states, and can be fit to panel data that provide only a binary assessment of the active or inactive state at a series of discrete time points using a hidden Markov approach. We accommodate individual heterogeneity and covariates using a random effects model, and simulate the posterior distribution of unknowns using a Markov chain Monte Carlo algorithm. Application to a clinical trial of genital herpes shows how the method can characterize the biology of the disease and estimate treatment efficacy.     

Alien plant species coexist over time with native ones in Chilean Mediterranean grasslands

Aims Alien species are commonly considered as harmful weeds capable of decreasing native biodiversity and threatening ecosystems. Despite this assumption, little is known about the long-term patterns of the native–alien relationships associated with human disturbed managed landscapes. This study aims to elucidate the community dynamics associated with a successional gradient in Chilean Mediterranean grasslands, considering both native and alien species.Methods Species richness (natives and aliens separately) and life-form (annuals and perennials) were recorded in four Chilean post-agricultural grazed grasslands each covering a broad successional gradient (from 1 to 40 years since crop abandonment). A detrended correspondence analysis (DCA), mixed model effects analyses and correlation tests were conducted to assess how this temporal gradient influenced natives and aliens through community dynamics.Important findings Our results show different life-form patterns between natives and aliens over time. Aliens were mainly represented by annuals (especially ruderals and weeds), which were established at the beginning of succession. Annual aliens also predominated at mid-successional stages, but in old grasslands native species were slightly more representative than alien ones within the community. In the late successional states, positive or no correlations at all between alien and native species richness suggested the absence of competition between both species groups, as a result of different strategies in occupation of the space. Community dynamics over time constitute a net gain in biodiversity, increasing natives and maintaining a general alien pool, allowing the coexistence of both. Biotic interactions including facilitation and/or tolerance processes might be occurring in Chilean post-agricultural grasslands, a fact that contradicts the accepted idea of the alien species as contenders.     

Graphs, random sums, and sojourn time distributions, with application to ion-channel modeling.

This paper considers the distribution of a sojourn time in a class of states of a stochastic process having finite discrete state space where sojourn times in any individual state are independent and identically distributed, and transitions between states follow a Markov chain. The state space and possible transitions of the process are represented by a graph. Class sojourn time distributions are derived by modifying this graph using 'composition' of states, defining a new Markov chain on the modified graph, and expressing the sojourn time in a composition state as a random sum. Appropriate compositions are chosen according to the possible "cores" of sojourns in the particular class, where a core describes the structure of a sojourn in terms of a single state or a chain in the original graph. Graph methods provide an algorithmic basis for the derivation, which can be simplified by using symmetry results. Models of ion-channel kinetics are used throughout for illustration; class sojourn time distributions are important in such models because individual states are often indistinguishable experimentally. Markov processes are the special case where sojourn times in individual states are exponentially distributed. In this case kinetic parameter estimation based on the observed class sojourn time distribution is briefly discussed; explicit estimating equations applicable to sequential models of nicotinic receptor kinetics are given.     

A compact statistical model of the song syntax in Bengalese finch

Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition probabilities between the states do not depend on the state transition history. Here we analyze the song syntax in Bengalese finch. We show that the Markov model fails to capture the statistical properties of the syllable sequences. Instead, a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are revisited consecutively, and allows associations of more than one state to a given syllable. Such a model does not increase the model complexity significantly. Mathematically, the model is a partially observable Markov model with adaptation (POMMA). The success of the POMMA supports the branching chain network model of how syntax is controlled within the premotor song nucleus HVC, but also suggests that adaptation and many-to-one mapping from the syllable-encoding chain networks in HVC to syllables should be included in the network model.     

