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Studies of foraging behaviour and respiratory physiology in breath‐holding divers often assume that each dive cycle (dive plus surface duration) is physiologically and ecologically independent within a series (or "bout") of sequential dives. We tested this assumption using time depth recorders and GPS data for more than 42,000 dives in 1289 bouts by 39 pairs of male and female European shags (Gulosus aristotelis) provisioning nestlings. We found distinct patterns of temporal autocorrelation over several dives within bouts, but this was driven mainly by consecutive dives of the same type, that is, runs of V‐shaped (presumably prey searching) versus U‐shaped (presumably active hunting) dives. We found no evidence of cumulative physiological effects (i.e. fatigue and/or lowered body temperature) across dives within a bout. However, within‐individual variation in dive behaviour revealed complex interactions. Longer bouts were associated with more V‐shaped dives, including more and longer runs of V‐shaped dives. Meanwhile, more U‐shaped dives and longer runs of U‐shaped dives acted as limiting factors to bout lengths, with longer bouts being associated with more U‐shaped dives only later in the bout. Interactions between bout length and body mass, and between dive order within the bout and body mass, also suggested various size‐specific patterns in the temporal distribution of U‐shaped dives. Long bouts and bouts ending in longer runs of V‐shaped dives were more likely to indicate the termination of foraging activity. However, neither dive type nor bout length predicted whether individuals subsequently (i) stayed to forage in the same location or (ii) moved to a new location to continue foraging within the same trip from the nest. European shags therefore showed temporal non‐independence across successive dive cycles and successive bouts of dives, likely as a result of temporal and spatial variation in prey availabilities rather than cumulative physiological effects that might contravene the assumptions in models of optimal dive behaviour. [ABSTRACT FROM AUTHOR]

The storage effect is a general explanation for coexistence in a variable environment. Unfortunately, the storage effect is poorly understood, in part because the generality of the storage effect precludes an interpretation that is simultaneously simple, intuitive and correct. Here, we explicate the storage effect by dividing one of its key conditions—covariance between environment and competition—into two pieces, namely that there must be a strong causal relationship between environment and competition, and that the effects of the environment do not change too quickly. This finer‐grained definition can explain a number of previous results, including (1) that the storage effect promotes annual plant coexistence when the germination rate fluctuates, but not when the seed yield fluctuates, (2) that the storage effect is more likely to be induced by resource competition than the apparent competition, and (3) why the storage effect arises readily in models with either stage structure or environmental autocorrelation. Additionally, our expanded definition suggests two novel mechanisms by which the temporal storage effect can arise—transgenerational plasticity and causal chains of environmental variables—thus suggesting that the storage effect is a more common phenomenon than previously thought. [ABSTRACT FROM AUTHOR]

Many statistical models currently used in ecology and evolution account for covariances among random errors. Here, I address five points: (i) correlated random errors unite many types of statistical models, including spatial, phylogenetic and time‐series models; (ii) random errors are neither unpredictable nor mistakes; (iii) diagnostics for correlated random errors are not useful, but simulations are; (iv) model predictions can be made with random errors; and (v) can random errors be causal?These five points are illustrated by applying statistical models to analyse simulated spatial, phylogenetic and time‐series data. These three simulation studies are paired with three types of predictions that can be made using information from covariances among random errors: predictions for goodness‐of‐fit, interpolation, and forecasting.In the simulation studies, models incorporating covariances among random errors improve inference about the relationship between dependent and independent variables. They also imply the existence of unmeasured variables that generate the covariances among random errors. Understanding the covariances among random errors gives information about possible processes underlying the data.Random errors are caused by something. Therefore, to extract full information from data, covariances among random errors should not just be included in statistical models; they should also be studied in their own right. Data are hard won, and appropriate statistical analyses can make the most of them. [ABSTRACT FROM AUTHOR]

