Can the biotic nestedness matrix be used predictively?

The biotas of a suite of neighboring patches of remnant vegetation often form a series of nested sub-sets, in which the species present in species-poor patches are non-random sub-sets of those present in richer patches. There has been recent interest in ways in which this knowledge may be used to aid conservation. We focus here on whether nested patterns can be used predictively. If nestedness in a fragmented system increases over time through biotic relaxation, locations where particular species may become extinct or are likely to colonize might be predictable and this could be useful in threatened-species management. We used the Temperature Calculator of Atmar and Patterson (1995) to arrange a matrix of bird species' occurrences in a series of buloke Allocasuarina leuhmannii woodland remnants so that nestedness was maximized. Probability bands generated by the calculator were used to predict possible colonization and extinction events. We then re-surveyed the avifauna of the fragments after a seven-year interval to test these predictions. Although nestedness increased between the two survey periods, there was no linear relationship between the generated probability of extinctions or colonizations and the accuracy of the predictions. The predictions derived from the calculator were no more accurate than a second set of predictions generated by use of a simple non-nested model. Despite the increase in nestedness, the arrangement of sites in each of the two maximally packed matrices was substantially different. For the nestedness matrix to generate accurate predictions, an increase in nestedness must be due to a minimization of unexpected species presences and absences rather than an extensive redistribution of species among remnants, as we found. The potential utility of nested patterns in predicting systematic colonization and extinction events should be further evaluated in other, less dynamic, fragmented systems such as those undergoing biotic relaxation.     

A new algorithm to calculate the nestedness temperature of presence–absence matrices

Aim The nestedness temperature of presence–absence matrices is currently calculated with the nestedness temperature calculator (NTC). In the algorithm implemented by the NTC: (1) the line of perfect order is not uniquely defined, (2) rows and columns are reordered in such a way that the packed matrix is not the one with the lowest temperature, and (3) the null model used to determine the probabilities of finding random matrices with the same or lower temperature is not adequate for most applications. We develop a new algorithm, BINMATNEST (binary matrix nestedness temperature calculator), that overcomes these difficulties. Methods BINMATNEST implements a line of perfect order that is uniquely defined, uses genetic algorithms to determine the reordering of rows and columns that leads to minimum matrix temperature, and provides three alternative null models to calculate the statistical significance of matrix temperature. Results The NTC performs poorly when the input matrix has checkerboard patterns. The more efficient packing of BINMATNEST translates into matrix temperatures that are lower than those computed with the NTC. The null model implemented in the NTC is associated with a large frequency of type I error, while the other null models implemented in BINMATNEST (null models 2 and 3) are conservative. Overall, null model 3 provides the best performance. The nestedness temperature of a matrix is affected by its size and fill, but the probability that such a temperature is obtained by chance is not. BINMATNEST reorders the input matrix in such a way that, if fragment size/isolation plays a role in determining community structure, there will be a significant rank correlation between the size/isolation of the fragments and the way that they are ordered in the packed matrix. Main conclusions The nestedness temperature of presence–absence matrices should not be calculated with the NTC. The algorithm implemented by BINMATNEST is more robust, allowing for across‐study comparisons of the extent to which the nestedness of communities departs from randomness. The sequence in which BINMATNEST reorders habitat fragments provides information about the causal role of immigration and extinction in shaping the community under study.     

Alternative statistical approaches to the use of data as evidence for hypotheses in human behavioral ecology

In their ambitious Evolutionary Anthropology paper, Winterhalder and Smith 1 review the history, theory, and methods of human behavioral ecology (HBE). In establishing how HBE differs from traditional approaches within sociocultural anthropology, they and others laud its hypothetical‐deductive research method. 1 - 3 Our aim is to critically examine how human behavioral ecologists conduct their research, specifically how they analyze and interpret data as evidence for scientific hypotheses. Through computer simulations and a review of empirical studies of human sex ratios, we consider some limitations of the status quo and present alternatives that could strengthen the field. In particular, we suggest that because human behavioral ecologists often consider multiple hypotheses, they should use statistical approaches that can quantify the evidence in empirical data for competing hypotheses. Although we focus on HBE, the principles of this paper apply broadly within biological anthropology.     

