Inferring macro‐ecological patterns from local presence/absence data

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.     

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.     

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.     

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.     

Species dynamics alter community diversity–biomass stability relationships

The relationship between community diversity and biomass variability remains a crucial ecological topic, with positive, negative and neutral diversity–stability relationships reported from empirical studies. Theory highlights the relative importance of Species–Species or Species–Environment interactions in driving diversity–stability patterns. Much previous work is based on an assumption of identical (stable) species‐level dynamics. We studied ecosystem models incorporating stable, cyclic and more complex species‐level dynamics, with either linear or non‐linear density dependence, within a locally stable community framework. Species composition varies with increasing diversity, interacting with the correlation of species' environmental responses to drive either positive or negative diversity–stability patterns, which theory based on communities with only stable species‐level dynamics fails to predict. Including different dynamics points to new mechanisms that drive the full range of diversity–biomass stability relationships in empirical systems where a wider range of dynamical behaviours are important.     

Spatio‐temporal scaling of biodiversity in acoustic tropical bird communities

Automated analysis of acoustic communities is a rapidly emerging approach for the characterization and monitoring of biodiversity. To evaluate its utility, we should verify that such ‘bioacoustics’ can accurately detect ecological signal in spatiotemporal acoustic data. Targeting the ‘Biological Dynamics of Forest Fragments Project’ sites in Brazil, we ask: What is the relative contribution of the spatial, temporal and habitat dimension to variation in bird acoustic communities in a previously fragmented tropical rainforest? Does the functional diversity of bird communities scale similarly to space and time as does species diversity, when both are recorded by bioacoustics means? Overall, is the imprint of landscape fragmentation 30 years ago still audible in the present‐day soundscape? We sampled forty‐four sites in secondary forest and 107 sites in old‐growth forest, resulting in 11 000 h of audio recordings. We detected 60 bird species with satisfactory precision and recovered a linear log–log relation between sampling time and species diversity. Sites in primary forest host more species than sites in secondary forest, but the difference decreased with sampling time, as the slope was slightly higher in secondary than primary forests. Functional diversity, as exposed by vocalizing birds, accumulates faster than does species diversity. The similarity among local communities decreases with distance in both time and space, but stability in time is remarkably high: two acoustic samples from the same site one year (or more) apart prove more similar than two samples taken at the same time but from sites situated just a few hundred meters apart. These findings suggest that habitat modification can be heard as a long‐lasting imprint on the soundscape of regenerating habitats and identify soundscape–area and soundscape–time relations as a promising tool for biodiversity research, applied biomonitoring and restoration ecology.     

Global macroecology of bird assemblages in urbanized and semi‐natural ecosystems

Aim Despite the increasing pace of urbanization, little is known about how this process affects biodiversity globally. We investigate macroecological patterns of bird assemblages in urbanized areas relative to semi‐natural ecosystems. Location World‐wide. Methods We use a database of quantitative bird surveys to compare key assemblage structure parameters for plots in urbanized and semi‐natural ecosystems controlling for spatial autocorrelation and survey methodology. We use the term ‘urbanized’ instead of ‘urban’ ecosystems as many of the plots were not located in the centre of towns but in remnant habitat patches within conurbations. Results Some macroecological relationships were conserved in urbanized landscapes. Species–area, species–abundance and species–biomass relationships did not differ significantly between urbanized and non‐urbanized environments. However, there were differences in the relationships between productivity and assemblage structure. In forests, species richness increased with productivity; in both forests and open habitats, the evenness of species abundances declined as productivity increased. Among urbanized plots, instead, both species richness and the evenness of species abundances were independent of variation in productivity. Main conclusions Remnant habitats within urbanized areas are subject to many ecological alterations, yet key macroecological patterns differ remarkably little in urbanized versus non‐urbanized plots. Our results support the need for increased conservation activities in urbanized landscapes, particularly given the additional benefits of local experiences of biodiversity for the human population. With increasing urbanization world‐wide, broad‐scale efforts are needed to understand and manage the effects of this driver of change on biodiversity.     

Species–area relationships and biodiversity loss in fragmented landscapes

To estimate species loss from habitat destruction, ecologists typically use species–area relationships, but this approach neglects the spatial pattern of habitat fragmentation. Here, we provide new, easily applied, analytical methods that place upper and lower bounds on immediate species loss at any spatial scale and for any spatial pattern of habitat loss. Our formulas are expressed in terms of what we name the ‘Preston function’, which describes triphasic species–area relationships for contiguous regions. We apply our method to case studies of deforestation and tropical tree species loss at three different scales: a 50 ha forest plot in Panama, the tropical city‐state of Singapore and the Brazilian Amazon. Our results show that immediate species loss is somewhat insensitive to fragmentation pattern at small scales but highly sensitive at larger scales: predicted species loss in the Amazon varies by a factor of 16 across different spatial structures of habitat loss.     