Transgenerational plasticity is the plastic response of offspring to ancestral environments. Theoretical studies and experimental evolution have demonstrated the importance of environmental predictability in driving the evolution of transgenerational plasticity. Nevertheless, the contribution of environmental predictability to the variation of transgenerational plasticity in nature remains unexplored. Here, we took advantage of the natural variation of Arabidopsis thaliana, and employed genotypes collected from different geographic locations to explore their transgenerational effects in response to drought and nutrient addition. We found that transgenerational plasticity depended on the offspring environment and the genotype. In particular, we found that genotypes with greater transgenerational plasticity in reproductive traits came from locations with greater temporal autocorrelation of annual temperature and precipitation. These results suggest that the evolutionary process shaping natural variation of transgenerational effect is not random, and the predictability of natural environments may contribute to the evolution of transgenerational effects. While the robustness of our study is limited by the number of genotypes analyzed, we hope this study provides an incentive to conduct a more comprehensive analysis towards understanding natural divergence of transgenerational plasticity. [ABSTRACT FROM AUTHOR]

Maternal environmental effects create lagged population responses to past environments. Although they are ubiquitous and vary in expression across taxa, it remains unclear if and how their presence alters competitive interactions in ecological communities.Here, we use a discrete‐time competition model to simulate how maternal effects alter competitive dynamics in fluctuating and constant environments. Further, we explore how omitting maternal effects alter estimates of known model parameters from observational time series data.Our simulations demonstrate that (i) maternal effects change competitive outcomes, regardless of whether competitors otherwise interact neutrally or exhibit non‐neutral competitive differences, (ii) the consequences of maternal effects for competitive outcomes are mediated by the temporal structure of environmental variation, (iii) even in constant conditions, competitive outcomes are influenced by species' maternal effects strategies, and (iv) in observational time series data, omitting maternal effects reduces variation explained by models and biases parameter estimates, including competition coefficients.Our findings demonstrate that the ecological consequences of maternal effects hinge on the competitive environment. Evolutionary biologists have long recognized that maternal effects can be an important but often overlooked strategy buffering populations from environmental change. We suggest that maternal effects are similarly critical to ecology and call for research into maternal effects as drivers of dynamics in populations and communities. [ABSTRACT FROM AUTHOR]

First‐order dynamic occupancy models (FODOMs) are a class of state‐space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatiotemporally correlated. Consequently, the non‐independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second‐order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 yr of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long‐term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations. [ABSTRACT FROM AUTHOR]

Population dynamics are typically temporally autocorrelated: population sizes are positively or negatively correlated with past population sizes. Previous studies have found that positive temporal autocorrelation increases the risk of extinction due to 'inertia' that prolongs downward fluctuations in population size. However, temporal autocorrelation has not yet been analyzed at the level of life cycle transitions. We developed an R package, colorednoise, which creates stochastic matrix population projections with distinct temporal autocorrelation values for each matrix element. We used it to analyze long‐term demographic data on 25 populations from the COMADRE and COMPADRE databases and simulate their stochastic dynamics. We found a broad range of temporal autocorrelation across species, populations and life cycle stages. The number of stage‐classes in the matrix strongly affected the temporal autocorrelation of the growth rate. In the plant populations, reproduction transitions had more negative temporal autocorrelation than survival transitions, and matrices dominated by positive temporal autocorrelation had higher extinction risk, while in animal populations transition type was not associated with noise color. Our results indicate that temporal autocorrelation varies across life cycle transitions, even among populations of the same species. We present the colorednoise package for researchers to analyze the temporal autocorrelation of structured demographic rates. [ABSTRACT FROM AUTHOR]

One of the largest causes of fluctuations in the size and structure of populations is changes in the environment. In nature, these changes are often temporally autocorrelated and the strength and direction of the autocorrelation can affect population dynamics. These effects are mediated by complex, simultaneously occurring ecological and evolutionary processes, such as phenotypic plasticity and selection. Determining how these processes interact to affect responses of different life histories to autocorrelated environmental fluctuations is of paramount importance to infer which taxa are likely to go extinct or become invasive under global change. Here, we assessed the effect of autocorrelation in environmental states on the trait and population dynamics of different life histories using an evolutionary explicit individual- based modeling approach. We found that, in general, higher positive temporal autocorrelation caused more variation in population size. Fast life histories were more affected than slow ones as they were able to adapt more quickly to varying environmental optima and therefore experienced larger initial decreases in population size when optima changed. Including adaptive phenotypic plasticity buffered the effects of autocorrelation on population dynamics while nonadaptive plasticity amplified them, especially for slow life histories, which recovered less from maladaptation. This study highlights that integration of phenotypic plasticity, selection, and population dynamics is important to improve our understanding of how different life histories deal with autocorrelated climate variability. [ABSTRACT FROM AUTHOR]

