Six types of species-area curves

Macroecological studies infer ecological processes based on observed patterns. An often used measure of pattern is the species‐area curve. Insufficient attention has been paid to the variety of methods used to construct those curves. There are six different methods based on different combinations of: (1) the pattern of quadrats or areas sampled (nested, contiguous, noncontiguous, or island); (2) whether successively larger areas are constructed in a spatially explicit fashion or not; and (3) whether the curve is constructed from single values or mean values. The resulting six types of curves differ in their shapes, how diversity is encapsulated, and the scales encompassed. Inventory diversity (α) can either represent a single value or a mean value, creating a difference in the focus of the measure. Differentiation diversity (β) can vary in the extent encompassed, and thus the spatial scale, depending on the pattern of quadrat placement. Species‐area curves are used for a variety of purposes: extrapolation, setting a common grain, and hypothesis testing. The six types of curves differ in how they are used or interpreted in these contexts. A failure to recognize these differences can result in improper conclusions. Further work is needed to understand the sampling and measurement properties of the different types of species‐area curves.     

Subjecting the theory of the small‐island effect to Ockham’s razor

Species–area curves from islands and other isolates often differ in shape from sample‐area curves generated from mainlands or sections of isolates (or islands), especially at finer scales. We examine two explanations for this difference: (1) the small‐island effect (SIE), which assumes the species–area curve is composed of two distinctly different curve patterns; and (2) a sigmoid or depressed isolate species–area curve with no break‐points (in arithmetic space). We argue that the application of Ockham’s razor – the principle that the simplest, most economical explanation for a hypothesis should be accepted over less parsimonious alternatives – leads to the conclusion that the latter explanation is preferable. We hold that there is no reason to assume the ecological factors or patterns that affect the shapes of isolate (or island) curves cause two distinctly different patterns. This assumption is not required for the alternative, namely that these factors cause a single (though depressed) isolate species–area curve with no break‐points. We conclude that the theory of the small‐island effect, despite its present standing as an accepted general pattern in nature, should be abandoned.     

Getting biodiversity intactness indices right: ensuring that 'biodiversity' reflects 'diversity'

The 2010 biodiversity target of a ‘significant reduction in the current rate of biodiversity loss’ presents challenges for effective measurement of changes in global/regional biodiversity. A simple ‘biodiversity intactness index’ (BII) is attractive in using available data and expert opinion, but is seen to be only weakly linked to ‘biodiversity’ in its usual sense of ‘variation’. An example illustrates how an improved BII score could result even when there are large species losses. A family of alternative biodiversity ‘representativeness indices’ better reflect variation. They use the same readily available information, plus simple species–area relationships (SAR) and genetic diversity curves. A new genetic‐diversity abundance‐fraction curve, like SAR, is linear in log–log space. The new representativeness indices incorporate, through range‐abundance curves, the abundance fraction estimates normally used for BII. Land use or climate change impacts therefore can reflect partial rather than total biodiversity losses at localities. Estimates of biodiversity gains/losses using these indices enable a novel regional planning‐based approach for addressing the 2010 target.     

Assessing biodiversity with species accumulation curves; inventories of small reptiles by pit‐trapping in Western Australia