Leaf trait-environment relationships in a subtropical broadleaved forest in South-East China

Although trait analyses have become more important in community ecology, trait-environment correlations have rarely been studied along successional gradients. We asked which environmental variables had the strongest impact on intraspecific and interspecific trait variation in the community and which traits were most responsive to the environment. We established a series of plots in a secondary forest in the Chinese subtropics, stratified by successional stages that were defined by the time elapsed since the last logging activities. On a total of 27 plots all woody plants were recorded and a set of individuals of every species was analysed for leaf traits, resulting in a trait matrix of 26 leaf traits for 122 species. A Fourth Corner Analysis revealed that the mean values of many leaf traits were tightly related to the successional gradient. Most shifts in traits followed the leaf economics spectrum with decreasing specific leaf area and leaf nutrient contents with successional time. Beside succession, few additional environmental variables resulted in significant trait relationships, such as soil moisture and soil C and N content as well as topographical variables. Not all traits were related to the leaf economics spectrum, and thus, to the successional gradient, such as stomata size and density. By comparing different permutation models in the Fourth Corner Analysis, we found that the trait-environment link was based more on the association of species with the environment than of the communities with species traits. The strong species-environment association was brought about by a clear gradient in species composition along the succession series, while communities were not well differentiated in mean trait composition. In contrast, intraspecific trait variation did not show close environmental relationships. The study confirmed the role of environmental trait filtering in subtropical forests, with traits associated with the leaf economics spectrum being the most responsive ones.     

Biological significance of dead biomass retention trait in Mediterranean Basin species: an analysis between different successional niches and regeneration strategies as functional groups

Standing dead biomass retention is considered one of the most relevant fuel structural traits to affect plant flammability. However, very little is known about the biological significance of this trait and its distribution between different functional groups. Our aim was to analyse how the proportion of dead biomass produced in Mediterranean species is related to the successional niche of species (early‐, mid‐ and late‐successional stages) and the regeneration strategy of species (seeders and resprouters). We evaluated biomass distribution by size classes and standing dead biomass retention in nine dominant species from the Mediterranean Basin in different development stages (5, 9, 14 and 26 years since the last fire). The results revealed significant differences in the standing dead biomass retention of species that presented a distinct successional niche or regeneration strategy. These differences were restricted to the oldest ages studied (>9 years). Tree and small tree resprouters, typical in late‐successional stages, presented slight variations with age and a less marked trend to retain dead biomass, while seeder shrubs and dwarf shrubs, characteristic of early‐successional stages, showed high dead biomass loads. Our results suggest that the species that tend to retain more dead branches are colonising species that may promote fire in early‐successional stages.     

Effects of long‐term fire exclusion and frequent fire on plant community composition: A case study from semi‐arid shrublands

Time since last fire and fire frequency are strong determinants of plant community composition in fire‐prone landscapes. Our study aimed to establish the influence of time since last fire and fire frequency on plant community composition and diversity of a south‐west Australian semi‐arid shrubland. We employed a space‐for‐time approach using four fire age classes: ‘young’, 8–15 years since last fire; ‘medium’, 16–34; ‘old’, 35–50; and ‘very old’, 51–100; and three fire frequency classes: burnt once, twice and three times within the last 50 years. Species diversity was compared using one‐way ANOVA and species composition using PERMANOVA. Soil and climatic variables were included as covariables to partition underlying environmental drivers. We found that time since last fire influenced species richness, diversity and composition. Specifically, we recorded a late successional transition from woody seeders to long‐lived, arid‐zone, resprouting shrub species. Fire frequency did not influence species richness and diversity but did influence species composition via a reduction in cover of longer‐lived resprouter species – presumably because of a reduced ability to replenish epicormic buds and/or sufficient starch stores. The distinct floristic composition of old and very old habitat, and the vulnerability of these areas to wildfires, indicate that these areas are ecologically important and management should seek to preserve them.     

