Novel spatial analysis methods reveal scale‐dependent spread and infer limiting factors of invasion by Sahara mustard

Multiple scale‐dependent ecological processes influence species distributions. Uncovering these drivers of dynamic range boundaries can provide fundamental ecological insights and vital knowledge for species management. We develop a transferable methodology that uses widely available data and tools to determine critical scales in range expansion and to infer dominating scale‐dependent forces that influence spread. We divide a focal geographic region into different sized square cells, representing different spatial scales. We then used herbarium records to determine the species' occupancy of cells at each spatial scale. We calculated the growth in cell occupancy across scales to infer the scale dependent expansion rate. This is the first time such a ‘box‐counting’ method is used to study range expansion. We coupled this multi‐scale analysis with species distribution models to determine the range and spatial scales where suitable climate allows the species to spread, and where other factors may be influencing the expansion. We demonstrate our methodology by assessing the spread of invasive Sahara mustard in North America. We detect critical scales where its spread is limited (100–500 km) or unconstrained (5–50 km) by climatic variables. Using climate‐based models to assess the similarity of climate envelopes in its native and invaded range, we find that the climate in the invaded range generally predicts the native distribution, suggesting that either there has been little local adaptation to climate occurring since introduction or the biological interaction experienced in the invaded range has not driven the species to occupy climatic conditions much different from its native range. Our novel method can be broadly utilized in other studies to generate critical insights into the scale dependency of different ecological drivers that influence the spread and distribution limits, as well as to help parameterizing predictions of future spread, and thus inform management decisions.     

The Scale of Successional Models and Restoration Objectives

Successional models are used to predict how restoration projects will achieve their goals. These models have been developed on different spatial and temporal scales and consequently emphasize different types of dynamics. This paper focuses on the restoration goal of self-sustainability, but only in the context of a long-term goal. Because of the temporal scale of this goal, we must consider the impact of processes arising outside of the restoration site as of greater importance than restoration itself. Because ecological systems are open, restoration sites will be subjected to many external influential processes. Depending on the landscape context, the impact of these processes may not be noticeable, or, at the other extreme, they may prevent the achievement of restoration objectives. A second issue is to emphasize the nature of processes in the long term, that they are a complex of characteristics such as magnitude, frequency, and extent. Ecological systems are only adapted to a range of values in each of these characteristics. Restoration often combines goals that are of different scales. Models appropriate to these goals need consideration.     

General Relationships between Abiotic Soil Properties and Soil Biota across Spatial Scales and Different Land-Use Types

Very few principles have been unraveled that explain the relationship between soil properties and soil biota across large spatial scales and different land-use types. Here, we seek these general relationships using data from 52 differently managed grassland and forest soils in three study regions spanning a latitudinal gradient in Germany. We hypothesize that, after extraction of variation that is explained by location and land-use type, soil properties still explain significant proportions of variation in the abundance and diversity of soil biota. If the relationships between predictors and soil organisms were analyzed individually for each predictor group, soil properties explained the highest amount of variation in soil biota abundance and diversity, followed by land-use type and sampling location. After extraction of variation that originated from location or land-use, abiotic soil properties explained significant amounts of variation in fungal, meso- and macrofauna, but not in yeast or bacterial biomass or diversity. Nitrate or nitrogen concentration and fungal biomass were positively related, but nitrate concentration was negatively related to the abundances of Collembola and mites and to the myriapod species richness across a range of forest and grassland soils. The species richness of earthworms was positively correlated with clay content of soils independent of sample location and land-use type. Our study indicates that after accounting for heterogeneity resulting from large scale differences among sampling locations and land-use types, soil properties still explain significant proportions of variation in fungal and soil fauna abundance or diversity. However, soil biota was also related to processes that act at larger spatial scales and bacteria or soil yeasts only showed weak relationships to soil properties. We therefore argue that more general relationships between soil properties and soil biota can only be derived from future studies that consider larger spatial scales and different land-use types.     

Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales

Whether niche processes, like environmental filtering, or neutral processes, like dispersal limitation, are the primary forces driving community assembly is a central question in ecology. Here, we use a natural experimental system of isolated tree “islands” to test whether environment or geography primarily structures fungal community composition at fine spatial scales. This system consists of isolated pairs of two distantly related, congeneric pine trees established at varying distances from each other and the forest edge, allowing us to disentangle the effects of geographic distance vs. host and edaphic environment on associated fungal communities. We identified fungal community composition with Illumina sequencing of ITS amplicons, measured all relevant environmental parameters for each tree—including tree age, size and soil chemistry—and calculated geographic distances from each tree to all others and to the nearest forest edge. We applied generalized dissimilarity modelling to test whether total and ectomycorrhizal fungal (EMF) communities were primarily structured by geographic or environmental filtering. Our results provide strong evidence that as in many other organisms, niche and neutral processes both contribute significantly to turnover in community composition in fungi, but environmental filtering plays the dominant role in structuring both free‐living and symbiotic fungal communities at fine spatial scales. In our study system, we found pH and organic matter primarily drive environmental filtering in total soil fungal communities and that pH and cation exchange capacity—and, surprisingly, not host species—were the largest factors affecting EMF community composition. These findings support an emerging paradigm that pH may play a central role in the assembly of all soil‐mediated systems.     

From introduction to equilibrium: reconstructing the invasive pathways of the Argentine ant in a Mediterranean region

Determining the geographical range of invasive species is an important component of formulating effective management strategies. In the absence of detailed distributional data, species distribution models can provide estimates of an invasion range and increase our understanding of the ecological processes acting at various spatial scales. We used two complementary approaches to evaluate the influence of historical and environmental factors in shaping the distribution of the Argentine ant ( Linepithema humile ), a widespread, highly invasive species native to South America. Occurrence data were combined with environmental data at incremental spatial scales (extent and resolution) to predict the suitable range of the ant invasion using ecological niche models. In addition, we also used a spread model that simulated the jump dispersal of the species to identify the most plausible scenarios of arrival of L. humile in the NE Iberian Peninsula at local scales. Based on the results of both modelling practices, we suggest that L. humile might have reached its maximum geographic range at regional scales in the NE Iberian Peninsula. However, the species does not appear in equilibrium with the environment at small spatial scales, and further expansions are expected along coastal and inland localities of the Costa Brava. Long-distance jumps are ultimately responsible for the spread of the Argentine ant in the area. Overall, our study shows the utility of combining niche based models with spread models to understand the dynamics of species' invasions.     

Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM)

Aim Analyses of species distributions are complicated by various origins of spatial autocorrelation (SAC) in biogeographical data. SAC may be particularly important for invasive species distribution models (iSDMs) because biological invasions are strongly influenced by dispersal and colonization processes that typically create highly structured distribution patterns. We examined the efficacy of using a multi‐scale framework to account for different origins of SAC, and compared non‐spatial models with models that accounted for SAC at multiple levels. Location We modelled the spatial distribution of an invasive forest pathogen, Phytophthora ramorum, in western USA. Methods We applied one conventional statistical method (generalized linear model, GLM) and one nonparametric technique (maximum entropy, Maxent) to a large dataset on P. ramorum occurrence (n = 3787) to develop four types of model that included environmental variables and that either ignored spatial context or incorporated it at a broad scale using trend surface analysis, a local scale using autocovariates, or multiple scales using spatial eigenvector mapping. We evaluated model accuracies and amounts of explained spatial structure, and examined the changes in predictive power of the environmental and spatial variables. Results Accounting for different scales of SAC significantly enhanced the predictive capability of iSDMs. Dramatic improvements were observed when fine‐scale SAC was included, suggesting that local range‐confining processes are important in P. ramorum spread. The importance of environmental variables was relatively consistent across all models, but the explanatory power decreased in spatial models for factors with strong spatial structure. While accounting for SAC reduced the amount of residual autocorrelation for GLM but not for Maxent, it still improved the performance of both approaches, supporting our hypothesis that dispersal and colonization processes are important factors to consider in distribution models of biological invasions. Main conclusions Spatial autocorrelation has become a paradigm in biogeography and ecological modelling. In addition to avoiding the violation of statistical assumptions, accounting for spatial patterns at multiple scales can enhance our understanding of dynamic processes that explain ecological mechanisms of invasion and improve the predictive performance of static iSDMs.     

