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Summary1. Competition theory predicts that community structure may be shaped by resource partitioning between co-occurring species. As such, quantifying the degree of resource partitioning (i.e., niche overlap) is a key component of studies examining community structure and species coexistence. 2. For many organisms, multiple resource axes quantify niche space. Each axis may be described by a different type of data (e.g. categorical, continuous, count or binary data, as well as electivity scores), with different data types requiring different statistical treatments. Therefore, incorporating multiple axes into a single measure of niche space is problematic. 3. Here, we propose general methods for combining multiple niche axes, each characterized by different data types, within a unified analysis of niche overlap. Using appropriate transformations and probability models, we show that each data type can give rise to directly comparable measures of niche overlap, with the overlap statistic between two species defined as the overlapping area between the distributions for each species. 4. Measurements derived from different types of data can be combined into a single unified analysis of niche overlap by averaging over multiple axes. 5. We then describe null model permutation tests that assess statistical differences in niche overlap, which can address questions commonly posed by population ecologists (e.g. do two species occupy different niche space?) and community ecologists (e.g. are multiple species evenly distributed across niche space?). 6. To illustrate the use of these newly devised indices, we use an example from reef fishes that combines ratio, categorical and electivity data, and an example from alpine plants that combines continuous and ratio data. 7. The methods described in this article are relevant to a wide variety of ecological projects, including the investigation of invasive species, relative abundance distributions, global change, species coexistence and evolutionary diversification.

Many communities experience repeated periods of colonization due to seasonally regenerating habitats or pulsed arrival of young-of-year. When an individual's persistence in a community depends upon the strength of competitive interactions, changes in the timing of arrival relative to the arrival of a competitor can modify competitive strength and, ultimately, establishment in the community. We investigated whether the strength of intracohort competitive interactions between recent settlers of the reef fishes Thalassoma hardwicke and T. quinquevittatum are dependent on the sequence and temporal separation of their arrival into communities. To achieve this, we manipulated the sequence and timing of arrival of each species onto experimental patch reefs by simulating settlement pulses and monitoring survival and aggressive interactions. Both species survived best in the absence of competitors, but when competitors were present, they did best when they arrived at the same time. Survival declined as each species entered the community progressively later than its competitor and as aggression by its competitor increased. Intraspecific effects of resident T. hardwicke were similar to interspecific effects. This study shows that the strength of competition depends not only on the identity of competitors, but also on the sequence and timing of their interactions, suggesting that when examining interaction strengths, it is important to identify temporal variability in the direction and magnitude of their effects. Furthermore, our findings provide empirical evidence for the importance of competitive lotteries in the maintenance of species diversity in demographically open marine systems.

The importance of prey density in modifying predator foraging behavior, and hence community dynamics, has a rich history in ecology (Nicholson and Bailey 1935, Holling 1959). For example, shifts in a predator's foraging behavior can affect the stability of predator-prey dynamics (Deangelis et al. 1975), spatial distribution of predators (Van Der Meer and Ens 1997), food chain length (Schmitz 1992), and the strength of species interactions in complex food webs (Novak and Wootton 2008). The functional response represents mechanisms underlying the preypredator interaction; thus, quantifying changes in functional response parameters is a natural way to test hypotheses about these mechanisms. Shifts in predator foraging response can occur due to predator-predator interactions (e.g. intraguild predation, interference competition, or cooperative hunting by predators) (Skalski and Gilliam 2001) as well as common or conflicting behavioral responses of prey to multiple predators (Sih et al. 1998). Predatorinduced benefits of group living are common for a number of taxa, from caribou (Wittmer et al. 2005) to cliff swallows (Brown and Brown 2003) to queen conch (Ray and Stoner 1994), and active group formation by prey in the presence of predators (e.g. schools of fish and herds of savannah ungulates) or group formation of predators hunting prey can stabilize predator-prey dynamics by modifying the functional response (Fryxell et al. 2007). Below we describe a first experiment where we estimate how two components of a predator's functional response (attack rate and handling time) vary under different predator densities, and link these changes to mechanistic hypotheses.Functional responses can also be used to study the effect of multiple prey species on predator foraging behavior (Murdoch 1973, Golubski and Abrams 2011). For example, an alternative prey species can increase predator C h o i c e E d i t o r ' s OIKOS SynthesisPredation risk experienced by individuals living in groups depends on the balance between predator dilution, competition for refuges, and predator interference or synergy. These interactions operate between prey species as well: the benefits of group living decline in the presence of an alternative prey species. We apply a novel model-fitting approach to data from field experiments to distinguish among competing hypotheses about shifts in predator foraging behavior across a range of predator and prey densities. Our study provides novel analytical tools for analyzing predator foraging behavior and offers insight into the processes driving the dynamics of coral reef fish.Studies of predator foraging behavior typically focus on single prey species and fixed predator densities, ignoring the potential importance of complexities such as predator dilution; predator-mediated effects of alternative prey; heterospecific competition; or predator-predator interactions. Neglecting the effects of prey density is particularly problematic for prey species that live in mixed species groups, where the beneficial effects of...

AimProducing quantitative descriptions of large‐scale biodiversity patterns is challenging, particularly where biological sampling is sparse or inadequate. This issue is particularly problematic in marine environments, where sampling is both difficult and expensive, often resulting in patchy and/or uneven coverage. Here, we evaluate the ability of Gradient Forest (GF) modelling to describe broad‐scale marine biodiversity patterns, using a large dataset that also provided opportunity to investigate the effects of sample size on model stability.LocationNew Zealand's Extended Continental Shelf to depths of 2,000 m.MethodsGF models were used to analyse and predict spatial patterns of demersal fish species turnover (beta diversity) using an extensive demersal fish dataset (>27,000 research trawls) and high‐resolution environmental data layers (1 km2 grid resolution). GF models were fitted using various sized, mutually exclusive subsets of the demersal fish data to explore the effect of variation in numbers of training observations on model performance and stability. A final GF model using 13,917 samples was used to transform the environmental layers, which were then classified to produce 30 spatial groups; the ability of these groups to identify fish samples with similar composition was evaluated using independent sample data.ResultsModel fitting using varying sized subsets of the data indicated only minimal changes in model outcomes when using >7,000 observations. A multiscale spatial classification of marine environments created using results from a final GF model fitted using ~14,000 samples was highly effective at summarizing spatial variation in both fish assemblage composition and species turnover.Main conclusionsThe hierarchical nature of the classification supports its use at varying levels of classification detail, which is advantageous for conservation planning at differing spatial scales. This approach also facilitates the incorporation of information on intergroup similarities into conservation planning, allowing greater protection of distinctive groups likely to support unusual assemblages of species.