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs. [ABSTRACT FROM AUTHOR]

Abstract: The dynamics of foundation species in ecosystems are key to the fate of many species. Kelp forests are foundation species in temperate ocean ecosystems and contribute to carbon storage, macronutrient dynamics, primary production and biodiversity of a myriad of associated species. Downward trends in their abundance globally have been of concern. We analysed 26 years of aerial censuses (1989–2015) of two canopy kelp species in Washington State (USA) waters. We compared these modern censuses with censuses in 1911 and 1912 to determine the persistence of kelp cover over the past century. Using Autoregressive Integrated Moving Average (ARIMA) models, we compared kelp dynamics with likely environmental drivers, including local environmental variables and ocean indices for this region. Kelp remains at historic levels in many areas, although some eastern populations in proximity to greater human populations are the exception to this pattern. Over the last 26 years, kelp abundance showed high spatial autocorrelation in western areas of Straits of Juan de Fuca, with more variable populations in the annual species and eastward towards Puget Sound. Both species covaried positively in their abundance throughout most of the study area, suggesting that environmental factors rather than competition, drove their dynamics. The population dynamics of these kelp species showed that the abundance 1 year previously was an important predictor, and cyclic dynamics were not indicated using ARIMA models. Kelp abundance correlated inversely with the Pacific Decadal Oscillation and the Oceanic Nino Index, and positively with the North Pacific Gyre Oscillation, indicating that large‐scale processes associated with colder seawater temperatures were associated with greater relative abundance of kelp. Synthesis. Kelp beds in the northern California Current Large Marine Ecosystem have mostly remained persistent over the past century and over many kilometres, but some areas may have decreased in abundance. The sensitivity of these populations to indices of ocean climate, our demonstration that a historic 93‐year sea surface temperature record (Race Rocks, Canada) showed a 0.72°C increase and the classification of some areas as high variability‐low abundance, suggest that the viability of these foundational species remain a concern into the future. [ABSTRACT FROM AUTHOR]

Transgenerational environmental effects can trigger strong phenotypic variation. However, it is unclear how cues from different preceding generations interact. Also, little is known about the genetic variation for these life history traits., Here, we present the effects of grandparental and parental mild heat, and their combination, on four traits of the third-generation phenotype of 14 Arabidopsis thaliana genotypes. We tested for correlations of these effects with climate and constructed a conceptual model to identify the environmental conditions that favour the parental effect on flowering time., We observed strong evidence for genotype-specific transgenerational effects. On average, A. thaliana accustomed to mild heat produced more seeds after two generations. Parental effects overruled grandparental effects in all traits except reproductive biomass. Flowering was generally accelerated by all transgenerational effects. Notably, the parental effect triggered earliest flowering in genotypes adapted to dry summers. Accordingly, this parental effect was favoured in the model when early summer heat terminated the growing season and environments were correlated across generations., Our results suggest that A. thaliana can partly accustom to mild heat over two generations and genotype-specific parental effects show non-random evolutionary divergence across populations that may support climate change adaptation in the Mediterranean. [ABSTRACT FROM AUTHOR]

Temporal data describe processes and phenomena that evolve over time. In many real-world applications temporal data are characterized by temporal autocorrelation, which expresses the dependence of time-stamped data over a certain a time lag. Often such processes and phenomena are characterized by evolving complex entities, which we can represent with evolving networks of data. In this scenario, a task that deserves attention is regression inference in temporal network data. In this paper, we investigate how to improve the predictive inference on network data by accommodating temporal autocorrelation of the historical data in the learning process of the prediction models. Historical data is a type of temporal data where most part of the elements has been already stored. In practice, we study how to explicitly consider the influence of data of a network observed in the past, to enhance the prediction on the same network observed at the present. The proposed approach relies on a model ensemble built with individual predictors learned on historical network data. The predictors are trained from summary networks, which synthesize the effect of the autocorrelation in distinct sequences of network observations. Summary networks are identified with a sliding window model. Finally, the model ensemble combines together the predictors with a weighting schema, which reflects the degree of influence of a predictor with respect to the network observed at the present. So, we aim at accommodating the temporal autocorrelation both in the data and in the prediction model. Empirical evaluation demonstrates that the proposed approach can boost regression performance in real-world network data. [ABSTRACT FROM AUTHOR]