Integrating trait and phylogenetic distances to assess scale‐dependent community assembly processes

Biodiversity is structured by multiple mechanisms that are dependent, at least in part, on ecological similarities and differences among species. Integrating traits and phylogenies in diversity metrics may provide deeper insight into community assembly processes across spatial scales. However, different traits are influenced by processes at different spatial scales, and it is not clear how trait‐spatial scale mismatches skew our ability to detect assembly patterns. An additional complexity is how phylogenetic distances, which might capture unmeasured traits, reflect spatially dependent processes. Here we analyze a freshwater zooplankton dataset from 91 ponds and show that different traits are associated with processes at different spatial scales. We first assessed the response of individual traits to processes at both α‐ and β‐scales, and then quantified the power of different combinations of traits and phylogenetic distances to reveal environmental and spatial drivers of α‐ and β‐diversity. We found that explanatory power was maximised when we accounted for environmental and spatial drivers with single, but different traits for α‐ and β‐diversity. Using the most appropriate trait for each spatial scale outperformed phylogenetic information, but phylogenetic information outperformed the same traits when these were used at the wrong spatial scale, and all outperformed taxonomic analyses that ignore trait and phylogenetic information. We demonstrate that accounting for species’ similarities and differences provides important information about dominant assembly mechanisms at different spatial scales, and that phylogeny is especially useful when measured traits are uninformative at a given spatial scale or when there is lack of trait data. Our study also indicates, however, that trait‐scale mismatches among phylogenetically conserved traits may affect the performance of phylogenetic indices compared to indices that account only for the best single trait at each spatial scale.     

A dynamic multi‐scale occupancy model to estimate temporal dynamics and hierarchical habitat use for nomadic species

Distribution models are increasingly being used to understand how landscape and climatic changes are affecting the processes driving spatial and temporal distributions of plants and animals. However, many modeling efforts ignore the dynamic processes that drive distributional patterns at different scales, which may result in misleading inference about the factors influencing species distributions. Current occupancy models allow estimation of occupancy at different scales and, separately, estimation of immigration and emigration. However, joint estimation of local extinction, colonization, and occupancy within a multi‐scale model is currently unpublished. We extended multi‐scale models to account for the dynamic processes governing species distributions, while concurrently modeling local‐scale availability. We fit the model to data for lark buntings and chestnut‐collared longspurs in the Great Plains, USA, collected under the Integrated Monitoring in Bird Conservation Regions program. We investigate how the amount of grassland and shrubland and annual vegetation conditions affect bird occupancy dynamics and local vegetation structure affects fine‐scale occupancy. Buntings were prevalent and longspurs rare in our study area, but both species were locally prevalent when present. Buntings colonized sites with preferred habitat configurations, longspurs colonized a wider range of landscape conditions, and site persistence of both was higher at sites with greener vegetation. Turnover rates were high for both species, quantifying the nomadic behavior of the species. Our model allows researchers to jointly investigate temporal dynamics of species distributions and hierarchical habitat use. Our results indicate that grassland birds respond to different covariates at landscape and local scales suggesting different conservation goals at each scale. High turnover rates of these species highlight the need to account for the dynamics of nomadic species, and our model can help inform how to coordinate management efforts to provide appropriate habitat configurations at the landscape scale and provide habitat targets for local managers.     

