State space models with spatial deformation

Space deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal stationarity. In real applications, however, the temporal stationarity assumption is inappropriate and unrealistic. In this work we propose a spatial-temporal model whose temporal trend is modeled through state space models and a spatially varying anisotropy is modeled through spatial deformation, under the Bayesian approach. A distinctive feature of our approach is the consideration of model uncertainty in an unified framework. Our model has a clear advantage over the ones proposed so far in the literature when the main objective of the study is to perform spatial interpolation for fixed points in time. Approximations of the posterior distributions of the model parameters are obtained via Markov chain Monte Carlo methods. This allows for prediction of the process values in space and time as well as handling of missing values. Two applications are presented: the first one to model concentrations of sulfur dioxide in the eastern United States and the second one to model monthly minimum temperatures in the State of Rio de Janeiro.     

Population dynamics of the barnacle <Emphasis Type="Italic">Chthamalus montagui</Emphasis> at two spatial and temporal scales in northern Spain

This study describes spatial and temporal patterns of variability in population parameters in the barnacle Chthamalus montagui Southward in three localities of northern Spain and evaluates whether density-dependent settlement may regulate population dynamics. The sampling design considered two spatial scales, localities and sites within localities, and two temporal scales, years and six month intervals. Density, amount of free space, mortality, growth rate and magnitude of settlement (both absolute and per unit of free space) were obtained from photographs of permanent quadrats and from direct counts in the field. The number of settlers in scraped and untouched quadrats was used to estimate the importance of the presence of conspecifics in settlement. Significant variation at the two spatial and temporal scales was found for most parameters. Large spatial and temporal variations in adult mortality rate, density, and settlement were observed. Patterns of mortality were not consistent with differences in density among localities. Differences in settlement among localities were maintained through time. We suggest that magnitude of settlement is regulated by persistent features such as topography or local water circulation. We assume that early post-settlement mortality does not differ among localities. In the absence of differential mortality, settlement determines average population density within localities. Within localities, settlement was independent of density and free space. No consistent evidence was found on preferential settlement at the vicinity of conspecifics. The main conclusion is that density-dependent settlement is not relevant for the regulation of the populations of C. montagui in the northern Spain. Regulation might occur by density-dependent processes within the adult fraction of the population and/or the larval phase before settlement.     

Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates

Social Network Analysis has become an important methodological tool for advancing our understanding of human and animal group behaviour. However, researchers tend to rely on arbitrary distance and time measures when defining ‘contacts’ or ‘associations’ between individuals based on preliminary observation. Otherwise, criteria are chosen on the basis of the communication range of sensor devices (e.g. bluetooth communication ranges) or the sampling frequencies of collection devices (e.g. Global Positioning System devices). Thus, researchers lack an established protocol for determining both relevant association distances and minimum sampling rates required to accurately represent the network structure under investigation. In this paper, we demonstrate how researchers can use experimental and statistical methods to establish spatial and temporal association patterns and thus correctly characterise social networks in both time and space. To do this, we first perform a mixing experiment with Merino sheep (Ovis aries) and use a community detection algorithm that allows us to identify the spatial and temporal distance at which we can best identify clusters of previously familiar sheep. This turns out to be within 2–3 m of each other for at least 3 min. We then calculate the network graph entropy rate—a measure of ease of spreading of information (e.g. a disease) in a network—to determine the minimum sampling rate required to capture the variability observed in our sheep networks during distinct activity phases. Our results indicate the need for sampling intervals of less than a minute apart. The tools that we employ are versatile and could be applied to a wide range of species and social network datasets, thus allowing an increase in both the accuracy and efficiency of data collection when exploring spatial association patterns in gregarious species.     

