Re‐approaching the small island effect

Aim To propose a new approach to the small island effect (SIE) and a simple mathematical procedure for the estimation of its upper limit. The main feature of the SIE is that below an upper size threshold an increase of species number with increase of area in small islands is not observed. Location Species richness patterns from different taxa and insular systems are analysed. Methods Sixteen different data sets from 12 studies are analysed. Path analysis was used for the estimation of the upper limit of the SIE. We studied each data set in order to detect whether there was a certain island size under which the direct effects of area were eliminated. This detection was carried out through the sequential exclusion of islands from the largest to the smallest. For the cases where an SIE was detected, a log‐log plot of species number against area is presented. The relationships between habitat diversity, species number and area are studied within the limits of the SIE. In previous studies only area was used for the detection of the SIE, whereas we also encompass habitat diversity, a parameter with well documented influence on species richness, especially at small scales. Results An SIE was detected in six out of the 16 studied cases. The upper limit of the SIE varies, depending on the characteristics of the taxon and the archipelago under study. In general, the values of the upper limit of the SIE calculated according to the approach undertaken in our study differ from the values calculated in previous studies. Main conclusions Although the classical species–area models have been used to estimate the upper limit of the SIE, we propose that the detection of this phenomenon should be undertaken independently from the species–area relationship, so that the net effects of area are calculated excluding the surrogate action of area on other variables, such as environmental heterogeneity. The SIE appears when and where area ceases to influence species richness directly. There are two distinct SIE patterns: (1) the classical SIE where both the direct and indirect effects of area are eliminated and (2) the cryptic SIE where area affects species richness indirectly. Our approach offers the opportunity of studying the different factors influencing biodiversity on small scales more accurately. The SIE cannot be considered a general pattern with fixed behaviour that can be described by the same model for different island groups and taxa. The SIE should be recognized as a genuine but idiosyncratic phenomenon.     

Ant communities on small tropical islands: effects of island size and isolation are obscured by habitat disturbance and ‘tramp’ ant species

Aim Comparisons among islands offer an opportunity to study the effects of biotic and abiotic factors on small, replicated biological communities. Smaller population sizes on islands accelerate some ecological processes, which may decrease the time needed for perturbations to affect community composition. We surveyed ants on 18 small tropical islands to determine the effects of island size, isolation from the mainland, and habitat disturbance on ant community composition. Location Thousand Islands Archipelago (Indonesian name: Kepulauan Seribu) off Jakarta, West Java, Indonesia. Methods Ants were sampled from the soil surface, leaf litter and vegetation in all habitat types on each island. Island size, isolation from the mainland, and land‐use patterns were quantified using GIS software. The presence of settlements and of boat docks were used as indicators of anthropogenic disturbance. The richness of ant communities and non‐tramp ant species on each island were analysed in relation to the islands’ physical characteristics and indicators of human disturbance. Results Forty‐eight ant species from 5 subfamilies and 28 genera were recorded from the archipelago, and approximately 20% of the ant species were well‐known human‐commensal ‘tramp’ species. Islands with boat docks or human settlements had significantly more tramp species than did islands lacking these indicators of anthropogenic disturbance, and the diversity of non‐tramp species decreased with habitat disturbance. Main conclusions Human disturbance on islands in the Thousand Islands Archipelago promotes the introduction and/or establishment of tramp species. Tramp species affect the composition of insular ant communities, and expected biogeographical patterns of ant richness are masked. The island with the greatest estimated species richness and the greatest number of unique ant species, Rambut Island, is a forested bird sanctuary, highlighting the importance of protected areas in preserving the diversity of species‐rich invertebrate faunas.     

Richness and composition of plants and birds on land‐bridge islands: effects of island attributes and differential responses of species groups

Aim To test relationships between the richness and composition of vascular plants and birds and attributes of habitat fragments using a model land‐bridge island system, and to investigate whether the effects of fragmentation differ depending on species natural history traits. Location Thousand Island Lake, China. Methods We compiled presence/absence data of vascular plant and bird species through exhaustive surveys of 41 islands. Plant species were assigned to two categories: shade‐intolerant and shade‐tolerant species; bird species were assigned to three categories: edge, interior, and generalist species. We analysed the relationships between island attributes (area, isolation, elevation, shape complexity, and perimeter to area ratio) and species richness using generalized linear models (GLMs). We also investigated patterns of composition in relation to island attributes using ordination (redundancy analysis). Results We found that island area explained a high degree of variation in the species richness of all species groups. The slope of the species–area relationship (z) was 0.16 for all plant species and 0.11 for all bird species. The lowest z‐value was for generalist birds (0.04). The species richness of the three plant species groups was associated with island area per se, while that of all, generalist, and interior birds was explained mainly by elevation, and that of edge bird species was associated primarily with island shape. Patterns of species composition were most strongly related to elevation, island shape complexity, and perimeter to area ratio rather than to island area per se. Species richness had no significant relationship with isolation, but species composition did. We also found differential responses among the species groups to changes in island attributes. Main conclusions Within the Thousand Island Lake system, the effects of fragmentation on both bird and plant species appear to be scale‐dependent and taxon‐specific. The number of plant species occurring on an island is strongly correlated with island area, and the richness of birds and the species composition of plants and birds are associated with variables related to habitat heterogeneity. We conclude that the effects of fragmentation on species diversity and composition depend not only on the degree of habitat loss but also on the specific patterns of habitat fragmentation.     