生态系统服务功能的空间尺度特征

生态系统服务功能的形成依赖于一定的空间和时间尺度上的生态系统结构与过程,只有在特定的时空尺度上才能表现其显著的主导作用和效果。不同尺度的生态系统服务功能对不同行政尺度上的利益相关方具有不同的重要性。一般而言,生态系统产品提供功能往往与当地居民的利益更密切;调节功能和支持功能通常与区域、全国,甚至全球尺度的人类利益相关;文化功能则与本地-全球尺度上的利益相关方关系密切。由于不同尺度的生态系统服务功能有时互相冲突,从而可能导致不同的生态系统管理策略。本文探讨了生态系统服务功能在不同空间尺度上的表现特征和生态系统服务功能的空间规律,论述了不同空间尺度上生态系统服务功能与不同行政尺度上利益相关方的关系,以期为生态系统服务功能的评价、生态补偿机制的建立与生态系统管理提供科学依据。     

The transferability of distribution models across regions: an amphibian case study

Aim Predicting species distribution is of fundamental importance for ecology and conservation. However, distribution models are usually established for only one region and it is unknown whether they can be transferred to other geographical regions. We studied the distribution of six amphibian species in five regions to address the question of whether the effect of landscape variables varied among regions. We analysed the effect of 10 variables extracted in six concentric buffers (from 100 m to 3 km) describing landscape composition around breeding ponds at different spatial scales. We used data on the occurrence of amphibian species in a total of 655 breeding ponds. We accounted for proximity to neighbouring populations by including a connectivity index to our models. We used logistic regression and information‐theoretic model selection to evaluate candidate models for each species. Location Switzerland. Results The explained deviance of each species’ best models varied between 5% and 32%. Models that included interactions between a region and a landscape variable were always included in the most parsimonious models. For all species, models including region‐by‐landscape interactions had similar support (Akaike weights) as models that did not include interaction terms. The spatial scale at which landscape variables affected species distribution varied from 100 m to 1000 m, which was in agreement with several recent studies suggesting that land use far away from the ponds can affect pond occupancy. Main conclusions Different species are affected by different landscape variables at different spatial scales and these effects may vary geographically, resulting in a generally low transferability of distribution models across regions. We also found that connectivity seems generally more important than landscape variables. This suggests that metapopulation processes may play a more important role in species distribution than habitat characteristics.     

Spatial modelling of succession-disturbance dynamics in forest ecosystems: Concepts and examples

Over the last few decades it has become increasingly obvious that disturbance, whether natural or anthropogenic in origin, is ubiquitous in ecosystems. Disturbance-related processes are now considered to be important determinants of the composition, structure and function of ecological systems. However, because disturbance and succession processes occur across a wide range of spatio-temporal scales their empirical investigation is difficult. To counter these difficulties much use has been made of spatial modelling to explore the response of ecological systems to disturbance(s) occurring at spatial scales from the individual to the landscape and above, and temporal scales from minutes to centuries. Here we consider such models by contrasting two alternative motivations for their development and use: prediction and exploration, with a focus on forested ecosystems. We consider the two approaches to be complementary rather than competing. Predictive modelling aims to combine knowledge (understanding and data) with the goal of predicting system dynamics; conversely, exploratory models focus on developing understanding in systems where uncertainty is high. Examples of exploratory modelling include model-based explorations of generic issues of criticality in ecological systems, whereas predictive models tend to be more heavily data-driven (e.g. species distribution models). By considering predictive and exploratory modelling alongside each other, we aim to illustrate the range of methods used to model succession and disturbance dynamics and the challenges involved in the model-building and evaluation processes in this arena.     