Multiple factors complicate the analysis of animal telemetry location data. Recent advancements address issues such as temporal autocorrelation and telemetry measurement error, but additional challenges remain. Difficulties introduced by complicated error structures or barriers to animal movement can weaken inference. We propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. We specify a model for telemetry data observed with error conditional on unobserved true locations that reflects prior knowledge about constraints in the animal movement process. The observed telemetry data are modeled using a flexible distribution that accommodates extreme errors and complicated error structures. Although constraints to movement are often viewed as a nuisance, we use constraints to simultaneously estimate and account for telemetry error. We apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with measurement error and movement constraints. We then apply our framework to an Argos satellite telemetry data set on harbor seals (Phoca vitulina) in the Gulf of Alaska, a species that is constrained to move within the marine environment and adjacent coastlines. [ABSTRACT FROM AUTHOR]

Activity budget data are essential for determining behavioral responses to physiological and ecological variables. Yet, few studies are available to investigate the robustness, accuracy, and biases of the methods used to estimate activity budgets for cetaceans. In this study, we compare activity budgets of 55 adult female bottlenose dolphins in Shark Bay, Australia derived from two methods: surveys (n = 6,903) and focal follows (n = 1,185, totaling 2,721 h of observation). Activity budgets estimated from survey data differed in all behavioral states compared to focal follow data. However, when controlling for temporal autocorrelation, only time spent socializing and time spent traveling remained disparate between the methods. To control for biases associated with assigning group-level behavior to individuals, we also compared survey and focal follow activity budgets for lone females. Here we found differences between methods in time spent foraging and traveling regardless of whether we controlled for temporal autocorrelation, which suggests detection biases likely play a role in explaining differences in activity budget estimates between the two methodologies. Our results suggest that surveys are less representative of individual-level activity budgets, and thus, when individual-level knowledge about behavior is needed, focal follows are preferred. [ABSTRACT FROM AUTHOR]

Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for Island Scrub-Jays (Aphelocoma insularis), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying numbers of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring. [ABSTRACT FROM AUTHOR]

Tropical timber production forests have the potential to harbour many species, but landscape-level management strategies for minimizing extinctions are lacking., To develop relevant conservation guidelines, we investigated how harvest plans with different spatiotemporal patterns affected the persistence of sessile species with different traits and preharvest characteristics. We confronted this problem via an individual-based simulation model., We explored several harvest plans with different levels of spatiotemporal aggregation, but equivalent total harvest area, and found that extinction probabilities for each plan varied with species traits and predisturbance characteristics. Most notably, plans with large contiguous harvest units yielded particularly high extinction probabilities for dispersal-limited habitat specialists with clustered preharvest distributions. Differences between plans were small for some types of species, but highly aggregated plans yielded the highest extinction rates for all extinction-prone groups., Synthesis and applications. Our simulations suggest that reducing the size of contiguous harvest units, even while total harvest area remains constant, may reduce extinction rates in tropical production forests (assuming that road-related threats can be effectively managed). Our findings have important implications for tropical conservation efforts and also provide general insight into the compositional effects of disturbances with different spatiotemporal characteristics. [ABSTRACT FROM AUTHOR]