Dung beetle assemblages on tropical land‐bridge islands: small island effect and vulnerable species

Aim Using dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) in a tropical land‐bridge island system, we test for the small island effect (SIE) in the species–area relationship and evaluate its effects on species richness and community composition. We also examine the determinants of species richness across island size and investigate the traits of dung beetle species in relation to their local extinction vulnerability following forest fragmentation. Location Lake Kenyir, a hydroelectric reservoir in north‐eastern Peninsular Malaysia. Methods We sampled dung beetles using human dung baited pitfall traps on 24 land‐bridge islands and three mainland sites. We used regression tree analyses to test for the SIE, as well as species traits related to local rarity, as an indication of extinction vulnerability. We employed generalized linear models (GLMs) to examine determinants for species richness at different scales and compared the results with those from conventional linear and breakpoint regressions. Community analyses included non‐metric multidimensional scaling, partial Mantel tests, nestedness analysis and abundance spectra. Results Regression tree analysis revealed an area threshold at 35.8 ha indicating an SIE. Tree basal area was the most important predictor of species richness on small islands (<35.8 ha). Results from GLMs supported these findings, with isolation and edge index also being important for small islands. The SIE also manifested in patterns of dung beetle community composition where communities on small islands (<35.8 ha) departed from those on the mainland and larger islands, and were highly variable with no significant nestedness, probably as a result of unexpected species occurrences on several small islands. The communities exhibited a low degree of spatial autocorrelation, suggesting that dispersal limitation plays a part in structuring dung beetle assemblages. Species with lower baseline density and an inability to forage on the forest edge were found to be rarer among sites and hence more prone to local extinction. Main conclusions We highlight the stochastic nature of dung beetle community composition on small islands and argue that this results in reduced ecosystem functionality. A better understanding of the minimum fragment size required for retaining functional ecological communities will be important for effective conservation management and the maintenance of tropical forest ecosystem stability.     

Marine island biogeography. Response to comment on ‘Island biogeography: patterns of marine shallow‐water organisms’

In this response we have incorporated data on gastropod and seaweed biodiversity referred to by Ávila et al. (2016, Journal of Biogeography, doi: 10.1111/jbi.12816 ) to allow an updated analysis on marine shallow‐water biogeography patterns. When compared to the biogeography patterns reported in Hachich et al. (2015, Journal of Biogeography, 42 , 1871–1882), we find (1) no differences in the patterns originally reported for reef fish or seaweeds, (2) minor differences in gastropod species–area and species–age patterns and (3) a significant difference for the gastropod species‐isolation pattern. In our original work, we reported that there was limited evidence that gastropod species richness was influenced by island isolation; however, our new analysis reveals a power‐model relationship between these variables. Thus, we are now able to conclude that gastropod species diversity, whose dispersal capacity is intermediate between seaweeds (lowest) and reef fish (highest), is also influenced by island isolation.     

Plants on small islands revisited: the effects of spatial scale and habitat quality on the species–area relationship

Understanding how species diversity is related to sampling area and spatial scale is central to ecology and biogeography. Small islands and small sampling units support fewer species than larger ones. However, the factors influencing species richness may not be consistent across scales. Richness at local scales is primarily affected by small‐scale environmental factors, stochasticity and the richness at the island scale. Richness at whole‐island scale, however, is usually strongly related to island area, isolation and habitat diversity. Despite these contrasting drivers at local and island scales, island species–area relationships (SARs) are often constructed based on richness sampled at the local scale. Whether local scale samples adequately predict richness at the island scale and how local scale samples influence the island SAR remains poorly understood. We investigated the effects of different sampling scales on the SAR of trees on 60 small islands in the Raja Ampat archipelago (Indonesia) using standardised transects and a hierarchically nested sampling design. We compared species richness at different grain sizes ranging from single (sub)transects to whole islands and tested whether the shape of the SAR changed with sampling scale. We then determined the importance of island area, isolation, shape and habitat quality at each scale on species richness. We found strong support for scale dependency of the SAR. The SAR changed from exponential shape at local sampling scales to sigmoidal shape at the island scale indicating variation of species richness independent of area for small islands and hence the presence of a small‐island effect. Island area was the most important variable explaining species richness at all scales, but habitat quality was also important at local scales. We conclude that the SAR and drivers of species richness are influenced by sampling scale, and that the sampling design for assessing the island SARs therefore requires careful consideration.     

Towards a more general species–area relationship: diversity on all islands, great and small

Aim

To demonstrate a new and more general model of the species–area relationship that builds on traditional models, but includes the provision that richness may vary independently of island area on relatively small islands (the small island effect).

Location

We analysed species–area patterns for a broad diversity of insular biotas from aquatic and terrestrial archipelagoes.

Methods

We used breakpoint or piecewise regression methods by adding an additional term (the breakpoint transformation) to traditional species–area models. The resultant, more general, species–area model has three readily interpretable, biologically relevant parameters: (1) the upper limit of the small island effect (SIE), (2) an estimate of richness for relatively small islands and (3) the slope of the species–area relationship (in semi‐log or log–log space) for relatively large islands.

Results

The SIE, albeit of varying magnitude depending on the biotas in question, appeared to be a relatively common feature of the data sets we studied. The upper limit of the SIE tended to be highest for species groups with relatively high resource requirements and low dispersal abilities, and for biotas of more isolated archipelagoes.

Main conclusions

The breakpoint species–area model can be used to test for the significance, and to explore patterns of variation in small island effects, and to estimate slopes of the species–area (semi‐log or log–log) relationship after adjusting for SIE. Moreover, the breakpoint species–area model can be expanded to investigate three fundamentally different realms of the species–area relationship: (1) small islands where species richness varies independent of area, but with idiosyncratic differences among islands and with catastrophic events such as hurricanes, (2) islands beyond the upper limit of SIE where richness varies in a more deterministic and predictable manner with island area and associated, ecological factors and (3) islands large enough to provide the internal geographical isolation (large rivers, mountains and other barriers within islands) necessary for in situ speciation.

     

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.