Are savannas patch-dynamic systems? A landscape model

Savannas are ecosystems characterized by the coexistence of woody species (trees and bushes) and grasses. Given that savanna characteristics are mainly formed from competition, herbivory, fire, woodcutting, and patchy soil and precipitation characteristics, we propose a spatially explicit model to examine the effects of the above-mentioned parameters on savanna vegetation dynamics in space and time. Furthermore, we investigate the effects of the above-mentioned parameters on tree–bush–grass ratios, as well as the degrees of aggregation of tree–bush–grass biomass. We parameterized our model for an arid savanna with shallow soil depth as well as a mesic one with generally deeper and more variable soil depths. Our model was able to reproduce savanna vegetation characteristics for periods of time over 2000 years with daily updated time steps. According to our results, tree biomass was higher than bush biomass in the arid savanna but bush biomass exceeded tree and grass biomass in the simulated mesic savanna. Woody biomass increased in our simulations when the soil's porosity values were increased (mesic savanna), in combination with higher precipitation. Savanna vegetation varied from open savanna to woodland and back to open savanna again. Vegetation cycles varied over ∼300-year cycles in the arid and ∼220-year cycles in the mesic-simulated savanna. Autocorrelation values indicated that there are both temporal and spatial vegetation cycles. Our model indicated cycling savanna vegetation at the landscape scale, cycles in cells, and patchiness, i.e. patch dynamics.     

Hierarchical modeling for extreme values observed over space and time

We propose a hierarchical modeling approach for explaining a collection of spatially referenced time series of extreme values. We assume that the observations follow generalized extreme value (GEV) distributions whose locations and scales are jointly spatially dependent where the dependence is captured using multivariate Markov random field models specified through coregionalization. In addition, there is temporal dependence in the locations. There are various ways to provide appropriate specifications; we consider four choices. The models can be fitted using a Markov Chain Monte Carlo (MCMC) algorithm to enable inference for parameters and to provide spatio–temporal predictions. We fit the models to a set of gridded interpolated precipitation data collected over a 50-year period for the Cape Floristic Region in South Africa, summarizing results for what appears to be the best choice of model.

Modelling spatial and temporal variability in a zero-inflated variable: The case of stone pine (Pinus pinea L.) cone production

Modelling masting habit, i.e. the spatial synchronized annual variability in fruit production, is a huge task due to two main circumstances: (1) the identification of main ecological factors controlling fruiting processes, and (2) the common departure of fruit data series from the main basic statistical assumptions of normality and independence. Stone pine (Pinus pinea L.) is one of the main species in the Mediterranean basin that is able to grow under hard limiting conditions (sandy soils and extreme continental climate), and typically defined as a masting species. Considering the high economical value associated with edible nut production, the masting habit of stone pine has been a main concern for the forest management of the species. In the present work we have used annual fruit data series from 740 stone pine trees measured during a 13 years period (1996-2008) in order: (a) to verify our main hypothesis pointing out to the existence of a weather control of the fruiting process in limiting environments, rather than resource depletion or endogenous inherent cycles; (b) to identify those site factors, stand attributes and climate events affecting specific traits involved in fruiting process; and (c) to construct a model for predicting spatial and temporal patterns of variability in stone pine cone production at different spatial extents as region, stand and tree. Given the nature of the data, the model has been formulated as zero-inflated log-normal, incorporating random components to carry out with the observed lack of independence. This model attains efficiencies close to 70-80% in predicting temporal and spatial variability at regional scale. Though efficiencies are reduced according to the spatial extent of the model, it leads to unbiased estimates and efficiencies over 35-50% when predicting annual yields at tree or stand scale, respectively. In this sense, the proposed model is a main tool for facilitating decision making in some management aspects such as the quantification of total amount of cones annually supplied to nut industry, design of cone harvest programs or the optimal application of seedling felling.     

Predicting abundance of desert riparian birds: validation and calibration of the Effective Area Model.

