Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.     

Biotic interactions influence the projected distribution of a specialist mammal under climate change

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.     

Predicting community characteristics from habitat conditions: fluvial fish and hydraulics

1. One current approach to the prediction of community characteristics is to use models of key local-scale processes (e.g. niche dimensions) affecting individuals and to estimate the effects of these attributes over larger scales. We tested this approach, focusing on how the hydraulic habitat structures fluvial fish communities. 2. We used a recent statistical habitat model to predict fish community characteristics in eleven reaches in the Rhône river basin in France. Predictions were made ‘blindly’ since most reaches were not used to calibrate the model. The model reflects species preferences for local hydraulics. We made predictions of the fish community from the local hydraulic conditions found in the reaches under low flow conditions. The overall abundance and the relative abundance (both as indices) of fish species, specific size classes and species traits (i.e. reproductive, trophic, morphological and others) were predicted. We summarized our predictions of the relative abundance of species as two ‘community structure indices’ using Principal Component Analysis. 3. Our predictions from low-flow hydraulics were compared with long-term observations of fish communities. The relative abundance of species actually observed depended largely on zoogeographic factors within the Rhône basin which could not be predicted by the model. The model predicted 13% of the variance in the indices of relative abundance at the species level and 23% of this variance at the trait level for all zoogeographic regions combined. However, when focused on reaches within a geographic region, the model explained up to 47% of the same variance. Therefore, geographic regions act as ‘filters’ on the relative abundance of species, but hydraulics do affect fish communities within a given geographical context. 4. For the synthetic ‘community structure indices’, we obtained good predictions from hydraulics independently of the geographical context (variance explained up to 95%). These indices were linked to simple key hydraulic characteristics of river reaches (Froude and/or Reynolds number). The indices enabled interpretations of the links between hydraulics, geomorphology, discharge and community patterns. These links were consistent with existing knowledge of species and their traits. 5. In addition to the above validations, the habitat model partly explained the observed effects of impoundment on fish communities. 6. The present results show that stream hydraulics strongly impact fish community structure. Consequently, our findings confirm that community characteristics can be predicted using models of the local-scale habitat requirements of the species forming the community.     

Integrating correlation between traits improves spatial predictions of plant functional composition

Functional trait composition is increasingly recognized as key to better understand and predict community responses to environmental gradients. Predictive approaches traditionally model the weighted mean trait values of communities (CWMs) as a function of environmental gradients. However, most approaches treat traits as independent regardless of known tradeoffs between them, which could lead to spurious predictions. To address this issue, we suggest jointly modeling a suit of functional traits along environmental gradients while accounting for relationships between traits. We use generalized additive mixed effect models to predict the functional composition of alpine grasslands in the Guisane Valley (France). We demonstrate that, compared to traditional approaches, joint trait models explain considerable amounts of variation in CWMs, yield less uncertainty in trait CWM predictions and provide more realistic spatial projections when extrapolating to novel environmental conditions. Modeling traits and their co‐variation jointly is an alternative and superior approach to predicting traits independently. Additionally, compared to a ‘predict first, assemble later’ approach that estimates trait CWMs post hoc based on stacked species distribution models, our ‘assemble first, predict later’ approach directly models trait‐responses along environmental gradients, and does not require data and models on species’ distributions, but only mean functional trait values per community plot. This highlights the great potential of joint trait modeling approaches in large‐scale mapping applications, such as spatial projections of the functional composition of vegetation and associated ecosystem services as a response to contemporary global change.     