Intuition, functional responses and the formulation of predator-prey models when there is a large disparity in the spatial domains of the interacting species

1. The disparity of the spatial domains used by predators and prey is a common feature of many terrestrial avian and mammalian predatory interactions, as predators are typically more mobile and have larger home ranges than their prey. 2. Incorporating these realistic behavioural features requires formulating spatial predator-prey models having local prey mortality due to predation and its spatial aggregation, in order to generate a numerical response at timescales longer than the local prey consumption. Coupling the population dynamics occurring at different spatial scales is far from intuitive, and involves making important behavioural and demographic assumptions. Previous spatial predator-prey models resorted to intuition to derive local functional responses from non-spatial equivalents, and often involve unrealistic biological assumptions that restrict their validity. 3. We propose a hierarchical framework for deriving generic models of spatial predator-prey interactions that explicitly considers the behavioural and demographic processes occurring at different spatial and temporal scales. 4. The proposed framework highlights the circumstances wherein static spatial patterns emerge and can be a stabilizing mechanism of consumer-resource interactions.     

Environmental and biotic drivers of soil microbial β‐diversity across spatial and phylogenetic scales

Soil microbial communities play a key role in ecosystem functioning but still little is known about the processes that determine their turnover (β‐diversity) along ecological gradients. Here, we characterize soil microbial β‐diversity at two spatial scales and at multiple phylogenetic grains to ask how archaeal, bacterial and fungal communities are shaped by abiotic processes and biotic interactions with plants. We characterized microbial and plant communities using DNA metabarcoding of soil samples distributed across and within eighteen plots along an elevation gradient in the French Alps. The recovered taxa were placed onto phylogenies to estimate microbial and plant β‐diversity at different phylogenetic grains (i.e. resolution). We then modeled microbial β‐diversities with respect to plant β‐diversities and environmental dissimilarities across plots (landscape scale) and with respect to plant β‐diversities and spatial distances within plots (plot scale). At the landscape scale, fungal and archaeal β‐diversities were mostly related to plant β‐diversity, while bacterial β‐diversities were mostly related to environmental dissimilarities. At the plot scale, we detected a modest covariation of bacterial and fungal β‐diversities with plant β‐diversity; as well as a distance–decay relationship that suggested the influence of ecological drift on microbial communities. In addition, the covariation between fungal and plant β‐diversity at the plot scale was highest at fine or intermediate phylogenetic grains hinting that biotic interactions between those clades depends on early‐evolved traits. Altogether, we show how multiple ecological processes determine soil microbial community assembly at different spatial scales and how the strength of these processes change among microbial clades. In addition, we emphasized the imprint of microbial and plant evolutionary history on today's microbial community structure.     

From diversity indices to community assembly processes: a test with simulated data

Ecological theory suggests that spatial distribution of biodiversity is strongly driven by community assembly processes. Thus the study of diversity patterns combined with null model testing has become increasingly common to infer assembly processes from observed distributions of diversity indices. However, results in both empirical and simulation studies are inconsistent. The aim of our study is to determine with simulated data which facets of biodiversity, if any, may unravel the processes driving its spatial patterns, and to provide practical considerations about the combination of diversity indices that would produce significant and congruent signals when using null models. The study is based on simulated species’ assemblages that emerge under various landscape structures in a spatially explicit individual‐based model with contrasting, predefined assembly processes. We focus on four assembly processes (species‐sorting, mass effect, neutral dynamics and competition colonization trade‐off) and investigate the emerging species’ distributions with varied diversity indices (alpha, beta and gamma) measured at different spatial scales and for different diversity facets (taxonomic, functional and phylogenetic). We find that 1) the four assembly processes result in distinct spatial distributions of species under any landscape structure, 2) a broad range of diversity indices allows distinguishing between communities driven by different assembly processes, 3) null models provide congruent results only for a small fraction of diversity indices and 4) only a combination of these diversity indices allows identifying the correct assembly processes. Our study supports the inference of assembly processes from patterns of diversity only when different types of indices are combined. It highlights the need to combine phylogenetic, functional and taxonomic diversity indices at multiple spatial scales to effectively infer underlying assembly processes from diversity patterns by illustrating how combination of different indices might help disentangling the complex question of coexistence.     

