sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

Colonization and extinction of ant communities in a fragmented landscape

Abstract In this paper we tested the assumption that smaller and more isolated remnants receive fewer ant colonizers and lose more species. We also tested hypotheses to explain such a pattern. We sampled ants in Brazil for 3 years in 18 forest remnants and in 10 grasslands between them. We tested the influence of remnant area and isolation on colonization rate, as well as the effect of remnant area on extinction rate. We tested the correlation between remnant area and isolation to verify the landscape design. Colonization rate was not affected by remnant area or isolation. Extinction rate, however, was smaller in larger remnants. Remnant area and isolation were negatively correlated. We tested two hypotheses related to the decrease in ant species extinction rate with increased remnant area: (i) small remnants support smaller and more extinction‐prone populations; and (ii) small remnants are more often invaded by generalist species, which suffer higher extinction inside remnants. The density of ant populations significantly increased with area. Generalist species presented a lower colonization rate in larger remnants, contrary to the pattern observed in forest species. Generalist species suffered more extinction than expected inside remnants. The lack of response of colonization rate to remnant area can be explained by the differential colonization by generalist and forest species. The decrease of ant population density in smaller remnants could be related to loss of habitat quality or quantity. The higher colonization by generalist ant species in the smaller remnants could be related to landscape design, because smaller remnants are more similar to the matrix than larger ones. Our results have important implications for conservation strategies because small remnants seem to be more affected by secondary effects of fragmentation, losing more forest species and being invaded more often by generalist species. Studies that compare only species richness between remnants cannot detect such patterns in species composition.     

Lizard predation alters the effect of habitat area on the species richness of insect assemblages on Bahamian isles

Aim Anolis lizard invasions are a serious threat world‐wide, and information about how this invasive predator affects the diversity of prey assemblages is important for many strategic conservation goals. It is hypothesized that these predators reduce the slope of species–area relationships (SARs) of their prey assemblages. The effects of island area and predation by anolis lizards on the species richness of insular insect assemblages were investigated. Location Twenty‐four isles around Staniel Cay, Exuma Cays, Bahamas. Methods Flying insects were sampled using half‐sized Malaise traps for three consecutive days on each island in May 2007. First, the effect of island area on the probability of lizard presence was evaluated. Then, the effects of the presence–absence of predatory lizards on SARs were analysed for the overall insect assemblage and for the assemblages of five dominant insect orders. Results Our results indicated that anolis lizards occurred primarily on larger islands. The species richness of the overall insect assemblage and five dominant insect orders significantly increased with island area. The interaction between island area and predator presence–absence significantly affected the overall insect assemblage and Diptera and Hymenoptera assemblages (but not Coleoptera, Hemiptera and Lepidoptera assemblages). The presence of predators caused decreases in the slope of the SARs. Main conclusions The presence of predatory lizards strongly affects species richness of insular insect assemblages with the island area being a crucial determinant of the species richness. Therefore, the slope of the SAR can serve as a measure of the consequence of invasive predatory species on native insect assemblages.     

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.     

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.     

Functional redundancy modifies species–area relationship for freshwater phytoplankton

Although species–area relationship (SAR ) is among the most extensively studied patterns in ecology, studies on aquatic and/or microbial systems are seriously underrepresented in the literature. We tested the algal SAR in lakes, pools and ponds of various sizes (10^?2–10⁸ m²) and similar hydromorphological and trophic characteristics using species‐specific data and functional groups. Besides the expectation that species richness increases monotonously with area, we found a right‐skewed hump‐shaped relationship between the area and phytoplankton species richness. Functional richness however did not show such distortion. Differences between the area dependence of species and functional richness indicate that functional redundancy is responsible for the unusual hump‐backed SAR . We demonstrated that the Small Island Effect, which is a characteristic for macroscopic SAR s can also be observed for the phytoplankton. Our results imply a so‐called large lake effect, which means that in case of large lakes, wind‐induced mixing acts strongly against the habitat diversity and development of phytoplankton patchiness and finally results in lower phytoplankton species richness in the pelagial. High functional redundancy of the groups that prefer small‐scale heterogeneity of the habitats is responsible for the unusual humpback relationship. The results lead us to conclude that although the mechanisms that regulate the richness of both microbial communities and communities of macroscopic organisms are similar, their importance can be different in micro‐ and macroscales.     