Reliable prediction of the effects of landscape change on species abundance is critical to land managers who must make frequent, rapid decisions with long-term consequences. However, due to inherent temporal and spatial variability in ecological systems, previous attempts to predict species abundance in novel locations and/or time frames have been largely unsuccessful. The Effective Area Model (EAM) uses change in habitat composition and geometry coupled with response of animals to habitat edges to predict change in species abundance at a landscape scale. Our research goals were to validate EAM abundance predictions in new locations and to develop a calibration framework that enables absolute abundance predictions in novel regions or time frames. For model validation, we compared the EAM to a null model excluding edge effects in terms of accurate prediction of species abundance. The EAM outperformed the null model for 83.3% of species (N=12) for which it was possible to discern a difference when considering 50 validation sites. Likewise, the EAM outperformed the null model when considering subsets of validation sites categorized on the basis of four variables (isolation, presence of water, region, and focal habitat). Additionally, we explored a framework for producing calibrated models to decrease prediction error given inherent temporal and spatial variability in abundance. We calibrated the EAM to new locations using linear regression between observed and predicted abundance with and without additional habitat covariates. We found that model adjustments for unexplained variability in time and space, as well as variability that can be explained by incorporating additional covariates, improved EAM predictions. Calibrated EAM abundance estimates with additional site-level variables explained a significant amount of variability (P < 0.05) in observed abundance for 17 of 20 species, with R2 values >25% for 12 species, >48% for six species, and >60% for four species when considering all predictive models. The calibration framework described in this paper can be used to predict absolute abundance in sites different from those in which data were collected if the target population of sites to which one would like to statistically infer is sampled in a probabilistic way.     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.     

Spatio-temporal vigilance architecture of an Australian flying-fox colony

The social structure of animal aggregations may vary considerably in both space and time, yet little is known about how this affects vigilance. Here, we investigate the vigilance architecture of a colony of wild-living grey-headed flying-foxes (Pteropus poliocephalus) in Australia and examine how spatial as well as temporal variation in social organization influences social and environmental vigilance. We sampled color-marked individuals at different stages of the reproductive cycle and the year and at different locations in the colony to examine the effects of temporal and spatial factors on social and environmental vigilance. We found that vigilance architecture reflected the social structure of the colony, with the highest environmental vigilance being displayed by bats at the periphery of the colony, and the highest social vigilance by bats that roosted at intermediate distances from the colony’s edge. Furthermore, we found that vigilance levels reflected changes in reproductive state, with social vigilance increasing toward the mating season, particularly in males. Our findings show that spatial and temporal variation in social structure can have differential effects on social and environmental vigilance. This highlights the necessity to differentiate between functions of vigilance to understand fully vigilance architecture in aggregations of social animals.     

The effects of landscape structure on space competition and alternative stable states

Many species that compete for space live on heterogeneous landscapes and interact at local scales. The quality, amount, and structure of landscapes may have considerable impact on the ability of species to compete or coexist, yet basic models of space competition do not include that level of detail. We model space competition between two species with positive feedback through recruitment facilitation, which creates the potential for alternative stable states to occur. We compare the predictions of a spatially implicit model with a simulation model that includes explicit space and landscape structure. We create structured landscapes in which we specify the amount of habitat and degree of fragmentation and ask how landscape structure, dispersal strategy, and scale affect the presence of alternative stable states, or bistability. We find that structured landscapes can reduce the range of parameter values that lead to bistability in our model, but they do not eliminate bistability. The type of landscape and the dispersal distance for each species also influence the amount of environmental change needed for abrupt community shifts to occur. Coexistence of the two competitors is possible under certain conditions when connectivity is low. Consequently, landscape structure may lead to considerable disparity between the predictions of simple models and actual dynamics on complex landscapes during environmental change.     

Environmental variability and allocation trade-offs maintain species diversity in a process-based model of succulent plant communities