Abstract We examined 11 non‐linear regression models to determine which of them best fitted curvilinear species accumulation curves based on pit‐trapping data for reptiles in a range of heterogeneous and homogenous sites in mesic, semi‐arid and arid regions of Western Australia. A well‐defined plateau in a species accumulation curve is required for any of the models accurately to estimate species richness. Two different measures of effort (pit‐trapping days and number of individuals caught) were used to determine if the measure of effort influenced the choice of the best model(s). We used species accumulation curves to predict species richness, determined the trapping effort required to catch a nominated percentage (e.g. 95%) of the predicted number of species in an area, and examined the relationship between species accumulation curves with diversity and rarity. Species richness, diversity and the proportion of rare species in a community influenced the shape of species accumulation curves. The Beta‐P model provided the best overall fit (highest r²) for heterogeneous and homogeneous sites. For heterogeneous sites, Hill, Rational, Clench, Exponential and Weibull models were the next best. For homogeneous habitats, Hill, Weibull and Chapman–Richards were the next best models. There was very little difference between Beta‐P and Hill models in fitting the data to accumulation curves, although the Hill model generally over‐estimated species richness. Most models worked equally well for both measures of trapping effort. Because the number of individuals caught was influenced by both pit‐trapping effort and the abundance of individuals, both measures of effort must be considered if species accumulation curves are to be used as a planning tool. Trapping effort to catch a nominated percentage of the total predicted species in homogeneous and heterogeneous habitats varied among sites, but even for only 75% of the predicted number of species it was generally much higher than the typical effort currently being used for terrestrial vertebrate fauna surveys in Australia. It was not possible to provide a general indication of the effort required to predict species richness for a site, or to capture a nominated proportion of species at a site, because species accumulation curves are heavily influenced by the characteristics of particular sites.     

Do commonly used indices of β‐diversity measure species turnover?

Abstract. Indices of β‐diversity are of two major types, (1) those that measure among‐plot variability in species composition independently of the position of individual plots on spatial or environmental gradients, and (2) those that measure the extent of change in species composition along predefined gradients, i.e. species turnover. Failure to recognize this distinction can lead to the inappropriate use of some β‐diversity indices to measure species turnover. Several commonly‐used indices of β‐diversity are based on Whittaker's β_W (β_W = γ/α, where γ is the number of species in an entire study area and α is the number of species per plot within the study area). It is demonstrated that these indices do not take into account the distribution of species on spatial or environmental gradients, and should therefore not be used to measure species turnover. The terms ‘β‐diversity’ and ‘species turnover’ should not be used interchangeably. Species turnover can be measured using matrices of compositional similarity and physical or environmental distances among pairs of study plots. The use of indices of β‐diversity and similarity‐distance curves is demonstrated using simulated data sets.     

A model for the species–area–habitat relationship

Aim To propose a model (the choros model) for species diversity, which embodies number of species, area and habitat diversity and mathematically unifies area per se and habitat hypotheses. Location Species richness patterns from a broad scale of insular biotas, both from island and mainland ecosystems are analysed. Methods Twenty‐two different data sets from seventeen studies were examined in this work. The r² values and the Akaike's Information Criterion (AIC) were used in order to compare the quality of fit of the choros model with the Arrhenius species–area model. The classic method of log‐log transformation was applied. Results In twenty of the twenty‐two cases studied, the proposed model gave a better fit than the classic species–area model. The values of z parameter derived from choros model are generally lower than those derived from the classic species–area equation. Main conclusions The choros model can express the effects of area and habitat diversity on species richness, unifying area per se and the habitat hypothesis, which as many authors have noticed are not mutually exclusive but mutually supplementary. The use of habitat diversity depends on the specific determination of the ‘habitat’ term, which has to be defined based on the natural history of the taxon studied. Although the values of the z parameter are reduced, they maintain their biological significance as described by many authors in the last decades. The proposed model can also be considered as a stepping‐stone in our understanding of the small island effect.     

Effect of species richness and relative abundance on the shape of the species accumulation curve

Abstract We explain how species accumulation curves are influenced by species richness (total number of species), relative abundance and diversity using computer‐generated simulations. Species richness defines the boundary of the horizontal asymptote value for a species accumulation curve, and the shape of the curve is influenced by both relative abundance and diversity. Simulations with a high proportion of rare species and a few abundant species have a species accumulation curve with a low ‘shoulder’ (inflection point on the ordinate axis) and a long upward slope to the asymptote. Simulations with a high proportion of relatively abundant species have a steeply rising initial slope to the species accumulation curve and plateau early. Diversity (as measured by Simpson's and Shannon–Weaver indices) for simulations is positively correlated with the initial slope of the species accumulation curve. Species accumulation curves cross when one simulation has a high proportion of both rare and abundant species compared with another that has a more even distribution of abundance among species.     