Fungal endophyte β-diversity associated with Myrtaceae species in an Andean Patagonian forest (Argentina) and an Atlantic forest (Brazil)

The biogeography of microorganisms is poorly understood and how microbial diversity is structured is still an open debate. We investigated the processes underlying the fungal endophyte assemblages of phylogenetically related Myrtae host tree species at different spatial scales: regional, 101–5 000 km; local, 0–100 km; and microscale, 0–1 km. A total of 939 isolates was obtained and assigned to 51 distinct MOTUs based on the sequencing of the nrITS region. At regional scales, geographic distance was responsible for explaining the fungal community similarity, while, at a local scale, it was the environmental distance. Moreover, fungal endophytes exhibit preference in the colonization of Luma apiculata but not for Myrceugenia ovata var. nanophylla. Our results suggest that fungal endophytes are not randomly distributed and are influenced by both geographic and environment distances depending on the spatial scale analysed.     

Community structure in ichneumonid parasitoids at different spatial scales

The processes underlying parasitoid community structure are little known. Stochastic niche-apportionment models provide one route to underlying assemblage rules in this and other groups. Previous work has applied this approach to parasitoids found on single host species in single populations. However, parasitoid communities are known to extend across multiple hosts and scales. The patterns of relative abundances generated by five niche-apportionment models were compared to those observed in assemblages of two sub-families of the Ichneumonidae, the Diplazontinae and Pimplinae, at landscape and patch scales, Yorkshire, UK. Three of the five models produced patterns that were significantly different to the observed pattern for all taxonomic levels at both spatial scales. The Diplazontinae fit the random fraction (RF) model at the landscape scale in broadleaved woods. This suggests that hierarchical structuring and biotic interactions may play a role in the structuring of Diplazontinae assemblages at this scale. In contrast the Pimplinae fit the RF model only at the patch scale and only at one site. However, the Pimplini tribe (all chiefly parasitoids of Lepidoptera) fit the random assortment (RA) model at both the landscape and the patch scales, whilst the Ephialtini tribe (wide range of hosts) fit no model at either scale. The ecological interpretation of the RA model suggests that the Pimplini tribe is an unsaturated assemblage, where some of the total available resources are unused. Our results show, through the fit of mechanistic niche-apportionment models, that the processes that may structure ichneumonid parasitoid assemblages are not consistent across taxa and spatial scales.     

Placing the Effects of Leaf Litter Diversity on Saprotrophic Microorganisms in the Context of Leaf Type and Habitat

Because of conflicting results in previous studies, it is unclear whether litter diversity has a predictable impact on microbial communities or ecosystem processes. We examined whether effects of litter diversity depend on factors that could confound comparisons among previous studies, including leaf type, habitat type, identity of other leaves in the mixture, and spatial covariance at two scales within habitats. We also examined how litter diversity affects the saprotrophic microbial community using terminal restriction fragment length polymorphism to profile bacterial and fungal community composition, direct microscopy to quantify bacterial biomass, and ergosterol extraction to quantify fungal biomass. We found that leaf mixture diversity was rarely significant as a main effect (only for fungal biomass), but was often significant as an interaction with leaf type (for ash-free dry mass recovered, carbon-to-nitrogen ratio, fungal biomass, and bacterial community composition). Leaf type and habitat were significant as main effects for all response variables. The majority of variance in leaf ash-free dry mass and C/N ratio was explained after accounting for treatment effects and spatial covariation at the meter (block) and centimeter (litterbag) scales. However, a substantial amount of variability in microbial communities was left unexplained and must be driven by factors at other spatial scales or more complex spatiotemporal dynamics. We conclude that litter diversity effects are primarily dependent on leaf type, rather than habitat type or identity of surrounding leaves, which can guide the search for mechanisms underlying effects of litter diversity on ecosystem processes.     