Ecological theory suggests that environmental variability can promote coexistence, provided that species occupy differential niches. In this study, we focus on two questions: (1) Do allocation trade-offs provide a sufficient basis for niche differentiation in succulent plant communities? (2) What is the relative importance of different forms of environmental variability on species diversity and community composition? We approach these questions with a generic, individual-based simulation model. In our model, plants compete for water in a spatially explicit environment. Species differ in their size at maturity and in the allocation of carbon to roots, leaves and storage tissue. The model was fully specified with independent literature data. Model output was compared to characteristics of a species-rich community in the semi-arid Richtersveld (South Africa). The model reproduced the coexistence of plants with different sizes at maturity, the dominance of succulent shrubs, and the level of vegetation cover. We analyzed the effects of three forms of environmental variability: (a) temporal fluctuations in precipitation (rain and fog), (b) spatial heterogeneity of water supply due to run-on and run-off processes and (c) ‘rock pockets’ that limit root competition in space. The three types of variability had differential effects on diversity: diversity exhibited a strong hump-shaped response to temporal variation. Spatial variability increased diversity, with the strongest increase occurring at intermediate levels of temporal variability. Finally, rock pockets had the weakest effect, but contributed to diversity by providing refuges for small species, particularly at low temporal variability. The model thus shows that spatio-temporal variation of resource supply can maintain diversity over long time scales even in small systems, as is the case in the Richtersveld succulent communities. Trade-offs in allocation provide the basis for necessary niche differentiation. By describing resource competition between individual plants, our model provides a mechanistic basis for the link from species traits to community composition at given environmental conditions. It thereby contributes to an understanding of the forces shaping plant communities. Such an understanding is critical to reduce the threats environmental change poses to biodiversity and ecosystem services.     

Detecting and Modeling Spatial and Temporal Dependence in Conservation Biology

Abstract: Due to the structuring forces and large-scale physical processes that shape our biosphere, we often find that environmental and ecological data are either spatially or temporally—or both spatially and temporally—dependent. When these data are analyzed, statistical techniques and models are frequently applied that were developed for independent data. We describe some of the detrimental consequences, such as inefficient parameter estimators, biased hypothesis test results, and inaccurate predictions, of ignoring spatial and temporal data dependencies, and we cite an example of adverse statistical results occurring when spatial dependencies were disregarded. We also discuss and recommend available techniques used to detect and model spatial and temporal dependence, including variograms, covariograms, autocorrelation and partial autocorrelation plots, geostatistical techniques, Gaussian autoregressive models, K functions, and ARIMA models, in environmental and ecological research to avoid the aforementioned difficulties.     

Vegetation structure constrains primary production response to water availability in the Patagonian steppe

Grassland aboveground net primary production (ANPP) increases linearly with precipitation in space and time, but temporal models relating time series of ANPP and annual precipitation for single sites show lower slopes and regression coefficients than are shown by spatial models. The analysis of several ANPP time series showed lags in the ecosystem response to increased water availability, which may explain the difference between spatial and temporal models. The lags may result from constraints that ecosystems experience after drought. Our objective was to explore the structural constraints of the ANPP response to rainfall variability in a semiarid ecosystem, the Patagonian steppe, in southern Argentina. We designed a 3-yr rainfall manipulation experiment where we decreased water input with rainout shelters during two consecutive years, which included three levels of rainfall interception (30%, 55%, and 80%) and a control. In the third year, we irrigated one-half of the plots of each rainfall-interception treatment. We evaluated the immediate effects of drought on current-year ANPP and the effects of previous-year drought on vegetation recovery after water supplementation. ANPP (g x m(-2) x yr(-1)) was linearly related to annual precipitation input (APPT; mm/yr) along the experimental precipitation gradient (ANPP = 0.13 x APPT + 58.3; r2 = 0.34, P < 0.01), and this relationship was mostly accounted for by changes in the ANPP of grasses. Plant density (D; no. individuals/mm2) was related to the precipitation received during the drought period (D = 0.11 x APPT + 18; r2 = 0.39, P < 0.05). The recovery of plants after irrigation was lower for those plots that had experienced experimental drought the previous years relative to controls, and the lags were proportional to the intensity of drought. Therefore, our results suggest that the density of plants may constrain the recovery of vegetation after drought, and these constraints may determine lags that limit the capacity of the ecosystem to take advantage of wet years after dry years.     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.     