From plots to islands: species diversity at different scales

Aim To investigate how plant diversity of whole islands (‘gamma’) is related to alpha and beta diversity patterns among sampling plots within each island, thus exploring aspects of diversity patterns across scales. Location Nineteen islands of the Aegean Sea, Greece. Methods Plant species were recorded at both the whole‐island scale and in small 100 m² plots on each island. Mean plot species richness was considered as a measure of alpha diversity, and six indices of the ‘variation’‐type beta diversity were also applied. In addition, we partitioned beta diversity into a ‘nestedness’ and a ‘replacement’ component, using the total species richness recorded in all plots of each island as a measure of ‘gamma’ diversity. We also applied 10 species–area models to predict the total observed richness of each island from accumulated plot species richness. Results Mean alpha diversity was not significantly correlated with the overall island species richness or island area. The range of plot species richness for each island was significantly correlated with both overall species richness and area. Alpha diversity was not correlated with most indices of beta diversity. The majority of beta diversity indices were correlated with whole‐island species richness, and this was also true for the ‘replacement’ component of beta diversity. The rational function model provided the best prediction of observed island species richness, with Monod’s and the exponential models following closely. Inaccuracy of predictions was positively correlated with the number of plots and with most indices of beta diversity. Main conclusions Diversity at the broader scale (whole islands) is shaped mainly by variation among small local samples (beta diversity), while local alpha diversity is not a good predictor of species diversity at broader scales. In this system, all results support the crucial role of habitat diversity in determining the species–area relationship.     

Incorporating functional dissimilarities into sample‐based rarefaction curves: from taxon resampling to functional resampling

Question: Indices of functional diversity have been seen as the key for integrating information on species richness with measures that focus on those components of community composition related to ecosystem functioning. For comparing species richness among habitats on an equal‐effort basis, so‐called sample‐based rarefaction curves may be used. Given a study area that is sampled for species presence and absence in N plots, sample‐based rarefaction generates the expected number of accumulated species as the number of sampled plots increases from 1 to N. Accordingly, the question for this study is: can we construct a ‘functional rarefaction curve’ that summarizes the expected functional dissimilarity between species when n plots are drawn at random from a larger pool of N plots? Methods: In this paper, we propose a parametric measure of functional diversity that is obtained by combining sample‐based rarefaction techniques that are usually applied to species richness with Rao's quadratic diversity. For a given set of N presence/absence plots, the resulting measure summarizes the expected functional dissimilarity at an increasingly larger cumulative number of plots n (n≤N). Results and Conclusions: Due to its parametric nature, the proposed measure is progressively more sensitive to rare species with increasing plot number, thus rendering this measure adequate for comparing the functional diversity of species assemblages that have been sampled with variable effort.     

Towards a ‘Sea‐Level Sensitive’ dynamic model: impact of island ontogeny and glacio‐eustasy on global patterns of marine island biogeography