Shade tolerance,canopy gaps and mechanisms of coexistence of forest trees

The belief that canopy gaps are important for the maintenance of tree species diversity appears to be widespread, but there have been no formal theoretical models to assess under what conditions gap phase processes allow coexistence. Much of the empirical research on niche differentiation in response to gaps has focused on evidence for an interspecific tradeoff between low light survival and high light growth. The objectives of this study are first to distinguish the possible mechanisms allowing coexistence based on this tradeoff, and second, to explore their limitations. We present a theory of forest dynamics driven by small‐scale disturbances as a special case of the theory of coexistence in variable environments. We demonstrate that temporal and spatial heterogeneity in light conditions that results from canopy gaps can allow stable coexistence as a result of three previously documented general mechanisms: ‘relative non‐linearity’, ‘the successional niche’ and the ‘storage effect’. We find that temporal fluctuations in light availability alone allow the stable coexistence of only two species. Spatial variation in disturbance synchronicity and intensity allows three species to coexist in a narrow parameter space. The rate of extinction is, however, extremely slow and there is transient coexistence of a larger number of species for a long period of time. We conclude that while the low light survival/high light growth tradeoff may be ubiquitous in forest tree species, it is unlikely to function as an important mechanism for the stable coexistence of several tree species.     

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola’s surviving endemic non‐volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country‐level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic‐only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic‐only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation‐priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation‐priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.     

Limiting similarity and functional diversity along environmental gradients

Recent developments in community models emphasize the importance of incorporating stochastic processes (e.g. ecological drift) in models of niche‐structured community assembly. We constructed a finite, spatially explicit, lottery model to simulate the distribution of species in a one‐dimensional landscape with an underlying gradient in environmental conditions. Our framework combines the potential for ecological drift with environmentally‐mediated competition for space in a heterogeneous environment. We examined the influence of niche breadth, dispersal distances, community size (total number of individuals) and the breadth of the environmental gradient on levels of species and functional trait diversity (i.e. differences in niche optima). Three novel results emerge from this model: (1) niche differences between adjacent species (e.g. limiting similarity) increase in smaller communities, because of the interaction of competitive effects and finite population sizes; (2) immigration from a regional species pool, stochasticity and niche‐assembly generate a bimodal distribution of species residence times (‘transient’ and ‘resident’) under a heterogeneous environment; and (3) the magnitude of environmental heterogeneity has a U‐shaped effect on diversity, because of shifts in species richness of resident vs. transient species. These predictions illustrate the potential importance of stochastic (although not necessarily neutral) processes in community assembly.     

Spatially structured intraspecific trait variation can foster biodiversity in disturbed,heterogeneous environments

Trait variation within populations is an important area of research for empirical and theoretical ecologists. While differences between individuals are doubtlessly ubiquitous, their role for species coexistence is much less clear and highly debated. Both unstructured (random) and structured (linked to space, time or inheritance) intraspecific trait variation (ITV) may modify species interactions with nontrivial consequences for emerging community compositions. In many ecosystems, these compositions are further driven by prevalent disturbance regimes. I therefore explored the effects of unstructured as well as spatially structured ITV under disturbances in a generic ecological model of competing sessile species. Using spatially explicit, individual‐based simulations, I studied how intraspecific variation in life history traits together with interspecific tradeoffs and disturbance regimes shape long‐term community composition. I found that 1) unstructured ITV does not affect species coexistence in the given context, 2) spatially structured ITV may considerably increase coexistence, but 3) spatially clumped disturbances reduce this effect of spatially structured ITV, especially if interspecific tradeoffs involve dispersal distance. The findings suggest that spatially structured ITV with individual trait responses to local habitat conditions differing among species may create or expand humps in disturbance–diversity relationships. Hence, if present, these forms of spatially structured ITV should be included in ecological models and will be important for reliably assessing community responses to environmental heterogeneity and change.     

Batesian mimics influence the evolution of conspicuousness in an aposematic salamander

Conspicuousness, or having high contrast relative to the surrounding background, is a common feature of unpalatable species. Several hypotheses have been proposed to explain the occurrence of conspicuousness, and while most involve the role of conspicuousness as a direct signal of unpalatability to potential predators, one hypothesis suggests that exaggerated conspicuousness may evolve in unpalatable species to reduce predator confusion with palatable species (potential Batesian mimics). This hypothesis of antagonistic coevolution between palatable and unpalatable species hinges on the ‘cost of conspicuousness’, in which conspicuousness increases the likelihood of predation more in palatable species than in unpalatable species. Under this mimicry scenario, four patterns are expected: (i) mimics will more closely resemble local models than models from other localities, (ii) there will be a positive relationship between mimic and model conspicuousness, (iii) models will be more conspicuous in the presence of mimics, and (iv) when models and mimics differ in conspicuousness, mimics will be less conspicuous than models. We tested these predictions in the salamander mimicry system involving Notophthalmus viridescens (model) and one colour morph of Plethodon cinereus (mimic). All predictions were supported, indicating that selection for Batesian mimicry not only influences the evolution of mimics, but also the evolution of the models they resemble. These findings indicate that mimicry plays a large role in the evolution of model warning signals, particularly influencing the evolution of conspicuousness.     