Conservation planning for successional landscapes

The systematic conservation planning literature invariably assumes that the biodiversity features being preserved in sites do not change through time. We develop a conservation planning framework for ecosystems where disturbance events and succession drive vegetation dynamics. The framework incorporates three key attributes of disturbance theory: heterogeneity in disturbance rates, spatial correlation between disturbance events and different impacts of disturbance. In our conservation problem we wish to maximise the chance that we represent a certain number of successional types given a cap on the number of sites we can conserve. Correlation between disturbance events dramatically complicates the problem of choosing the optimal suite of sites. However, in our problem we discover that spatial correlation in disturbances affects the optimal reserve network very little. The reason is twofold: (i) through our probabilistic framework we focus on the long-term effectiveness of reserve networks and (ii) in the dynamics considered in our model the state of a site is not only affected by the most recent (correlated) disturbance event but also by the site's long-term stochastic history which blurs the impact of spatial correlation. If successional states are the conservation target rather than individual species then, conserving a site can only contribute to meeting one target. However, given that correlation of disturbance events may be ignored, we show that if the number of candidate reserves is sufficiently large the statistical dependence of different conservation targets may be ignored, too. We conclude that the computational complexity of reserve selection methods for dynamic ecosystems can be much simpler than they first appear.     

Temporal, spatial, and body size effects on growth rates of loggerhead sea turtles (Caretta caretta) in the Northwest Atlantic

In response to a call from the US National Research Council for research programs to combine their data to improve sea turtle population assessments, we analyzed somatic growth data for Northwest Atlantic (NWA) loggerhead sea turtles (Caretta caretta) from 10 research programs. We assessed growth dynamics over wide ranges of geography (9–33°N latitude), time (1978–2012), and body size (35.4–103.3 cm carapace length). Generalized additive models revealed significant spatial and temporal variation in growth rates and a significant decline in growth rates with increasing body size. Growth was more rapid in waters south of the USA (<24°N) than in USA waters. Growth dynamics in southern waters in the NWA need more study because sample size was small. Within USA waters, the significant spatial effect in growth rates of immature loggerheads did not exhibit a consistent latitudinal trend. Growth rates declined significantly from 1997 through 2007 and then leveled off or increased. During this same interval, annual nest counts in Florida declined by 43 % (Witherington et al. in Ecol Appl 19:30–54, 2009) before rebounding. Whether these simultaneous declines reflect responses in productivity to a common environmental change should be explored to determine whether somatic growth rates can help interpret population trends based on annual counts of nests or nesting females. Because of the significant spatial and temporal variation in growth rates, population models of NWA loggerheads should avoid employing growth data from restricted spatial or temporal coverage to calculate demographic metrics such as age at sexual maturity.     

Niche complementarity for nitrogen: an explanation for the biodiversity and ecosystem functioning relationship?

The relationship between plant diversity and productivity has largely been attributed to niche complementarity, assuming that plant species are complementary in their resource use. In this context, we conducted an 15N field study in three different grasslands, testing complementarity nitrogen (N) uptake patterns in terms of space, time, and chemical form as well as N strategies such as soil N use, symbiotic N fixation, or internal N recycling for different plant species. The relative contribution of different spatial, temporal, and chemical soil N pools to total soil N uptake of plants varied significantly among the investigated plant species, within and across functional groups. This suggests that plants occupy distinct niches with respect to their relative N uptake. However, when the absolute N uptake from the different soil N pools was analyzed, no spatial, temporal, or chemical variability was detected, but plants, and in particular functional groups, differed significantly with respect to their total soil N uptake irrespective of treatment. Consequently, our data suggest that absolute N exploitation on the ecosystem level is determined by species or functional group identity and thus by community composition rather than by complementary biodiversity effects. Across functional groups, total N uptake from the soil was negatively correlated with leaf N concentrations, suggesting that these functional groups follow different N use strategies to meet their N demands. While our findings give no evidence for a biodiversity effect on the quantitative exploitation of different soil N pools, there is evidence for different and complementary N strategies and thus a potentially beneficial effect of functional group diversity on ecosystem functioning.     