A synthetic model is presented to enlarge the evolutionary framework of the General Dynamic Model (GDM) and the Glacial Sensitive Model (GSM) of oceanic island biogeography from the terrestrial to the marine realm. The proposed ‘Sea‐Level Sensitive’ dynamic model (SLS) of marine island biogeography integrates historical and ecological biogeography with patterns of glacio‐eustasy, merging concepts from areas as diverse as taxonomy, biogeography, marine biology, volcanology, sedimentology, stratigraphy, palaeontology, geochronology and geomorphology. Fundamental to the SLS model is the dynamic variation of the littoral area of volcanic oceanic islands (defined as the area between the intertidal and the 50‐m isobath) in response to sea‐level oscillations driven by glacial–interglacial cycles. The following questions are considered by means of this revision: (i) what was the impact of (global) glacio‐eustatic sea‐level oscillations, particularly those of the Pleistocene glacial–interglacial episodes, on the littoral marine fauna and flora of volcanic oceanic islands? (ii) What are the main factors that explain the present littoral marine biodiversity on volcanic oceanic islands? (iii) How can differences in historical and ecological biogeography be reconciled, from a marine point of view? These questions are addressed by compiling the bathymetry of 11 Atlantic archipelagos/islands to obtain quantitative data regarding changes in the littoral area based on Pleistocene sea‐level oscillations, from 150 thousand years ago (ka) to the present. Within the framework of a model sensitive to changing sea levels, we discuss the principal factors affecting the geographical range of marine species; the relationships between modes of larval development, dispersal strategies and geographical range; the relationships between times of speciation, modes of larval development, ecological zonation and geographical range; the influence of sea‐surface temperatures and latitude on littoral marine species diversity; the effect of eustatic sea‐level changes and their impact on the littoral marine biota; island marine species–area relationships; and finally, the physical effects of island ontogeny and its associated submarine topography and marine substrate on littoral biota. Based on the SLS dynamic model, we offer a number of predictions for tropical, subtropical and temperate volcanic oceanic islands on how rates of immigration, colonization, in‐situ speciation, local disappearance, and extinction interact and affect the marine biodiversity around islands during glacials and interglacials, thus allowing future testing of the theory.     

Vascular plant species richness in relation to habitat diversity and island area in the Finnish Archipelago

Aim The aim of this study is to explore the interrelationships between island area, species number and habitat diversity in two archipelago areas. Location The study areas, Brunskär and Getskär, are located in an archipelago in south‐western Finland. Methods The study areas, 82 islands in Brunskär and 78 in Getskär, were classified into nine habitat types based on land cover. In the Brunskär area, the flora (351 species) was surveyed separately for each individual habitat on the islands. In the Getskär area, the flora (302 species) was surveyed on a whole‐island basis. We used standard techniques to analyse the species–area relationship on a whole‐island and a habitat level. We also tested our data for the small island effect (SIE) using breakpoint and path analysis models. Results Species richness was significantly associated with both island area and habitat diversity. Vegetated area in particular, defined as island area with the rock habitat subtracted, proved to be a strong predictor of species richness. Species number had a greater association with island area multiplied by the number of habitats than with island area or habitat number separately. The tests for a SIE in the species–area relationship showed the existence of a SIE in one of the island groups. No SIE could be detected for the species–vegetated area relationship in either of the island groups. The strength of the species–area relationship differed considerably between the habitats. Main conclusions The general principles of island biogeography apply well to the 160 islands in this study. Vascular plant diversity for small islands is strongly influenced by physiographic factors. For the small islands with thin and varying soil cover, vegetated area was the most powerful predictor of species richness. The species–area curves of various habitats showed large variations, suggesting that the measurement of habitat areas and establishment of habitat‐based species lists are needed to better understand species richness on islands. We found some evidence of a SIE, but it is debatable whether this is a ‘true’ SIE or a soil cover/habitat characteristics feature.     

Island biogeography of the Mediterranean sea: the species–area relationship for terrestrial isopods