Ecological traits reveal functional nestedness of bird communities in habitat islands: a global survey

The widespread destruction and fragmentation of natural habitats around the world creates a strong incentive to understand how species and communities respond to such pressures. The vast majority of research into habitat fragmentation has focused solely on species presence or absence. However, analyses using innovative functional methodologies offer the prospect of providing new insights into the key questions surrounding community structure in fragmented systems. A key topic in fragmentation research is nestedness (i.e. the ordered composition of species assemblages involving a significant tendency for packing of the presence–absence matrix into a series of proper subsets). To date, nestedness analyses have been concerned solely with nestedness of species membership. Here, we capitalize on the publication of a recent nestedness index (traitNODF) in which the branch lengths of functional dendrograms are incorporated into the standard NODF nestedness index. Using bird community data from 18 forest‐habitat‐island studies, and measurements of eight continuous functional traits from over 1000 bird species, we conduct the first synthetic analysis of nestedness from a functional perspective (i.e. a nestedness analysis which incorporates how similar species are in terms of their ecological traits). We use two null models to test the significance of any observed functional nestedness, and investigate the role of habitat island area in driving functional nestedness. We also determine whether functional nestedness is driven primarily by species composition or by differences in species’ traits. We found that the majority (94%) of datasets were functionally nested by island area when a permutation null model was used, although only 11–22% of datasets were significantly functionally nested when a more conservative fixed‐fixed null model was used. Species composition was always the most important driver of functional nestedness, but the effect of differences in species traits was occasionally quite large. Our results isolate the importance of island area in driving functional nestedness where it does occur and show that habitat loss results in the ordered loss of functional traits. This analysis demonstrates the potential insights that may derive from testing for ordered patterns of functional diversity. Synthesis The widespread fragmentation of natural habitats around the world creates a strong incentive to understand how ecological communities respond to such pressures. A key topic in this research agenda is nestedness; however, to date, nestedness analyses have been concerned solely with species presence or absence. Using data from 18 bird‐habitat‐island studies we conduct the first synthetic analysis of nestedness from a functional perspective (i.e. a nestedness analysis which incorporates how similar species are in terms of their ecological traits). Our findings suggest that many bird‐habitat island communities are significantly functionally nested, although our results were sensitive to the null model used. Our study demonstrates the benefits of testing for ordered patterns of functional diversity.     

Nestedness of butterfly assemblages in the Zhoushan Archipelago,China: area effects,life-history traits and conservation implications

The nested subset pattern (nestedness) of faunal assemblages has been a research focus in the fields of island biogeography and conservation biology in recent decades. However, relatively few studies have described nestedness in butterfly assemblages in oceanic archipelago systems. Moreover, previous studies often quantified nestedness using inappropriate nestedness metrics and random fill algorithms with high Type I errors. The aims of this study are to examine the existence of nestedness and underlying causal mechanisms of butterfly assemblages in the Zhoushan Archipelago, China. We used the line-transect method to determine butterfly occupancy and abundance on 42 study islands from July to August 2014. We obtained butterfly life-history traits (wingspan, body weight and minimum area requirement) by field work and island geographical features (area and isolation) from the literature. We used the recently developed metric WNODF to estimate nestedness. Partial Spearman rank correlation was used to evaluate the associations of nestedness and island geographical features as well as butterfly life-history traits related to species extinction risk and colonization ability. The butterfly assemblages were significantly nested. Island area and minimum area requirement of butterflies were significantly correlated with nestedness after controlling for other independent variables. In contrast, the nestedness of butterflies did not appear to result from passive sampling or selective colonization. However, multi-year studies are needed to confirm that target effects are not muddling these results. Our results indicate that selective extinction may be the main driver of nestedness of butterfly assemblages in our study system. From a conservation viewpoint, we should protect both large islands and species with large area requirement to maximize the number of species preserved.     

Nestedness of Ectoparasite-Vertebrate Host Networks

Determining the structure of ectoparasite-host networks will enable disease ecologists to better understand and predict the spread of vector-borne diseases. If these networks have consistent properties, then studying the structure of well-understood networks could lead to extrapolation of these properties to others, including those that support emerging pathogens. Borrowing a quantitative measure of network structure from studies of mutualistic relationships between plants and their pollinators, we analyzed 29 ectoparasite-vertebrate host networks—including three derived from molecular bloodmeal analysis of mosquito feeding patterns—using measures of nestedness to identify non-random interactions among species. We found significant nestedness in ectoparasite-vertebrate host lists for habitats ranging from tropical rainforests to polar environments. These networks showed non-random patterns of nesting, and did not differ significantly from published estimates of nestedness from mutualistic networks. Mutualistic and antagonistic networks appear to be organized similarly, with generalized ectoparasites interacting with hosts that attract many ectoparasites and more specialized ectoparasites usually interacting with these same “generalized” hosts. This finding has implications for understanding the network dynamics of vector-born pathogens. We suggest that nestedness (rather than random ectoparasite-host associations) can allow rapid transfer of pathogens throughout a network, and expand upon such concepts as the dilution effect, bridge vectors, and host switching in the context of nested ectoparasite-vertebrate host networks.     