Ground‐dwelling beetle assemblages in the northern Cape Floristic Region: Patterns,correlates and implications

Abstract Recent studies have both shown and predicted that global climate change will have a substantial influence on biodiversity. This is true especially of a global biodiversity hotspot, the Cape Floristic Region. Although the effects of predicted changes have been widely assessed for plants, little is known about how insect diversity in the region might be affected. In particular, patterns in and the correlates of diversity in the region are poorly understood, and therefore the likely affects of a changing abiotic environment on this significant group of organisms are not clear. Therefore, we investigate patterns in, and correlates of, epigaeic beetle (Tenebrionidae and Carabidae) diversity in one of the most climate change‐sensitive areas in the Cape Floristic Region, the Cederberg. In particular, we determine whether epigaeic beetle assemblage structure differs between the main vegetation types in the Cederberg (Strandveld, Mountain Fynbos and Succulent Karoo), how restricted these beetles are to specific vegetation types, and which environmental variables might be associated with site‐related differences in beetle richness and abundance. Sampling was undertaken during October 2002 and 2003 across an altitudinal gradient ranging from sea level (Lambert's Bay) to approximately 2000 m above sea level (Sneeukop, Cederberg) and down again to 500 m above sea level (Wupperthal) using pitfall traps. The environmental correlates of abundance and species density in the epigaeic beetles were similar to those identified previously for ants across the transect, with both taxa being positively related to several temperature variables. Several species showed habitat specificity and fidelity, and clear distinctions existed between the vegetation types across the transect. A larger proportion of the variance in tenebrionid species density was explained by environmental variables and spatial factors than for carabids. The most likely explanation for this difference is that the correlates might well reflect collinear historical processes, rather than a causal relationship between contemporary environmental variables and species density. If this is the case, it suggests that caution should be exercised when interpreting environmental correlates of species density, and making climate change predictions based on these correlates.     

No biogeographical pattern for a root‐associated fungal species complex

Aim The biogeography of microbes is poorly understood and there is an open debate regarding if and how microbial biodiversity is structured. At the beginning of the 20th century, Baas Becking laid the foundations for the biogeography of microbes by stating that ‘Everything is everywhere, but the environment selects’ (the EisE hypothesis). This hypothesis remained dogma for almost a century. However, the recognition that microbial ‘species’ are often assemblages of reproductively isolated lineages challenged the EisE hypothesis, leading to the now common assumption that microbial communities possess cryptic biogeographic structures. We tested the presence of a cryptic biogeographical structure for a well‐characterized fungal species complex (the Phialocephala fortinii s.l.–Acephala applanata species complex, PAC) using precise molecular species resolution. In addition, we analysed factors that could govern PAC community assembling. Locations Forty‐four study sites in temperate and boreal forests across the Northern Hemisphere were included. Methods (1) The distance–decay relationship among PAC communities was calculated and a resampling procedure was applied to analyse the effect of sampling intensity and geographic distances among PAC communities. (2) Factors shaping PAC communities (e.g. climatic factors and tree species composition) were studied. (3) We tested PAC communities for random composition. Results We found that the similarity of species assemblages did not decrease with increasing geographical distance. Moreover, species diversity did not increase by expanding the area sampled. Instead, species diversity increased by increasing the sampling effort. Community composition correlated neither with tree species composition nor climate, and no association among species was observed. Main conclusions We could not discover any cryptic biogeographic structure even after applying refined species assignment but we demonstrate the importance of sampling effort for understanding the biogeography of microorganisms. Moreover, we show that primarily stochastic effects are responsible for the species composition of PAC communities.     

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.     

Species richness and phylogenetic diversity of native and non‐native species respond differently to area and environmental factors

Aim

To test whether native and non‐native species have similar diversity–area relationships (species–area relationships [SARs] and phylogenetic diversity–area relationships [PDARs]) and whether they respond similarly to environmental variables.

Location

United States.

Methods

Using lists of native and non‐native species as well as environmental variables for >250 US national parks, we compared SARs and PDARs of native and non‐native species to test whether they respond similarly to environmental conditions. We then used multiple regressions involving climate, land cover and anthropogenic variables to further explore underlying predictors of diversity for plants and birds in US national parks.

Results

Native and non‐native species had different slopes for SARs and PDARs, with significantly higher slopes for native species. Corroborating this pattern, multiple regressions showed that native and non‐native diversity of plants and birds responded differently to a greater number of environmental variables than expected by chance. For native species richness, park area and longitude were the most important variables while the number of park visitors, temperature and the percentage of natural area were among the most important ones for non‐native species richness. Interestingly, the most important predictor of native and non‐native plant phylogenetic diversity, temperature, had positive effects on non‐native plants but negative effects on natives.

Main conclusions

SARs, PDARs and multiple regressions all suggest that native and non‐native plants and birds responded differently to environmental factors that influence their diversity. The agreement between diversity–area relationships and multiple regressions with environmental variables suggests that SARs and PDARs can be both used as quick proxies of overall responses of species to environmental conditions. However, more importantly, our results suggest that global change will have different effects on native and non‐native species, making it inappropriate to apply the large body of knowledge on native species to understand patterns of community assembly of non‐native species.

     

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.     

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.