Markov chain analysis of succession in a rocky subtidal community

We present a Markov chain model of succession in a rocky subtidal community based on a long-term (1986-1994) study of subtidal invertebrates (14 species) at Ammen Rock Pinnacle in the Gulf of Maine. The model describes successional processes (disturbance, colonization, species persistence, and replacement), the equilibrium (stationary) community, and the rate of convergence. We described successional dynamics by species turnover rates, recurrence times, and the entropy of the transition matrix. We used perturbation analysis to quantify the response of diversity to successional rates and species removals. The equilibrium community was dominated by an encrusting sponge (Hymedesmia) and a bryozoan (Crisia eburnea). The equilibrium structure explained 98% of the variance in observed species frequencies. Dominant species have low probabilities of disturbance and high rates of colonization and persistence. On average, species turn over every 3.4 years. Recurrence times varied among species (7-268 years); rare species had the longest recurrence times. The community converged to equilibrium quickly (9.5 years), as measured by Dobrushin's coefficient of ergodicity. The largest changes in evenness would result from removal of the dominant sponge Hymedesmia. Subdominant species appear to increase evenness by slowing the dominance of Hymedesmia. Comparison of the subtidal community with intertidal and coral reef communities revealed that disturbance rates are an order of magnitude higher in coral reef than in rocky intertidal and subtidal communities. Colonization rates and turnover times, however, are lowest and longest in coral reefs, highest and shortest in intertidal communities, and intermediate in subtidal communities.     

Influence of late season fire on early successional vegetation of an Oklahoma prairie

Abstract. The study of vegetation dynamics in tallgrass prairie in response to fire has focused on dormant season fire in late successional prairies. Our objective was to determine if late season fire of varying frequency results in divergent successional patterns in an early successional tallgrass prairie disturbed by grazing and cultivation. Specifically, we evaluated the influence of late‐summer fires of varying frequency on community composition and species richness. We collected vegetation and environmental data on two sites burned in the late growing‐season at varying frequencies. These communities differed in composition depending primarily on edaphic factors, time since the last burn, and year‐to‐year variation. We interpret the time effect as related to changes in species composition accompanying plant succession that followed disturbance either from cropping and heavy grazing on the loamy site or heavy grazing on the shallow site. Other unidentified factors also have a role in vegetation dynamics on this prairie. Community composition and species richness were not consistently responsive to frequency of growing‐season fires.     

Species assembly in model ecosystems, II: Results of the assembly process

In the companion paper of this set (Capitán and Cuesta, 2010) we have developed a full analytical treatment of the model of species assembly introduced in Capitán et al. (2009). This model is based on the construction of an assembly graph containing all viable configurations of the community, and the definition of a Markov chain whose transitions are the transformations of communities by new species invasions. In the present paper we provide an exhaustive numerical analysis of the model, describing the average time to the recurrent state, the statistics of avalanches, and the dependence of the results on the amount of available resource. Our results are based on the fact that the Markov chain provides an asymptotic probability distribution for the recurrent states, which can be used to obtain averages of observables as well as the time variation of these magnitudes during succession, in an exact manner. Since the absorption times into the recurrent set are found to be comparable to the size of the system, the end state is quickly reached (in units of the invasion time). Thus, the final ecosystem can be regarded as a fluctuating complex system where species are continually replaced by newcomers without ever leaving the set of recurrent patterns. The assembly graph is dominated by pathways in which most invasions are accepted, triggering small extinction avalanches. Through the assembly process, communities become less resilient (e.g., have a higher return time to equilibrium) but become more robust in terms of resistance against new invasions.     

Bayesian estimation of ancestral character states on phylogenies

Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree and its branch lengths when making inferences about ancestral states or other comparative parameters. Here we present a general Bayesian approach for testing comparative hypotheses across statistically justified samples of phylogenies, focusing on the specific issue of reconstructing ancestral states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic trees and for investigating the parameters of a statistical model of trait evolution. We describe how to combine information about the uncertainty of the phylogeny with uncertainty in the estimate of the ancestral state. Our approach does not constrain the sample of trees only to those that contain the ancestral node or nodes of interest, and we show how to reconstruct ancestral states of uncertain nodes using a most-recent-common-ancestor approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla. Software implementing the methods (BayesMultiState) is available from the authors.     

Bayesian restoration of a hidden Markov chain with applications to DNA sequencing.