海南岛霸王岭热带低地雨林树木的空间格局

树木空间格局及其形成过程是物种共存及生物多样性维持机制研究的一个重要方面。该文以海南岛两个1 hm ²的典型热带低地雨林老龄林森林动态样地为基础, 通过4个点格局模型(均质Poisson过程、异质Poisson过程、均质Thomas过程和异质Thomas过程)模拟扩散限制和生境异质性作用对树木空间分布格局的影响, 并分析不同空间尺度下(< 2 m, 2-5 m, 5-10 m, 10-15 m, 15-20 m和20-25 m)不同作用的相对重要性。结果表明: 热带低地雨林的所有树木总体上呈现聚集分布的空间格局, 随着尺度的增大, 聚集强度逐渐减小。树种在模拟空间分布格局最优模型中的比例由高到低分别是: 均质Thomas过程, 均质Poisson过程、异质Thomas过程和异质Poisson过程。扩散限制作用是形成热带低地雨林树木空间分布格局最重要的生态过程, 其次是完全随机作用以及生境异质性和扩散限制的联合作用, 而生境异质性的作用最小。不同空间尺度上模拟各树种空间分布格局的最优模型比例差异显著, 扩散限制作用能够在多数空间尺度上模拟多个树种的空间分布格局, 其次为随机作用; 生境异质性和扩散限制的联合作用主要在小尺度(0-5 m)影响树种分布, 而生境异质性在较大尺度(15-25 m)上影响树种的空间分布格局。     

Using hierarchical sampling to understand scales of spatial variation in early coral recruitment

Ecological patterns are created by processes acting over multiple spatial and temporal scales. By combining spatially explicit sampling with variance components models, the relative importance of spatial scale to overall variability can be determined. We used a spatially structured experimental design in the Mombasa Marine National Park in Kenya to quantify variation in coral recruitment across four spatial scales (~1–1,000 m) and to generate hypotheses about processes affecting recruitment and potential sources of post-settlement mortality during early life history. For the dominant recruiting corals (Pocillopora spp.), variation in recruitment on surfaces protected from fish grazing was greatest at the largest spatial scale examined (1,000 m). We hypothesize that recruitment on protected surfaces varies mainly with larval delivery due to different lagoonal circulation and water flow between sites. Conversely, variation on surfaces exposed to fishes was greatest at the smallest spatial scale (1 m). We hypothesize that recruitment on exposed surfaces mainly reflects local differences in the scale and intensity of fish grazing, which may obscure larval delivery patterns. Spatial variation in recruitment can affect many ecological processes and factors, including growth, survival to maturity, the distribution of habitat, and variation in species interaction strengths. This study demonstrates how spatially explicit sampling, followed by variance components modeling to partition variance across scales, can help to identify potential drivers of patterns at each relevant scale.     

Scaling processes and problems

A personal view is presented of current approaches to scaling processes and variables in the context of better understanding ecosystem function and predicting the consequences of global environmental change. Issues considered include spatial and temporal scales of interest, the scaling process, scaling strategy, scaling problems, heterogeneity, patchiness and non-linearity, aggregation methodology and feedbacks. Knowledge of processes in plants and vegetation is largely at small scales. The transfer of this knowledge up to larger spatial and longer temporal scales is an open-ended process with potential errors arising from heterogeneity and patchiness in the distribution of processes and non-linearities in the functional relationships between processes and environmental variables. Scaling now covers several orders of magnitude with respect to spatial and temporal scales with attendant risks of propagating errors. At larger scales the wide diversity of vegetation classes poses a problem, and it is suggested that this can be countered by classifying classes of vegetation (not species) into a small number of ‘functional types’ of vegetation. Scaling through summation of component processes and through derivation of appropriately averaged parameters is considered. However, the increasing role of feedbacks at larger spatial and longer temporal scales is an essential feature of the scaling process. Thus, understanding the feedbacks and including them in upscaling schemes is a major priority. A scaling strategy is outlined to minimize the propagation of errors. Because the scaling process is open-ended it is essential that good models are used and tested at each increase in scale.