Population ecologists often use telemetry to estimate the probability that an animal is available for sampling (availability probability). Such estimates are paramount for generating reliable estimates of animal abundance and are sometimes of biological interest in their own right (e.g. for characterizing pinniped haul-out behaviour or landbird singing frequency)., We consider the estimation of availability probability from telemetry data when there are missing records. When records are missing-completely-at-random (i.e. not influenced by whether an animal is available for sampling or other covariates), approaches that censor missing records result in unbiased estimates of availability probability. However, censoring such records can result in bias when data are not missing-completely-at-random., We present a novel Bayesian temporal availability model that can be used to explicitly account for the process by which missing data arise. Our approach couples an underlying model for the partially observed availability process together with an observation model that allows state-dependent probabilities of missingness. This approach provides relatively unbiased estimates of availability probability even in the face of violations of the missing-at-random assumption. A new R package, TempOcc, is introduced to automate estimation., We demonstrate the utility of our approach by analysing both simulated data and hourly satellite telemetry records for 157 ice-associated seals in the Bering Sea, for which 27% of records were missing. In this case, we were interested in generating unbiased estimates of haul-out probabilities. Such probabilities are necessary when estimating abundance from aerial survey counts and assessing possible changes in seal phenology., Our analysis indicates that the missing-at-random assumption is indeed violated in telemetry studies of phocid seals in the Bering Sea. However, estimates of availability probability do not depend critically on this assumption., We recommend that ecologists routinely consider the implications of violating the missing-at-random assumption when estimating availability probabilities. Where possible, the modelling framework articulated in this study can be used to diagnose and correct for violations of the missing-at-random assumption. [ABSTRACT FROM AUTHOR]

Ecologists have long studied the effects of disturbance on species diversity. More recently, researchers have become interested in understanding how the various aspects of disturbance interact to influence community diversity. While the effects of temporal autocorrelation have also received some attention, the potential for manipulating disturbance autocorrelation to achieve management goals has not been theoretically explored., We consider the interactions between temporal autocorrelation of disturbance occurrence and disturbance intensity at varying disturbance frequencies. Using an annual plant model, we show that when intensity and frequency are kept constant, changing the temporal autocorrelation of disturbance occurrence can also affect competitive outcomes. Additionally, we show that when species coexist, the degree of autocorrelation can affect which species reaches higher densities., We describe several examples (including prescribed burning, grazing and mowing) that outline how manipulation of temporal autocorrelation may be used to achieve conservation and eradication goals at no additional cost., Synthesis and applications. Our results provide important insights into, and have potential application to, land management and conservation. While changing the intensity and frequency of human-induced disturbances can be costly, adjusting the temporal autocorrelation of disturbance occurrence may be considered a 'no-cost manipulation'. In instances where a land manager lacks the funds or resources to manipulate other aspects of disturbance, such as intensity and frequency, changing the temporal autocorrelation may provide an effective, economical alternative. [ABSTRACT FROM AUTHOR]

The article investigates the connection of spatial distribution, climate and soil moisture-holding capacity in plague epizootics in black-tailed prairie dogs, Cynomys ludovicianus, in northeastern Colorado. The study uses regression model that was parameterized using data from 1981 to 2003 and validated with data from 2004 to 2005. The study suggests that plague occurrence depends upon direct climatic effects on flea vectors and the plague pathogen.

Ecology Letters (2010) 13: 1182–1197 Explaining variation in population growth rates is fundamental to predicting population dynamics and population responses to environmental change. In this study, we used matrix population models, which link birth, growth and survival to population growth rate, to examine how and why population growth rates vary within and among 50 terrestrial plant species. Population growth rates were more similar within species than among species; with phylogeny having a minimal influence on among-species variation. Most population growth rates decreased over the observation period and were negatively autocorrelated between years; that is, higher than average population growth rates tended to be followed by lower than average population growth rates. Population growth rates varied more through time than space; this temporal variation was due mostly to variation in post-seedling survival and for a subset of species was partly explained by response to environmental factors, such as fire and herbivory. Stochastic population growth rates departed from mean matrix population growth rate for temporally autocorrelated environments. Our findings indicate that demographic data and models of closely related plant species cannot necessarily be used to make recommendations for conservation or control, and that post-seedling survival and the sequence of environmental conditions are critical for determining plant population growth rate. [ABSTRACT FROM AUTHOR]