Do fitness-equalizing tradeoffs lead to neutral communities?

Neutral theory in ecology is aimed at describing communities where species coexist due to similarities rather than the classically posited niche differences. It assumes that all individuals, regardless of species identity, are demographically equivalent. However, Hubbell suggested that neutral theory may describe even niche communities because tradeoffs equalize fitness across species which differ in their traits. In fact, tradeoffs can involve stabilization as well as fitness equalization, and stabilization involves different dynamics and can lead to different community patterns than neutral theory. Yet the important question remains if neutral theory provides a robust picture of all fitness-equalized communities, of which communities with demographic equivalence are one special case. Here, I examine Hubbell’s suggestion for a purely fitness-equalizing interspecific birth–death tradeoff, expanding neutral theory to a theory describing this broader class of fitness-equalized communities. In particular, I use a flexible framework allowing examination of the influence of speciation dynamics. I find that the scaling of speciation rates with birth and death rates, which is poorly known, has large impacts on community structure. In most cases, the departure from the predictions of current neutral models is substantial. This work suggests that demographic and speciation complexities present a challenge to the future development and use of neutral theory in ecology as null model. The framework presented here will provide a starting point for meeting that challenge, and may also be useful in the development of stochastic niche models with speciation dynamics.     

Linking community assembly and structure across scales in a wild mouse parasite community

Understanding what processes drive community structure is fundamental to ecology. Many wild animals are simultaneously infected by multiple parasite species, so host–parasite communities can be valuable tools for investigating connections between community structures at multiple scales, as each host can be considered a replicate parasite community. Like free‐living communities, within‐host–parasite communities are hierarchical; ecological interactions between hosts and parasites can occur at multiple scales (e.g., host community, host population, parasite community within the host), therefore, both extrinsic and intrinsic processes can determine parasite community structure. We combine analyses of community structure and assembly at both the host population and individual scales using extensive datasets on wild wood mice (Apodemus sylvaticus) and their parasite community. An analysis of parasite community nestedness at the host population scale provided predictions about the order of infection at the individual scale, which were then tested using parasite community assembly data from individual hosts from the same populations. Nestedness analyses revealed parasite communities were significantly more structured than random. However, observed nestedness did not differ from null models in which parasite species abundance was kept constant. We did not find consistency between observed community structure at the host population scale and within‐host order of infection. Multi‐state Markov models of parasite community assembly showed that a host's likelihood of infection with one parasite did not consistently follow previous infection by a different parasite species, suggesting there is not a deterministic order of infection among the species we investigated in wild wood mice. Our results demonstrate that patterns at one scale (i.e., host population) do not reliably predict processes at another scale (i.e., individual host), and that neutral or stochastic processes may be driving the patterns of nestedness observed in these communities. We suggest that experimental approaches that manipulate parasite communities are needed to better link processes at multiple ecological scales.     

Creating individual accessible area hypotheses improves stacked species distribution model performance

Aim

Stacked species distribution models (SDMs) are an important step towards estimating species richness, but frequently overpredict this metric and therefore erroneously predict which species comprise a given community. We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often‐overlooked facet of ecological niche modelling.

Innovation

By limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas.

Main conclusions

Restricting a species’ training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non‐restricted stacked SDMs. These accessible area‐based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to ‘clip’ predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.     