Aim We looked at the biogeographical patterns of Oniscidean fauna from the small islands of the Mediterranean Sea in order to investigate the species–area relationship and to test for area‐range effects. Location The Mediterranean Sea. Methods We compiled from the literature a data set of 176 species of Oniscidea (terrestrial isopods) distributed over 124 Mediterranean islands. Jaccard's index was used as input for a UPGMA cluster analysis. The species–area relationship was investigated by applying linear, semi‐logarithmic, logarithmic and sigmoid models. We also investigated a possible ‘small island effect’ (SIE) by performing breakpoint regression. We used a cumulative and a sliding‐window approach to evaluate scale‐dependent area‐range effects on the log S/log A regression parameters. Results Based on similarity indexes, results indicated that small islands of the Mediterranean Sea can be divided into two major groups: eastern and western. In general, islands from eastern archipelagos were linked together at similarity values higher than those observed for western Mediterranean islands. This is consistent with a more even distribution of species in the eastern Mediterranean islands. Separate archipelagos in the western Mediterranean could be discriminated, with the exception of islets, which tended to group together at the lowest similarity values regardless of the archipelago to which they belong. Islets were characterized by a few common species with large ranges. The species–area logarithmic model did not always provide the best fit. Most continental archipelagos showed very similar intercepts, higher than the intercept for the Canary island oceanic archipelago. Sigmoid regression returned convex curves. Evidence for a SIE was found, whereas area‐range effects that are dependent on larger scale analyses were not unambiguously supported. Main conclusions The Oniscidea fauna from small islands of the Mediterranean Sea is highly structured, with major and minor geographical patterns being identifiable. Some but not all of the biogeographical complexity can be explained by interpreting the different shapes of species–area curves. Despite its flexibility, the sigmoid model tested did not always provide the best fit. Moreover, when the model did provide a good fit the curves looked convex, not sigmoid. We found evidence for a SIE, and minor support for scale‐dependent area‐range effects.     

Species–area relationships in the Andaman and Nicobar Islands emerge because rarer species are disproportionately favored on larger islands

The island species–area relationship (ISAR) describes how the number of species increases with increasing size of an island (or island‐like habitat), and is of fundamental importance in island biogeography and conservation. Here, we use a framework based on individual‐based rarefaction to infer whether ISARs result from passive sampling, or whether some processes are acting beyond sampling (e.g., disproportionate effects and/or habitat heterogeneity). Using data on total and relative abundances of four taxa (birds, butterflies, amphibians, and reptiles) from multiple islands in the Andaman and Nicobar archipelago, we examine how different metrics of biodiversity (total species richness, rarefied species richness, and abundance‐weighted effective numbers of species emphasizing common species) vary with island area. Total species richness increased for all taxa, as did rarefied species richness controlling for a given sampling effort. This indicates that the ISAR did not result because of passive sampling, but that instead, some species were disproportionately favored on larger islands. For birds, frogs, and lizards, this disproportionate effect was only associated with species that were rarer in the samples, but for butterflies, both more common and rarer species were affected. Furthermore, for the two taxa for which we had plot‐level data (reptiles and amphibians), within‐island β‐diversity did not increase with island size, suggesting that within‐island compositional effects were unlikely to be driving these ISARs. Overall, our results indicate that the ISARs of these taxa are most likely driven by disproportionate effects, that is, where larger islands are important sources of biodiversity beyond a simple sampling expectation, especially through their influence on rarer species, thus emphasizing their role in the preservation and conservation of species.     

Non‐native species have multiple abundance–impact curves

The abundance–impact curve is helpful for understanding and managing the impacts of non‐native species. Abundance–impact curves can have a wide range of shapes (e.g., linear, threshold, sigmoid), each with its own implications for scientific understanding and management. Sometimes, the abundance–impact curve has been viewed as a property of the species, with a single curve for a species. I argue that the abundance–impact curve is determined jointly by a non‐native species and the ecosystem it invades, so that a species may have multiple abundance–impact curves. Models of the impacts of the invasive mussel Dreissena show how a single species can have multiple, noninterchangeable abundance–impact curves. To the extent that ecosystem characteristics determine the abundance–impact curve, abundance–impact curves based on horizontal designs (space‐for‐time substitution) may be misleading and should be used with great caution, it at all. It is important for scientists and managers to correctly specify the abundance–impact curve when considering the impacts of non‐native species. Diverting attention from the invading species to the invaded ecosystem, and especially to the interaction between species and ecosystem, could improve our understanding of how non‐native species affect ecosystems and reduce uncertainty around the effects of management of populations of non‐native species.     