Temporal variability of nestedness and idiosyncratic species in stream insect assemblages

Aims The nested subset pattern has been widely studied in the last 20 years, and recent syntheses have challenged the prevalence of this pattern in nature. We examined the degree of nestedness, its temporal variability and its environmental correlates in stream insects of a boreal drainage system. We also examined differences between nested and idiosyncratic species in site occupancy, niche position and niche breadth. Location Koutajoki drainage basin in northern Finland. Methods We used (i) nestedness analyses with three null models for testing the significance of nestedness; (ii) Spearman rank correlation to examine the correlates of nestedness; (iii) outlying mean index analysis to analyse the niche characteristics of species; (iv) and t‐test to examine differences in niche breadth, niche position and site occupancy of idiosyncratic and other nested species. Results Stream insect assemblages were significantly nested in each of the three study years. The maximally packed matrices were significantly nested according to the nestedness calculator based on null models I (species frequencies and site richness equiprobable) and II (species frequencies fixed and site richness equiprobable), but non‐significant based on a conservative null model III (species frequencies and site richness fixed to those of the observed matrix). The most important correlate of nestedness was stream size, whereas isolation, productivity (total phosphorus) and habitat heterogeneity exhibited non‐significant relationship with nestedness. Idiosyncratic species occurred, on average, at more sites than nested species, mirroring the restricted distributions of several nested species that were inclined towards species‐rich sites. Idiosyncratic and nested species also differed in niche position and niche breadth, with idiosyncratic species having, on average, less marginal niche positions and wider niches than nested species. Main conclusions Stream size correlated with nestedness, possibly because small streams were inhabited only by species able to persist under, or colonize shortly after, disturbances, while most species could occur at larger sites where disturbances are less severe. From the conservation perspective, our findings suggest that stream size really matters, given that sites with high species richness and many rare species are more likely to occur in larger streams. However, also the requirements of idiosyncratic species should be accommodated in conservation planning.     

Toward ecologically explicit null models of nestedness

A community is "nested" when species assemblages in less rich sites form nonrandom subsets of those at richer sites. Conventional null models used to test for statistically nonrandom nestedness are under- or over-restrictive because they do not sufficiently isolate ecological processes of interest, which hinders ecological inference. We propose a class of null models that are ecologically explicit and interpretable. Expected values of species richness and incidence, rather than observed values, are used to create random presence-absence matrices for hypothesis testing. In our examples, based on six datasets, expected values were derived either by using an individually based random placement model or by fitting empirical models to richness data as a function of environmental covariates. We describe an algorithm for constructing unbiased null matrices, which permitted valid testing of our null models. Our approach avoids the problem of building too much structure into the null model, and enabled us to explicitly test whether observed communities were more nested than would be expected for a system structured solely by species-abundance and species-area or similar relationships. We argue that this test or similar tests are better determinants of whether a system is truly nested; a nested system should contain unique pattern not already predicted by more fundamental ecological principles such as species-area relationships. Most species assemblages we studied were not nested under these null models. Our results suggest that nestedness, beyond that which is explained by passive sampling processes, may not be as widespread as currently believed. These findings may help to improve the utility of nestedness as an ecological concept and conservation tool.     

Endemicity biases nestedness metrics: a demonstration, explanation and solution

Nestedness is frequently investigated to understand complex patterns of species occurrences. Temperature (T) is commonly used for comparisons of nestedness of different-sized datasets. However, the assumptions made for the standardization of this metric have not been fully explored, particularly the effects of endemicity. Here we show that T incorrectly indicates an increase in nestedness with the addition of non-nested endemics to matrices that are not perfectly nested – a consequence of standardizing matrix size by the product of species and sites. This problem is common both to test matrices and to real matrices that are typically subjected to nestedness analyses. The latter are often characterized by substantial numbers of endemics and by variation in the numbers of endemics in different taxa. Standardizing by occupancy resolves this problem, which is demonstrated using a derivative of discrepancy, d1. A small modification to T, such that it standardizes matrices by occupancy, would resolve the current problems with this nestedness metric.     