Using landscape history to predict biodiversity patterns in fragmented landscapes

Landscape ecology plays a vital role in understanding the impacts of land‐use change on biodiversity, but it is not a predictive discipline, lacking theoretical models that quantitatively predict biodiversity patterns from first principles. Here, we draw heavily on ideas from phylogenetics to fill this gap, basing our approach on the insight that habitat fragments have a shared history. We develop a landscape ‘terrageny’, which represents the historical spatial separation of habitat fragments in the same way that a phylogeny represents evolutionary divergence among species. Combining a random sampling model with a terrageny generates numerical predictions about the expected proportion of species shared between any two fragments, the locations of locally endemic species, and the number of species that have been driven locally extinct. The model predicts that community similarity declines with terragenetic distance, and that local endemics are more likely to be found in terragenetically distinctive fragments than in large fragments. We derive equations to quantify the variance around predictions, and show that ignoring the spatial structure of fragmented landscapes leads to over‐estimates of local extinction rates at the landscape scale. We argue that ignoring the shared history of habitat fragments limits our ability to understand biodiversity changes in human‐modified landscapes.     

The mechanistic basis for higher‐order interactions and non‐additivity in competitive communities

Motivated by both analytical tractability and empirical practicality, community ecologists have long treated the species pair as the fundamental unit of study. This notwithstanding, the challenge of understanding more complex systems has repeatedly generated interest in the role of so‐called higher‐order interactions (HOIs) imposed by species beyond the focal pair. Here we argue that HOIs – defined as non‐additive effects of density on per capita growth – are best interpreted as emergent properties of phenomenological models (e.g. Lotka–Volterra competition) rather than as distinct ‘ecological processes’ in their own right. Using simulations of consumer‐resource models, we explore the mechanisms and system properties that give rise to HOIs in observational data. We demonstrate that HOIs emerge under all but the most restrictive of assumptions, and that incorporating non‐additivity into phenomenological models improves the quantitative and qualitative accuracy of model predictions. Notably, we also observe that HOIs derive primarily from mechanisms and system properties that apply equally to single‐species or pairwise systems as they do to more diverse communities. Consequently, there exists a strong mandate for further recognition of non‐additive effects in both theoretical and empirical research.     

Variation in Cleaner Wrasse Cooperation and Cognition: Influence of the Developmental Environment?

Deviations from model‐based predictions of strategies leading to stable cooperation between unrelated individuals have raised considerable debate in regards to decision‐making processes in humans. Here, we present data on cleaner wrasse (Labroides dimidiatus) that emphasize the importance of generalizing this discussion to other species, with the aim to develop a coherent theoretical framework. Cleaners eat ectoparasites and mucus off client fishes and vary their service quality based on a clients’ strategic behaviour. Hitherto, cognitive tasks designed to replicate such behaviour have revealed a strong link between cooperative behaviour and game theoretic predictions. However, we show that individuals from a specific location within our study site repeatedly failed to conform to the published evidence. We started exploring potential functional and mechanistic causes for this unexpected result, focusing on client composition, cleaner standard personality measures and ontogeny. We found that failing individuals lived in a socially simple environment. Decision rules of these cleaners ignored existing information in their environment (‘bounded rationality’), in contrast to cleaners living in a socially complex area. With respect to potential mechanisms, we found no correlations between differences in performance and differences in aggressiveness or boldness, in contrast to results on other cooperative species. Furthermore, juveniles from the two habitat types performed similarly, and better than the adults from the socially simple environment. We propose that variation in the costs and benefits of knowledge may affect a cleaners’ information acquisition and storage, which may explain our observed variation in cooperation and cognition.     

Combined disturbances and the role of their spatial and temporal properties in shaping community structure

Disturbances are characteristic for many ecosystems. However, we still lack generalizations concerning their role in shaping communities, particularly when disturbances co-occur. To study such effects, we used a novel modeling approach that is unrestricted by a priori tradeoffs among specific plant traits, except for those generated by allocation principles. Thus, trait combinations were emergent properties associated with biotic and abiotic constraints. Specifically, we asked which traits dominate under specific disturbance regimes, whether single and combined disturbance regimes promote similar trait tradeoffs and how complex disturbance regimes affect species richness and functional diversity. Overall, disturbances’ temporal properties governed the outcome of combined disturbances and were a stronger assortative force than spatial disturbance properties: low temporal predictability decreased seed-dispersability and dormancy, but increased competitive ability and disturbance tolerance. Evidence for tradeoffs between different colonization modes and between dormancy and disturbance tolerance were found, while surprisingly, the widely accepted colonization–competition tradeoff was not generated. Diversity was highest at intermediate disturbance intensity, but decreased monotonically with increasing unpredictability. In accordance with our results, future models should avoid restrictive assumptions about tradeoffs to generate robust and more general predictions about the role of disturbances for community dynamics.     