Mollusc community patterns and species response curves along a mineral richness gradient: a case study in fens

Aim The goals of this study were to: (1) compare water conductivity and pH as proxy measures of mineral richness in relation to mollusc assemblages in fens, (2) examine the patterns of mollusc species richness along the gradient of mineral richness based on these factors, (3) model species–response curves and analyse calcicole–calcifuge behaviour of molluscs, and (4) compare the results with those from other studies concerning non‐marine mollusc ecology. Location Altogether, 135 treeless spring fen sites were sampled within the area of the Western Carpathians (east Czech Republic, north‐west Slovakia and south Poland; overall extent of study area was 12,000 km²). Methods Mollusc communities were recorded quantitatively from a homogeneous area of 16 m². Water conductivity and pH were measured in the field. The patterns of local species diversity along selected gradients, and species–response curves, were modelled using generalized linear models (GLM) and generalized additive models (GAM), both using the Poisson distribution. Results When the most acid sites (practically free of molluscs) were excluded, conductivity expressed the sites’ mineral richness and base saturation within the entire gradient, in contrast to pH. In the base‐rich sites, pH did not correlate with mineral richness. A unimodal response of local species diversity to mineral richness (expressed as conductivity) was found. In the extremely mineral‐rich, tufa‐forming sites (conductivity > 600 μS cm^?1) a decrease in species diversity was encountered. Response curves of the most common species showed clear differentiation of their niches. Significant models of either unimodal or monotonic form were fitted for 18 of the 30 species analysed. Species showed five types of calcicole–calcifuge behaviour: (1) a decreasing monotonic response curve and a preference for the really acid sites; (2) a skewed unimodal response curve with the optimum shifted towards the slightly acid sites; (3) a symmetrical unimodal model response curve with the optimum in the base‐rich sites, with no or slight tufa precipitation; (4) a skewed unimodal response curve but with the optimum shifted to the more mineral‐rich sites; and (5) an increasingly monotonic response curve, the optimum in the extremely base‐rich sites with strong tufa precipitation. Main conclusions Conductivity is the only reliable proxy measure of mineral richness across the entire gradient, within the confines of this study. This information is of great ecological significance in studies of fen mollusc communities. Species richness does not increase with increasing mineral richness along the entire gradient: only a few species are able to dwell in the extremely base‐rich sites. The five types of calcicole–calcifuge behaviour seen in species living in fens have a wider application: data published so far suggest they are also applicable to mollusc communities in other habitats.     

Species–area relationships and additive partitioning of diversity of native and nonnative herpetofauna of the West Indies

To evaluate the regional biogeographical patterns of West Indian native and nonnative herpetofauna, we derived and updated data on the presence/absence of all herpetofauna in this region from the recently published reviews. We divided the records into 24 taxonomic groups and classified each species as native or nonnative at each locality. For each taxonomic group and in aggregate, we then assessed the following: (1) multiple species–area relationship (SAR) models; (2) C‐ and Z‐values, typically interpreted to represent insularity or dispersal ability; and (3) the average diversity of islands, among‐island heterogeneity, γ‐diversity, and the contribution of area effect toward explaining among‐island heterogeneity using additive diversity partitioning approach. We found the following: (1) SARs were best modeled using the Cumulative Weibull and Lomolino relationships; (2) the Cumulative Weibull and Lomolino regressions displayed both convex and sigmoid curves; and (3) the Cumulative Weibull regressions were more conservative than Lomolino at displaying sigmoid curves within the range of island size studied. The Z‐value of all herpetofauna was overestimated by Darlington (Zoogeography: The geographic distribution of animals, John Wiley, New York, 1957), and Z‐values were ranked: (1) native > nonnative; (2) reptiles > amphibians; (3) snake > lizard > frog > turtle > crocodilian; and (4) increased from lower‐ to higher‐level taxonomic groups. Additive diversity partitioning showed that area had a weaker effect on explaining the among‐island heterogeneity for nonnative species than for native species. Our findings imply that the flexibility of Cumulative Weibull and Lomolino has been underappreciated in the literature. Z‐value is an average of different slopes from different scales and could be artificially overestimated due to oversampling islands of intermediate to large size. Lower extinction rate, higher colonization, and more in situ speciation could contribute to high richness of native species on large islands, enlarging area effect on explaining the between‐island heterogeneity for native species, whereas economic isolation on large islands could decrease the predicted richness, lowering the area effect for nonnative species. For most of the small islands less affected by human activities, extinction and dispersal limitation are the primary processes producing low species richness pattern, which decreases the overall average diversity with a large among‐island heterogeneity corresponding to the high value of this region as a biodiversity hotspot.     