The spatial and temporal variation in population sizes of animal colonies are rarely studied simultaneously. I examined factors determining colony size (number of nests) for 23 colonies from the only breeding population of rook Corvus frugilegus in Spain over 7 years. Population sizes within colonies were highly predictable over time, with autocorrelations up to a distance (lag) of 6 years. Autoregressive mixed models were used to explain colony size as a function of environmental factors, while controlling for temporal autocorrelation. These factors included refuse tips, widely used as food resource, and a derived variable that incorporated the two factors most often related to avian colony size (inter-colony competition and foraging habitat around colonies). Autoregressive models provided a better fit to the data than models which did not consider temporal autocorrelation. The information-theoretic (AICc-based) approach revealed uncertainty in the selection of the best model explaining colony-size, but relatively strong support for certain variables. The highest weights of evidence were for year ( ω i = 0.90) and the number of competitors per unit of foraging habitat (i.e., derived variable; ω i = 0.63), showing that the size of rook colonies in Spain was negatively affected by inter-colony competition relative to the foraging habitat surrounding the colonies. This variable measured within a 6-km radius from the colonies had ~30 times higher weight of evidence (more plausible) than the same variable measured within 3 km, indicating that food limitation may occur outside the breeding period. Sizes of colonies tended to decrease when distance between the colony and the nearest refuse tip increased. There was some evidence supporting the idea that the effect of the number of competitors per unit of foraging habitat on colony size varied from year to year, but statistical power was weak. These findings suggest that variation in number of rook nests within colonies reflects spatial and temporal heterogeneity of net food via both inter-colony competition and foraging habitat around the colony. [ABSTRACT FROM AUTHOR]

Computer models of flow and transport processes in critical or vulnerable reaches of rivers have become essential tools in assessing the long-term sustainable management of river basins. A generic long-standing source of uncertainty in the output from these models arises from the difficulty in estimating flow rates at the upstream boundary of the reach. When the process being modeled relies on an accurate description of the autocorrelation in the flow, such as in sediment transport, and predictions are required for future changed climatic conditions, this difficulty becomes acute. One currently popular solution is to combine stochastic weather generators with catchment hydrology models to simulate hydrographs. This paper offers an alternative solution, which bypasses the complexities of this approach and the extra uncertainties that arise from imposing restrictive predefined model structures. The proposed method uses an autoregressive multinomial logit model to link temperature and precipitation catchment data to flow data of the River Tees in the northeast United Kingdom. This model accounts for temporal autocorrelation by using the previous day's observation and other variables as explanatory variables. Incorporating basin area average temperature and precipitation allows climate change predictions from regional climate models to be used when predicting flows. The successful application of the method is demonstrated for the River Tees in northeast United Kingdom. [ABSTRACT FROM AUTHOR]

1. Patch dynamics is a new, potentially unifying mechanism for the explanation of tree-grass coexistence in savannas. In this scale-explicit paradigm, savannas consist of patches in which a cyclical succession between woody and grassy dominance proceeds spatially asynchronously. The growing ecological and economic problem of shrub encroachment is a natural transient phase in this cycle. 2. An important step towards understanding patterns at the landscape scale is achieved by investigating mechanisms at a smaller scale. We developed the spatially explicit individual-based simulation model SATCHMO to test the null hypothesis that cyclical succession cannot emerge from a realistic patch scale simulation model of the population dynamics of savanna woody species. 3. We calculated the partial temporal autocorrelation coefficient for 100 simulated time series of shrub cover over 500 years for time lags of up to 200 years to establish the existence and duration of successional cycles. We found a significant positive autocorrelation indicating the existence of cycles with a typical duration of about 33 years. 4. The shrub size frequency distributions over the course of a cycle showed shifts from dominance of small shrub sizes towards larger sizes during the increasing phase of a cycle and the reverse in the declining phase. This supports the three phase explanation as follows: (i) an initial phase when spatially and temporally overlapping favourable conditions lead to mass recruitment of shrubs; (ii) a build-up phase when the shrub cohort grows; and (iii) a break-down phase when increased competition due to crowding and unfavourable conditions lead to the break-down of the shrub cohort. The frequency distribution of shrub age at death over 10 simulations was also in agreement with this explanation. 5. We investigated the relationship between shrub cover, annual precipitation and time-lagged shrub cover to identify the driver of the cyclical successions. More than 90% of the variation in shrub cover was explained by shrub cover of the previous year, precipitation, and their interaction. 6. With the demonstration of precipitation-driven cyclical succession at the patch scale, we show that the mechanistic, temporal component of patch dynamics can be used to explain tree-grass coexistence in semi-arid savannas. [ABSTRACT FROM AUTHOR]