Island biogeography of Caribbean coral reef fish

Aim The goal of our study was to test fundamental predictions of biogeographical theories in tropical reef fish assemblages, in particular relationships between fish species richness and island area, isolation and oceanographic variables (temperature and productivity) in the insular Caribbean. These analyses complement an analogous and more voluminous body of work from the tropical Indo‐Pacific. The Caribbean is more limited in area with smaller inter‐island distances than the Indo‐Pacific, providing a unique context to consider fundamental processes likely to affect richness patterns of reef fish. Location Caribbean Sea. Methods We compiled a set of data describing reef‐associated fish assemblages from 24 island nations across the Caribbean Sea, representing a wide range of isolation and varying in land area from 53 to 110,860 km². Regression‐based analyses compared the univariate and combined effects of island‐specific physical predictors on fish species richness. Results We found that diversity of reef‐associated fishes increases strongly with increasing island area and with decreasing isolation. Richness also increases with increasing nearshore productivity. Analyses of various subsets of the entire data set reveal the robustness of the richness data and biogeographical patterns. Main conclusions Within the relatively small and densely packed Caribbean basin, fish species richness fits the classical species–area relationship. Richness also was related negatively to isolation, suggesting direct effects of dispersal limitation in community assembly. Because oceanic productivity was correlated with isolation, however, the related effects of system‐wide productivity on richness cannot be disentangled. These results highlight fundamental mechanisms that underlie spatial patterns of biodiversity among Caribbean coral reefs, and which are probably also are functioning in the more widespread and heterogeneous reefs of the Indo‐Pacific.     

Robust methods for detecting a small island effect

Aim The small island effect (SIE), i.e. the hypothesis that species richness below a certain threshold area varies independently of island size, has become a widely accepted part of the theory of island biogeography. However, there are doubts whether the findings of SIEs were based on appropriate methods. The aim of this study was thus to provide a statistically sound methodology for the detection of SIEs and to show this by re‐analysing data in which an SIE has recently been claimed ( Sfenthourakis & Triantis, 2009 , Diversity and Distributions, 15 , 131–140). Location Ninety islands of the Aegean Sea (Greece). Methods First, I reviewed publications on SIEs and evaluated their methodology. Then, I fitted different species–area models to the published data of area (A) and species richness (S) of terrestrial isopods (Oniscidea), with log A as predictor and both S (logarithm function) and log S (power function) as response variables: (i) linear; (ii) quadratic; (iii) cubic; (iv) breakpoint with zero slope to the left (SIE model); (v) breakpoint with zero slope to the right; (vi) two‐slope model. I used non‐linear regression with R²_adj., AIC_c and BIC as goodness‐of‐fit measures. Results Many different methods have been applied for detecting SIEs, all of them with serious shortcomings. Contrary to the claim of the original study, no SIE occurs in this particular dataset as the two‐slope variants performed better than the SIE variants for both the logarithm and power functions. Main conclusions For the unambiguous detection of SIEs, one needs to (i) include islands with no species; (ii) compare all relevant models; and (iii) account for different model complexities. As none of the reviewed SIE studies met all these criteria, their findings are dubious and SIEs may be less common than reported. Thus, conservation‐related predictions based on the assumption of SIEs may be unreliable.     

Lack of definition of mathematical terms in ecology: The case of the sigmoid class of functions in macro‐ecology

Defining mathematical terms and objects is a constant issue in ecology; often definitions are absent, erroneous, or imprecise. Through a bibliographic prospection, we show that this problem appears in macro‐ecology (biogeography and community ecology) where the lack of definition for the sigmoid class of functions results in difficulties of interpretation and communication. In order to solve this problem and to help harmonize papers that use sigmoid functions in ecology, herein we propose a comprehensive definition of these mathematical objects. In addition, to facilitate their use, we classified the functions often used in the ecological literature, specifying the constraints on the parameters for the function to be defined and the curve shape to be sigmoidal. Finally, we interpreted the different properties of the functions induced by the definition through ecological hypotheses in order to support and explain the interest of such functions in ecology and more precisely in biogeography.     

The influence of dispersal on macroecological patterns of Lesser Antillean birds

Aim Dispersal is often assumed to be a major force in shaping macroecological patterns, but this is rarely tested. Here I describe macroecological patterns for two groups of Lesser Antillean birds and then use population genetic data to assess if differences in dispersal ability could be responsible for the groups’ contrasting patterns. Importantly, the population genetic data are derived independently from any data used to generate the macroecological patterns. Location The Lesser Antilles, Caribbean. Methods I used data from the literature to construct species–area curves and evaluate the decline in species compositional similarity with geographic distance (hereafter distance–decay) for two sets of bird communities in the Lesser Antilles, those found in rain forest and those in dry forest. I then used mitochondrial DNA sequences from island populations to assess the dispersal ability of rain forest and dry forest species. Results Rain forest species show steeper species–area curves and greater distance–decay in community similarity than dry forest species, patterns that could be explained by rain forest species having more limited dispersal ability. Both conventional analyses of M, the number of migrants per generation between populations, and alternative analyses of D_A, the genetic distance between populations, suggest that rain forest species disperse between islands less frequently than dry forest species. Main conclusions Differences in dispersal ability are a plausible explanation for the contrasting macroecological patterns of rain forest and dry forest species. Additionally, historical factors, such as the taxon cycle and Pleistocene climate fluctuations, may have played a role in shaping the distribution patterns of Lesser Antillean birds.