The influence of colonization in nested species subsets

Biotic communities inhabiting collections of insular habitat patches often exhibit compositional patterns described as nested subsets. In nested biotas, the assemblages of species in relatively depauperate sites comprise successive subsets of species in relatively richer sites. In theory, nestedness may result from selective extinction, selective colonization, or other mechanisms, such as nested habitats. Allopatric speciation is expected to reduce nestedness. Previous studies, based largely on comparisons between land-bridge and oceanic archipelagos, have emphasized the role of selective extinction. However, colonization could also be important in generating strong patterns of nestedness. We apply a recently published index of nestedness to more than 50 island biogeographic data sets, and examine the roles of colonization, extinction, endemism, and, to a limited extent, habitat variability on the degree on nestedness. Most data sets exhibit a significant degree of nestedness, although there is no general tendency for land-bridge biotas to appear more nested than oceanic ones. Endemic species are shown to generally reduce nestedness. Comparisons between groups of non-endemic species differing in overwater or inter-patch dispersal ability indicate that superior dispersers generally exhibit a greater degree of nestedness than poorer dispersers, a result opposite that expected if colonization were a less predictable process than extinction. These results suggest that frequent colonization is likely to enhance nestedness, thereby increasing the compositional overlap among insular biotas. The prevalence of selective extinction in natural communities remains in question. The importance of colonization in generating and maintaining nested subsets suggests that (1) minimum critical areas will be difficult to determine from patterns of species distributions on islands; (2) multiple conservation sites are likely to be required to preserve communities in subdivided landscapes; and (3) management of dispersal processes may be as important to preserving species and communities as is minimizing extinctions.     

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.     

舟山群岛蝶类群落嵌套分布格局及其影响因素

片断化生境中群落的物种组成常呈现嵌套分布格局。2013年7-8月, 我们在浙江舟山群岛采用截线法对28个岛屿上的蝴蝶群落进行了野外调查, 探讨了岛屿物种嵌套分布格局及其影响因素。通过测量采集标本获得蝶类的生活史特征(最小需求面积、翅展和体重), 查阅文献资料获得蝶类的栖息地特征(岛屿面积、距最近大陆距离和距最近大岛距离), 分析了影响蝶类群落嵌套结构的因素。研究结果显示: (1)舟山群岛蝶类群落符合嵌套分布格局; (2)岛屿面积和物种最小需求面积对嵌套格局的形成有显著影响; (3)舟山群岛蝶类群落嵌套格局的形成支持选择性灭绝假说; (4)随机检验零模型结果显示该嵌套分布格局并非采样偏差造成的。因此, 在制定舟山群岛区域蝶类保护措施时, 应优先考虑那些分布在面积较大岛屿的和最小需求面积较大的物种。     

The relationship between nested subsets,habitat subdivision,and species diversity

Biotic assemblages are said to be nested when the species making up relatively species-poor biotas comprise subsets of the species present at richer sites. Because species number and site area are often correlated, previous studies have suggested that nestedness may be relevant to questions of how habitat subdivision affects species diversity, particularly with respect to the question of whether a single large, contiguous patch of habitat will generally contain more species than collections of smaller patches having the same total combined area. However, inferences from analyses of nestedness are complicated by (1) variability in degrees of nestedness measured in natural communities, (2) variability in species-area relationships, and (3) the fact that nestedness statistics do not account for the size of habitat patches, only in the degree of overlap among sites with different numbers of species. By comparing various indices of nestedness with a saturation index that more directly measures the effect of habitat subdivision, it is shown that the first two of these factors are not as important as the third. Whether a single large site or several smaller ones having the same total combined area maximizes species diversity is dependent on (1) overlap in species composition among sites and (2) the number of species per unit area in the different sites. Because nestedness indices do not account for species number at a site, they cannot accurately predict how habitat subdivision affects species diversity patterns. Still, nestedness analyses are important in that they indicate the degree to which rare species tend to be found in the largest, or the most species-rich, sites, patterns not revealed by the saturation index. Both types of analysis are important in order to obtain a more complete picture of how species richness and compositional patterns are influenced by habitat subdivision.     

Historical climate‐change influences modularity and nestedness of pollination networks