Intraspecific trait variation increases species diversity in a trait‐based grassland model

Intraspecific trait variation (ITV) is thought to play a significant role in community assembly, but the magnitude and direction of its influence are not well understood. Although it may be critical to better explain population persistence, species interactions, and therefore biodiversity patterns, manipulating ITV in experiments is challenging. We therefore incorporated ITV into a trait‐ and individual‐based model of grassland community assembly by adding variation to the plants’ functional traits, which then drive life‐history tradeoffs. Varying the amount of ITV in the simulation, we examine its influence on pairwise‐coexistence and then on the species diversity in communities of different initial sizes. We find that ITV increases the ability of the weakest species to invade most, but that this effect does not scale to the community level, where the primary effect of ITV is to increase the persistence and abundance of the competitively‐average species. Diversity of the initial community is also of critical importance in determining ITV's efficacy; above a threshold of interspecific diversity, ITV does not increase diversity further. For communities below this threshold, ITV mainly helps to increase diversity in those communities that would otherwise be low‐diversity. These findings suggest that ITV actively maintains diversity by helping the species on the margins of persistence, but mostly in habitats of relatively low alpha and beta diversity.     

Fungal infection alters the selection,dispersal and drift processes structuring the amphibian skin microbiome

Symbiotic microbial communities are important for host health, but the processes shaping these communities are poorly understood. Understanding how community assembly processes jointly affect microbial community composition is limited because inflexible community models rely on rejecting dispersal and drift before considering selection. We developed a flexible community assembly model based on neutral theory to ask: How do dispersal, drift and selection concurrently affect the microbiome across environmental gradients? We applied this approach to examine how a fungal pathogen affected the assembly processes structuring the amphibian skin microbiome. We found that the rejection of neutrality for the amphibian microbiome across a fungal gradient was not strictly due to selection processes, but was also a result of species‐specific changes in dispersal and drift. Our modelling framework brings the qualitative recognition that niche and neutral processes jointly structure microbiomes into quantitative focus, allowing for improved predictions of microbial community turnover across environmental gradients.     

Species‐specific responses to island connectivity cycles: refined models for testing phylogeographic concordance across a Mediterranean Pleistocene Aggregate Island Complex

The contribution of Pleistocene sea level changes to diversification patterns in archipelagos around the world, and specifically whether the repeated cycles of island connectivity and isolation acted as a ‘species pump’ is debated. The debate has been perpetuated in part because of the type of evidence used to evaluate the species‐pump hypothesis. Specifically, existing tests of the ‘Pleistocene Aggregate Island Complex’ (PAIC) model of diversification interpret the lack of concordant divergence times among multiple codistributed taxa as a rejection of the PAIC model. However, the null expectation of concordance disregards taxon‐specific ecological traits and geographic characteristics that may affect population persistence and gene flow among islands. Here, we study the factors affecting population divergence in thirteen flightless darkling beetle species (Coleoptera: Tenebrionidae) across the PAIC system of the Cycladic plateau in the Aegean archipelago. Based on isolation‐by‐resistance analyses, hierarchical amova and the degree of genealogical sorting on individual islands, we identify a major effect of bathymetry and habitat stability on the levels of genetic divergence across the PAIC, with island size and body size playing a secondary role as well. We subsequently use bathymetric maps and habitat association to generate predictions about the set of islands and group of taxa expected to show phylogeographic concordance. We test these predictions using hierarchical approximate Bayesian computation and show how our interpretations regarding the role of PAICs as drivers of divergence change when relying on a null expectation of concordance compared to a refined model that takes geography and ecological traits into account.     

Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds

Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey‐based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods Four species richness maps were compiled based on range maps, field‐derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad‐scale gradients in species diversity. Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large‐scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad‐scale patterns.