Maximizing plant species inventory efficiency by means of remotely sensed spectral distances

Aim Inventorying plant species in an area based on randomly placed quadrats can be quite inefficient. The aim of this paper is to test whether plant species richness can be inventoried more efficiently by means of a spectrally‐based ordering of sites to be sampled. Location The study area was a complex wetland ecosystem, the Lake Montepulciano Nature Reserve, central Italy. This is one of the most important wetland areas of central Italy because of the diverse plant communities and the seasonal avifauna. Methods Field sampling, based on a random stratified sampling design, was performed in June 2002. Plant species composition was recorded within sampling units of 100 m² (plots) and 1 ha (macroplots). A QuickBird multispectral image of the same date was acquired and corrected both geometrically and radiometrically. Species accumulation curves based on spectral information were obtained by ordering sites to be sampled according to a maximum spectral distance criterion (i.e. by ordering sampling units based on the maximum distances among them in a four‐dimensional spectral space derived from the remotely sensed data). Different distance measures based on mean and maximum spectral distances among sampling units were tested. The performance of the species accumulation curve derived by the spectrally‐based ordering of sampling units was tested against a rarefaction curve obtained from the mean of 10,000 accumulation curves based on randomly ordered sampling units. Results The spectrally‐derived curve based on the maximum spectral distance among sampling units showed the most rapid accumulation of species, well above the rarefaction curve, at both the plot and the macroplot scales. Other ordering criteria of sampling units captured less richness over most of the species accumulation curves at both the spatial scales. The accumulation curves based on other measurements of distance were much closer to the random curve and did not show differences with respect to the species rarefaction curve based on random ordering of sampling units. Main conclusions The present investigation demonstrated that spectral‐based ordering of sites to be sampled can lead to the maximization of the efficiency of plant species inventories, an activity usually driven by the ‘botanist's internal algorithm’ (intuition), without any formalized rule to drive field sampling. The proposed approach can reduce costs of plant species inventorying through a more efficient allotment of time and sampling.     

Spatio‐temporal scaling of biodiversity and the species–time relationship in a stream fish assemblage

1. The increase of species richness with the area of the habitat sampled, that is the species–area relationship, and its temporal analogue, the species–time relationship (STR), are among the few general laws in ecology with strong conservation implications. However, these two scale‐dependent phenomena have rarely been considered together in biodiversity assessment, especially in freshwater systems. 2. We examined how the spatial scale of sampling influences STRs for a Central‐European stream fish assemblage (second‐order Bernecei stream, Hungary) using field survey data in two simulation‐based experiments. 3. In experiment one, we examined how increasing the number of channel units, such as riffles and pools (13 altogether), and the number of field surveys involved in the analyses (12 sampling occasions during 3 years), influence species richness. Complete nested curves were constructed to quantify how many species one observes in the community on average for a given number of sampling occasions at a given spatial scale. 4. In experiment two, we examined STRs for the Bernecei fish assemblage from a landscape perspective. Here, we evaluated a 10‐year reach level data set (2000–09) for the Bernecei stream and its recipient watercourse (third‐order Kemence stream) to complement results on experiment one and to explore the mechanisms behind the observed patterns in more detail. 5. Experiment one indicated the strong influence of the spatial scale of sampling on the accumulation of species richness, although time clearly had an additional effect. The simulation methodology advocated here helped to estimate the number of species in a diverse combination of spatial and temporal scale and, therefore, to determine how different scale combinations influence sampling sufficiency. 6. Experiment two revealed differences in STRs between the upstream (Bernecei) and downstream (Kemence) sites, with steeper curves for the downstream site. Equations of STR curves were within the range observed in other studies, predominantly from terrestrial systems. Assemblage composition data suggested that extinction–colonisation dynamics of rare, non‐resident (i.e. satellite) species influenced patterns in STRs. 7. Our results highlight that the determination of species richness can benefit from the joint consideration of spatial and temporal scales in biodiversity inventory surveys. Additionally, we reveal how our randomisation‐based methodology may help to quantify the scale dependency of diversity components (α, β, γ) in both space and time, which have critical importance in the applied context.     

