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. Atmar , W. and B.D. Patterson . 1995 . The nestedness temperature calculator: a visual basic program, including 294 presence-absence matrices. AICS Research Inc, University Park, New Mexico, USA and the Field Museum , Chicago, IL, USA
Studies of functional diversity can help to understand processes that determine the presence of species in different habitats. Measurement of functional diversity in silviculture areas is important because different functional traits can show different responses to this landscape alteration, and therefore ecological functions can be affected. This study evaluated functional and taxonomic differences in bird assemblages in a native forest and eucalyptus plantations, and also assessed the functional nestedness of the bird species. We censused birds in eucalyptus plantations of four different ages, and also in a native forest. The results showed higher functional and taxonomic diversity of birds in the native forest than in plantations and higher similarity of functional traits between plantations of different ages. The high functional diversity in the native forest indicates a greater variety of functional traits, resulting in greater functional complementarity than in plantations. The association of some traits with the native forest, such as nectarivory and foraging in air, indicates the importance of native habitats in maintaining species and functions related to such traits. Already, species traits in eucalyptus plantations represent a subset of those that were recorded in the native forest, indicating that some functions are maintained in plantations. Our results demonstrate that the species occurrence in the plantations and native forest is determined by species traits. Thus, the maintenance of some functions in plantations is provided, although there is a higher functional diversity in native forest.
Nestedness is a pattern whereby species-poor assemblages are composed of subsets of the species occurring in richer assemblages. One of the most commonly used measures of the degree of nestedness for presence-absence matrices is the ‘discrepancy’ metric. A hitherto neglected property of that metric is that it may take several values for a given site-by-species matrix in the presence of ties in the marginal totals. This complicates the quantification of nestedness for the observed presence-absence matrix, as well as the assessment of statistical significance, which is typically achieved through Monte Carlo simulations. A solution to the problem is to calculate the minimum discrepancy using a modified algorithm involving permutations of columns with tied totals.
Beta diversity, species replacement and nestedness are often examined through pairwise comparisons of sites based on presence-absence data, and the relative importance of these ecological phenomena is evaluated by operations with dissimilarity coefficients. An example is the nestedness resultant dissimilarity (NRD) procedure recently proposed by Baselga (2010, Global Ecology andBiogeography 19: 134–143) to disentangle the nestedness fraction of beta diversity from species replacement. In our view, the component terms in this measure are not scaled uniformly and the nestedness fraction cannot be quantified properly without giving clear definitions for its measurement. We suggest to distinguish among three additive fractions of the species set of two sites: number of species shared (overlap), species replacement (=spatial turnover) and richness difference. Then, absolute beta diversity is obtained as a composite of the second two fractions (known as βWB), while nestedness is derived from the first and the third. To express beta diversity and nestedness in a relativized form, the respective sums are divided by the total number of species. These allow defining a new index to measure the fraction of beta diversity which is shared by nestedness as well, and is calculated as relativized richness difference with the condition that the two sites being compared have at least one species in common. It is called diversity-nestedness intersection coefficient (F). Baselga’s nestedness resultant dissimilarity and the diversity-nestedness intersection coefficient are compared graphically using artificial and actual examples. These functions follow a mathematical relationship for perfectly nested data, otherwise their results are divergent. Discrepancy increases when beta diversity is large, especially if richness differences override species replacement effects in shaping presence-absence data structures. An advantage of F is its compatibility with a general theoretical and methodological framework for revealing pattern in presence-absence data matrices.
A great challenge in ecology is to link patterns in nature with the factors that determine species coexistence and community structure. In general, these patterns have been associated with different environmental conditions and species traits. The coexistence of ant species could be affected by the availability of food and nesting resources, which depend on vegetation diversity and structural complexity. In this study, we attempt to reproduce, through null models, the properties of ant community structure in areas with different physiognomy of vegetation associated to different wildfire regimes. The null model construction considered ant traits such as occurrence frequency, body size, and nest type; and site characteristics such as vegetation height and extra-floral nectar availability, and their combinations. The null models were compared to observed species segregation and nestedness patterns. Ant species were more aggregated in space than expected by chance. Vegetation height and extra-floral nectar availability were included in the most successful models in predicting ant segregation and aggregation pattern. Furthermore, ants’ body size was enough to reproduce the nestedness of species distribution in sites. Our results suggest that under post-fire conditions, habitat complexity, resource availability and species traits such as body size may be the determinants of ant community structure.