Social network analysis resolves temporal dynamics of male dominance relationships

Social organization is often studied through point estimates of individual association or interaction patterns, which does not account for temporal changes in the course of familiarization processes and the establishment of social dominance. Here, we present new insights on short-term temporal dynamics in social organization of mixed-sex groups that have the potential to affect sexual selection patterns. Using the live-bearing Atlantic molly (Poecilia mexicana), a species with pronounced male size polymorphism, we investigated social network dynamics of mixed sex experimental groups consisting of eight females and three different-sized males over a period of 5 days. Analyzing association-based social networks as well as direct measures of spatial proximity, we found that large males tended to monopolize most females, while excluding small- and medium-bodied males from access to females. This effect, however, emerged only gradually over time, and different-sized males had equal access to females on day 1 as well as day 2, though to a lesser extent. In this highly aggressive species with strong social dominance stratifications, the observed temporal dynamics in male-female association patterns may balance the presumed reproductive skew among differentially competitive male phenotypes when social structures are unstable (i.e., when individual turnover rates are moderate to high). Ultimately, our results point toward context-dependent sexual selection arising from temporal shifts in social organization.     

Local genetic structure and relatedness in a solitary mammal, Neotoma fuscipes

We used DNA microsatellites to investigate temporal and spatial patterns of local genetic differentiation and relatedness in a solitary mammal, the dusky-footed woodrat (Neotoma fuscipes). Patterns of genetic variation were measured relative to spatial clusters, or neighborhoods, of woodrats. We detected significant genetic differentiation among woodrat neighborhoods in two populations spanning multiple habitat types and densities. Estimates of θ _ST among neighborhoods ranged 0.034–0.075 and were comparable to levels reported in social mammals. Genetic differentiation at such a local scale is noteworthy because it occurred in the absence of any physical barriers to gene flow, suggesting that the patterns observed are linked to the nonrandom patterns of mating and dispersal that characterize woodrat social structure. Genetic differentiation and relatedness among neighborhoods were even higher when only resident females were analyzed. These results are consistent with a pattern of female philopatry and male-biased dispersal in woodrats. Geographic distance and relatedness were inversely correlated in adult females at intermediate densities, but not at low densities. Nonetheless, matrilineal genetic structure was apparent even at low woodrat densities based on estimates of θ _ST among neighborhoods of resident females that were significantly greater than zero and consistently greater than estimates including all individuals. In summary, this study demonstrates a matrilineal genetic structure in dusky-footed woodrats. In addition, our results support the idea that intermediate densities may be better at facilitating the formation of spatial kin clusters than either extreme. An erratum to this article can be found at     

Variability of competition at scales of 10<Superscript>1</Superscript>, 10<Superscript>3</Superscript>, 10<Superscript>5</Superscript>, and 10<Superscript>6</Superscript> m: encrusting arctic community patterns

Variability in interference competition was studied in benthic marine communities of the arctic and subarctic Atlantic intertidal and shallow subtidal zones. We sampled multiple square-metre quadrats at distances of 10¹, 10³, and 10⁵ m apart around the high polar island of Spitsbergen (Svalbard Archipelago). We also took some similar samples in Iceland and in the Faeroe Islands (10⁶ m apart from Spitsbergen samples). Encrusting fauna were present on high arctic intertidal rocks but we only found competitive interactions on subtidal substrata. On subarctic Icelandic and Faeroese shores, in contrast, spatial competition was common even in the intertidal zone. Analysis of variance of competition intensity data (numbers of interactions per area) revealed multiple factors to be significant influences explaining variability. Amongst the 10¹-, 10³-, and 10⁵-m spatial scales, only the largest emerged as a significant term. Whether intra- or interspecific competition dominated the types of interactions varied greatly between sites: 21–97% of competition was intraspecific. The proportion of competitive encounters resulting in a decided outcome (i.e. a win for one competitor and a loss for the other, rather than a tie or standoff between them) showed little variability at any scale. All the values of competition transitivity (how hierarchical a pecking order is) were very high compared to values reported in the literature from any other (polar or non-polar) locality. Variability in this measure was generally <10% across scales. We conclude from our data that great care must be taken in interpreting patterns of competition between similar taxa in large-scale space or time. Not only did most aspects of competition in our study communities vary significantly at the 10⁵-m scale but different aspects of competition varied at different scales and by hugely different amounts.Communicated by J.P. Thorpe, Port Erin