The structure of species interaction networks is important for species coexistence, community stability and exposure of species to extinctions. Two widespread structures in ecological networks are modularity, i.e. weakly connected subgroups of species that are internally highly interlinked, and nestedness, i.e. specialist species that interact with a subset of those species with which generalist species also interact. Modularity and nestedness are often interpreted as evolutionary ecological structures that may have relevance for community persistence and resilience against perturbations, such as climate‐change. Therefore, historical climatic fluctuations could influence modularity and nestedness, but this possibility remains untested. This lack of research is in sharp contrast to the considerable efforts to disentangle the role of historical climate‐change and contemporary climate on species distributions, richness and community composition patterns. Here, we use a global database of pollination networks to show that historical climate‐change is at least as important as contemporary climate in shaping modularity and nestedness of pollination networks. Specifically, on the mainland we found a relatively strong negative association between Quaternary climate‐change and modularity, whereas nestedness was most prominent in areas having experienced high Quaternary climate‐change. On islands, Quaternary climate‐change had weak effects on modularity and no effects on nestedness. Hence, for both modularity and nestedness, historical climate‐change has left imprints on the network structure of mainland communities, but had comparably little effect on island communities. Our findings highlight a need to integrate historical climate fluctuations into eco‐evolutionary hypotheses of network structures, such as modularity and nestedness, and then test these against empirical data. We propose that historical climate‐change may have left imprints in the structural organisation of species interactions in an array of systems important for maintaining biological diversity.     

Informing management decisions for ecological networks,using dynamic models calibrated to noisy time‐series data

Well‐intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem‐wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision‐makers to select interventions. Using these time‐series data (sparse and noisy datasets drawn from deterministic Lotka‐Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species’ future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well‐constrained predictions before they can inform decisions that improve environmental outcomes.     

Back to the future: natural history and the way forward in modern fungal ecology

The growing power and increasing availability of molecular tools for identifying fungi in environmental samples has revolutionized the way that fungal ecologists work. As a result, more people from around the globe have jumped into the fungal community sequencing endeavor. Paradoxically, as these extensive datasets accumulate we are often at a loss for interpretation due to the lack of basic autecology and natural history information for most fungi. As a result we are in danger of learning less and about more and more. I suggest that one way forward in fungal ecology is through a modern version of fungal natural history, with a focus on holistic understanding of individual species and ecosystems, but driven by modern genomic and molecular tools. By combining the extensive data generated through environmental sequencing with an intensive, molecular-based natural history we can create a synergy that will propel fungal ecology forward.     

A Toolkit for ARB to Integrate Custom Databases and Externally Built Phylogenies

Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for processing experimental data since many molecular studies are characterized by massive datasets. To keep up with experimental data demands, ecologists are forced to choose between continually upgrading expensive in-house computer hardware or outsourcing the most demanding computations to the cloud. Outsourcing is attractive since it is the least expensive option, but does not necessarily allow direct user interaction with the data for exploratory analysis. Desktop analytical tools such as ARB are indispensable for this purpose, but they do not necessarily offer a convenient solution for the coordination and integration of datasets between local and outsourced destinations. Therefore, researchers are currently left with an undesirable tradeoff between computational throughput and analytical capability. To mitigate this tradeoff we introduce a software package to leverage the utility of the interactive exploratory tools offered by ARB with the computational throughput of cloud-based resources. Our pipeline serves as middleware between the desktop and the cloud allowing researchers to form local custom databases containing sequences and metadata from multiple resources and a method for linking data outsourced for computation back to the local database. A tutorial implementation of the toolkit is provided in the supporting information, S1 Tutorial. Availability: http://www.ece.drexel.edu/gailr/EESI/tutorial.php.     

Implications of scale for patterns and processes in stream ecology

Abstract Although the scale-dependence of ecological patterns and processes is recognized by freshwater ecologists, current knowledge of scale effects is rudimentary and non-quantitative. We review issues of spatial and temporal scale in this paper to highlight conceptual problems relating to scale and some potential solutions. We present examples of how the spatial scale of a study influences observed patterns and their interpretation, and discuss how the size of an experimental arena influences the degree to which the dynamics of studied populations are influenced by exchange processes (immigration and emigration). The results of small-scale field experiments in streams will often be strongly influenced by the per capita exchange rates of organisms and differences in exchange rates may explain differences in the perceived effects of stream manipulations across scales. Spatial extent also influences the amount of spatial heterogeneity within a study site or arena, with important consequences for the outcome of predator-prey interactions. We suggest that changes in the availability of prey refuges may help explain why predator manipulations in streams appear to weaken as arena size increases. We also recommend that new techniques for decomposing and quantifying spatial heterogeneity be applied to characterize scale-dependent variation in freshwater systems. Lastly, we discuss the pitfalls of mismatching the temporal scale of experiments and models. Models incorporating spatial heterogeneity and the behaviour of organisms are needed to predict the short-term outcome of perturbations in streams, whereas models predicting long-term dynamics will need to integrate the impacts of episodic disturbance and all life history stages of organisms. In general, we recommend that freshwater ecologists undertake more multi-scale sampling and experimentation to examine patterns and processes at multiple scales, and make greater attempts to match the scales of their observations and experiments to the characteristic scales of the phenomena that they investigate.