We studied spatial changes in species composition (i.e., beta diversity) of local assemblages of birds along ∼450 km of the Middle Paraná River, an extensive fluvial system of South America. Point counts were used to survey birds at 60 plots located in shrub swamps and marshes of the floodplain within four sites (15 plots per site). Two sites were surrounded by each of the two upland ecoregions. Beta diversity of bird assemblages was high and was more important than alpha diversity in shaping regional diversity (i.e., gamma diversity) of the fluvial system. Compositional changes were related to species turnover among plots, while nestedness dissimilarity was not important for shaping diversity patterns. Variation-partitioning analysis showed that local conditions (i.e., landscape composition within a radius of 200 m from the center of each plot) accounted for more spatial variation in assemblage composition than did location along the fluvial system. Adjacent upland ecoregions did not account for spatial changes in bird composition within the fluvial system. In conclusion, environmental heterogeneity created by flood pulses is an important factor for sustaining regional diversity of birds within the fluvial system through effects on beta diversity.
Community patterns in species-by-site matrices provide valuable clues for inferring ecological processes at work. One such pattern is the occupancy frequency distribution (OFD) depicting the frequency distribution of row sums (i.e., occupancy) with a quarter OFDs of bimodal forms. Another pattern that also reflects the structure of row sums is the ranked species occupancy curve (RSOC), and has been shown to imply a 50% of bimodality in OFDs. The use of RSOCs has been advocated in literature over the OFD based on two conclusions from a 6-model inference using only 24 matrices: (i) RSOCs have two general forms, with half representing bimodal OFDs; (ii) there are no effects of spatial and study scales on RSOCs of different forms. Using a much more representative dataset of 289 matrices, I cast doubt on these two conclusions. A missing but dominant RSOC model (the truncated power law) is added. The number of species and the nestedness of the community differ significantly among matrices of different RSOC forms; however, the number of sites and the taxa in the studies do not differ among RSOC or OFD forms. The quarter OFDs of bimodal forms is reassured, with the least frequent occupancy consistent with Raunkiaer’s law of frequency. Importantly, a RSOC is mathematically transferrable to an OFD, with the derivative of the occupancy ranking curve being equal to the negative reciprocal of the occupancy frequency. Based on the type of the community (null versus interactive) and site environment (homogenous versus heterogeneous), four scenarios are needed to identify pre-inferring assemblage mechanisms. The results highlight the need for shifting research from the emphasis of marginal sums to the analysis of matrix structure for an in-depth understanding of the community assemblage patterns and mechanisms.
Conservation of species is often focused either only on those that are endangered, or on maximising the number recorded on species lists. However, species share space and time with others, thus interacting and building frameworks of relationships that can be unravelled by community-level network analysis. It is these relationships that ultimately drive ecosystem function via the transfer of energy and nutrients. However interactions are rarely considered in conservation planning. Network analysis can be used to detect key species (“hubs”) that play an important role in cohesiveness of networks. We applied this approach to plant-pollinator communities on two montane Northern Apennine grasslands, paying special attention to the modules and the identity of hubs. We performed season-wide sampling and then focused the network analyses on time units consistent with plant phenology. After testing for significance of modules, only some modules were found to be significantly segregated from others. Thus, networks were organized around a structured core of modules with a set of companion species that were not organized into compartments. Using a network approach we obtained a list of important plant and pollinator species, including three Network Hubs of utmost importance, and other hubs of particular biogeographical interest. By having a lot of links and high partner diversity, hubs should convey stability to networks. Due to their role in the networks, taking into account such key species when considering the management of sites could help to preserve the greatest number of interactions and thus support many other species.
Atmar, W. and B.D. Patterson. 1995. The nestedness temperature calculator: a visual basic programme, including 294 presence-absence matrices . AICS Research, University Park, NM, and The Field Museum, Chicago, IL
In community ecology, randomization tests with problem specific test statistics (e.g., nestedness, functional diversity, etc.) are often applied. Researchers in such studies may want not only to detect the significant departure from randomness, but also to measure the effect size (i.e., the magnitude of this departure). Measuring the effect size is necessary, for instance, when the roles of different assembly forces (e.g., environmental filtering, competition) are compared among sites. The standard method is to calculate standardized effect size (SES), i.e., to compute the departure from the mean of random communities divided by their standard deviations. Standardized effect size is a useful measure if the test statistic (e.g., nestedness index, phylogenetic or functional diversity) in the random communities follows a symmetric distribution. In this paper, I would like to call attention to the fact that SES may give us misleading information if the distribution is asymmetric (skewed). For symmetric distribution median and mean values are equal (i.e., SES = 0 indicates p = 0.5). However, this condition does not hold for skewed distributions. For symmetric distributions departure from the mean shows the extremity of the value, regardless of the sign of departure, while in asymmetric distributions the same deviation can be highly probable and extremely improbable, depending on its sign. To avoid these problems, I recommend checking symmetry of null-distribution before calculating the SES value. If the distribution is skewed, I recommend either log-transformation of the test statistic, or using probit-transformed p-value as effect size measure.