Hidden Markov models (HMMs) are a class of stochastic models that have proven to be powerful tools for the analysis of molecular sequence data. A hidden Markov model can be viewed as a black box that generates sequences of observations. The unobservable internal state of the box is stochastic and is determined by a finite state Markov chain. The observable output is stochastic with distribution determined by the state of the hidden Markov chain. We present a Bayesian solution to the problem of restoring the sequence of states visited by the hidden Markov chain from a given sequence of observed outputs. Our approach is based on a Monte Carlo Markov chain algorithm that allows us to draw samples from the full posterior distribution of the hidden Markov chain paths. The problem of estimating the probability of individual paths and the associated Monte Carlo error of these estimates is addressed. The method is illustrated by considering a problem of DNA sequence multiple alignment. The special structure for the hidden Markov model used in the sequence alignment problem is considered in detail. In conclusion, we discuss certain interesting aspects of biological sequence alignments that become accessible through the Bayesian approach to HMM restoration.     

A mixed mover-stayer model for spatiotemporal two-state processes

Studies of recurring infection or chronic disease often collect longitudinal data on the disease status of subjects. Two-state transitional models are useful for analysis in such studies where, at any point in time, an individual may be said to occupy either a diseased or disease-free state and interest centers on the transition process between states. Here, two additional features are present. The data are spatially arranged and it is important to account for spatial correlation in the transitional processes corresponding to different subjects. In addition there are subgroups of individuals with different mechanisms of transitions. These subgroups are not known a priori and hence group membership must be estimated. Covariates modulating transitions are included in a logistic additive framework. Inference for the resulting mixture spatial Markov regression model is not straightforward. We develop here a Monte Carlo expectation maximization algorithm for maximum likelihood estimation and a Markov chain Monte Carlo sampling scheme for summarizing the posterior distribution in a Bayesian analysis. The methodology is applied to a study of recurrent weevil infestation in British Columbia forests.     

The role of mosaic phenomena in natural communities

Interrelations among three groups of ideas are considered. (1) The place where a plant is rooted, or a sessile animal is attached, may be termed a microsite. The microsites for a community form a mosaic that is differentiated by physical environment or biological effects or both. Population function and regulation, and community self-maintenance and response to environmental fluctuation, can be approached in terms of the flow of reproducing populations through the mosaic. (2) Most communities are subject to disturbance followed by succession. Communities are diverse in the kinds and frequencies of disturbances and in kinds of successions and climaxes; and the species of a biota diversify in their relationships to successional time and patterns of successional and climax communities. (3) Although mosaic phenomena are general, two broad groupings may be recognized: intracommunity patterns that relate to microsite differentiation and species response to this, and intercommunity successional mosaics and climax complexes for which community disturbance is a major determining force. Relationships between elements of a mosaic can often be formulated in terms of a chain or network of replacement rates, but formulations should allow for the influence of bath terms and occurrence of semipermanent plateau stages in some successions.     

Old-field secondary succession in SE Spain: can fire divert it?

In the Mediterranean Basin, most cultivated areas were abandoned in the last century and are now in various stages of old-field succession. The aim of this work was to analyse the successional trajectories of these ecosystems, and to assess possible deviations in these pathways due to fire occurrence at high or low recurrence levels. Old-fields abandoned either about 50 or about 100 years ago were selected in SE Spain. Within the 50-year-old abandoned fields, plots were established which had been burned by 1, 2 and 3 fires in the last 25 years. Cover values of vascular species were sampled and then analysed by means of multivariate analysis. Euclidean distances between resulting communities were used as an indicator of the possible deviation from the unburned successional pathway. Our results pointed to the possibility that different successional pathways may exist depending on fire occurrence and recurrence. In the absence of fire, the vegetation is dominated by pioneer species, mainly Pinus. With the passage of time this vegetation will become dominated by later successional tree species (Quercus). However, when early-successional communities are affected by fire, the succession can be diverted. A single fire is enough to change Pinus forests into alternative stable states dominated by Rosmarinus officinalis shrub communities, where the colonisation of species in later successional stages is arrested. This deviation increases in high fire recurrence regimes where the vegetation changes to dwarf shrubs and herbs.