Dispersal and diversification: macroevolutionary implications of the MacArthur–Wilson model, illustrated by Simulium (Inseliellum) Rubstov (Diptera: Simuliidae)

Aim Provide an empirical test of the ‘radiation zone’ hypothesis of the MacArthur–Wilson theory of island biogeography using the taxon‐pulse hypothesis of Erwin and Brooks Parsimony Analysis (BPA) on Simulium (Inseliellum) Rubstov. Location Micronesia, Cook Islands, Austral Islands, Society Islands, Marquesas Islands, Fiji and New Caledonia. Methods Primary and secondary BPA of the phylogeny of Inseliellum. Results Primary BPA showed that 15% of the taxon area cladogram contained area reticulations. Secondary BPA (invoking the area duplication convention) generated a clear sequence of dispersal for Inseliellum. The sequence follows a Micronesia – Cook Islands – Marquesas Islands – Society Islands dispersal, with a separate dispersal from the Cook Islands to the Austral Islands less than 1 Ma. A radiation in the island of Tahiti (Society Islands) produced numerous dispersals from Tahiti to other islands within the Society Islands system. Islands close to Tahiti (source island) have been colonized from Tahiti more often than islands far from Tahiti, but a higher proportion of those species colonizing distant islands have become distinct species. Main conclusions The dispersal sequence of Inseliellum exhibits both old to young island dispersal and young to old island dispersal. This is due to habitat availability on each island. Inseliellum is a model system in exemplifying the ‘radiation zone’ hypothesis of MacArthur and Wilson. As well, islands close to the source are colonized more often that those far from the source, but colonization of islands far away from the source results in a higher proportion of speciation events than for islands close to the source. The diversification of Inseliellum corresponds to a taxon‐pulse radiation, with a centre of diversification on Tahiti resulting from its large area and abundant freshwater habitats. This study illustrates the utility of BPA in identifying complex scenarios that can be used to test theories about the complementary roles of ecology and phylogeny in historical biogeography.     

A global analysis of avian island diversity–area relationships in the Anthropocene

Research on island species–area relationships (ISAR) has expanded to incorporate functional (IFDAR) and phylogenetic (IPDAR) diversity. However, relative to the ISAR, we know little about IFDARs and IPDARs, and lack synthetic global analyses of variation in form of these three categories of island diversity–area relationship (IDAR). Here, we undertake the first comparative evaluation of IDARs at the global scale using 51 avian archipelagic data sets representing true and habitat islands. Using null models, we explore how richness-corrected functional and phylogenetic diversity scale with island area. We also provide the largest global assessment of the impacts of species introductions and extinctions on the IDAR. Results show that increasing richness with area is the primary driver of the (non-richness corrected) IPDAR and IFDAR for many data sets. However, for several archipelagos, richness-corrected functional and phylogenetic diversity changes linearly with island area, suggesting that the dominant community assembly processes shift along the island area gradient. We also find that archipelagos with the steepest ISARs exhibit the biggest differences in slope between IDARs, indicating increased functional and phylogenetic redundancy on larger islands in these archipelagos. In several cases introduced species seem to have ‘re-calibrated’ the IDARs such that they resemble the historic period prior to recent extinctions.