A family of functional dissimilarity measures for presence and absence data

Plot‐to‐plot dissimilarity measures are considered a valuable tool for understanding the complex ecological mechanisms that drive community composition. Traditional presence/absence coefficients are usually based on different combinations of the matching/mismatching components of the 2 × 2 contingency table. However, more recently, dissimilarity measures that incorporate information about the degree of functional differences between the species in both plots have received increasing attention. This is because such “functional dissimilarity measures” capture information on the species' functional traits, which is ignored by traditional coefficients. Therefore, functional dissimilarity measures tend to correlate more strongly with ecosystem‐level processes, as species influence these processes via their traits. In this study, we introduce a new family of dissimilarity measures for presence and absence data, which consider functional dissimilarities among species in the calculation of the matching/mismatching components of the 2 × 2 contingency table. Within this family, the behavior of the Jaccard coefficient, together with its additive components, species replacement, and richness difference, is examined by graphical comparisons and ordinations based on simulated data.     

Can we model the probability of presence of species without absence data?

In ecological studies, it is useful to estimate the probability that a species occurs at given locations. The probability of presence can be modeled by traditional statistical methods, if both presence and absence data are available. However, the challenge is that most species records contain only presence data, without reliable absence data. Previous presence‐only methods can estimate a relative index of habitat suitability, but cannot estimate the actual probability of presence. In this study, we develop a presence and background learning algorithm (PBL) that is successful in modeling the conditional probability of presence of a simulated species. The model is trained by two completely separate sets: observed presence and background data. Assuming that the probability of presence is one for ‘prototypical presence’ locations where the habitats are maximally suitable for a species, we can estimate a constant that can calibrate the trained model into the actual probability of presence. Experimental results show that the PBL method performs similarly to a presence‐absence method, and significantly better than the widely used maximum entropy method. The new algorithm enables us to model the probability that a species occurs conditional on environmental covariates without absence data. Hence, it has potential to improve modeling of the geographical distributions of species.     

Spind: a package for computing spatially corrected accuracy measures

Using an appropriate accuracy measure is essential for assessing prediction accuracy in species distribution modelling. Therefore, model evaluation as an analytical uncertainty is a challenging problem. Although a variety of accuracy measures for the assessment of prediction errors in presence/absence models is available, there is a lack of spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions. We present ‘spind’, a new software package (based on the R software program) that provides spatial performance measures for grid‐based models. These accuracy measures are generalized, spatially corrected versions of the classical ones, thus enabling comparisons between them. Our method for evaluation consists of the following steps: 1) incorporate additional autocorrelation until spatial autocorrelation in predictions and actuals is balanced, 2) cross‐classify predictions and adjusted actuals in a 4 × 4 contingency table, 3) use a refined weighting pattern for errors, and 4) calculate weighted Kappa, sensitivity, specificity and subsequently ROC, AUC, TSS to get spatially corrected indices. To illustrate the impact of our spatial method we present an example of simulated data as well as an example of presence/absence data of the plant species Dianthus carthusianorum across Germany. Our analysis includes a statistic for the comparison of spatial and classical (non‐spatial) indices. We find that our spatial indices tend to result in higher values than classical ones. These differences are statistically significant at medium and high autocorrelation levels. We conclude that these spatial accuracy measures may contribute to evaluate prediction errors in presence/absence models, especially in case of medium or high degree of similarity of adjacent data, i.e. aggregated (clumped) or continuous species distributions.     

Presence‐only versus presence–absence data in species composition determinant analyses

Aim Studying relationships between species and their physical environment requires species distribution data, ideally based on presence–absence (P–A) data derived from surveys. Such data are limited in their spatial extent. Presence‐only (P‐O) data are considered inappropriate for such analyses. Our aim was to evaluate whether such data may be used when considering a multitude of species over a large spatial extent, in order to analyse the relationships between environmental factors and species composition. Location The study was conducted in virtual space. However, geographic origin of the data used is the contiguous USA. Methods We created distribution maps for 50 virtual species based on actual environmental conditions in the study. Sampling locations were based on true observations from the Global Biodiversity Information Facility. We produced P–A data by selecting ∼1000 random locations and recorded the presence/absence of all species. We produced two P‐O data sets. Full P‐O set was produced by sampling the species in locations of true occurrences of species. Partial P‐O was a subset of full P‐O data set matching the size of the P–A data set. For each data set, we recorded the environmental variables at the same locations. We used CCA to evaluate the amount of variance in species composition explained by each variable. We evaluated the bias in the data set by calculating the deviation of average values of the environmental variables in sampled locations compared to the entire area. Results P–A and P‐O data sets were similar in terms of the amount of variance explained by the different environmental variables. We found sizable environmental and spatial bias in the P‐O data set, compared to the entire study area. Main conclusions Our results suggest that although P‐O data from collections contain bias, the multitude of species, and thus the relatively large amount of information in the data, allow the use of P‐O data for analysing environmental determinants of species composition.     

Land use improves spatial predictions of mountain plant abundance but not presence‐absence

Question: Does a land‐use variable improve spatial predictions of plant species presence‐absence and abundance models at the regional scale in a mountain landscape? Location: Western Swiss Alps. Methods: Presence‐absence generalized linear models (GLM) and abundance ordinal logistic regression models (LRM) were fitted to data on 78 mountain plant species, with topo‐climatic and/or land‐use variables available at a 25‐m resolution. The additional contribution of land use when added to topo‐climatic models was evaluated by: (1) assessing the changes in model fit and (2) predictive power, (3) partitioning the deviance respectively explained by the topo‐climatic variables and the land‐use variable through variation partitioning, and (5) comparing spatial projections. Results: Land use significantly improved the fit of presence‐absence models but not their predictive power. In contrast, land use significantly improved both the fit and predictive power of abundance models. Variation partitioning also showed that the individual contribution of land use to the deviance explained by presence‐absence models was, on average, weak for both GLM and LRM (3.7% and 4.5%, respectively), but changes in spatial projections could nevertheless be important for some species. Conclusions: In this mountain area and at our regional scale, land use is important for predicting abundance, but not presence‐absence. The importance of adding land‐use information depends on the species considered. Even without a marked effect on model fit and predictive performance, adding land use can affect spatial projections of both presence‐absence and abundance models.     

Moving beyond presence and absence when examining changes in species distributions

Species distributions are often simplified to binary representations of the ranges where they are present and absent. It is then common to look for changes in these ranges as indicators of the effects of climate change, the expansion or control of invasive species or the impact of human land‐use changes. We argue that there are inherent problems with this approach, and more emphasis should be placed on species relative abundance rather than just presence. The sampling effort required to be confident of absence is often impractical to achieve, and estimates of species range changes based on survey data are therefore inherently sensitive to sampling intensity. Species niches estimated using presence‐absence or presence‐only models are broader than those for abundance and may exaggerate the viability of small marginal sink populations. We demonstrate that it is possible to transform models of predicted probability of presence to expected abundance if the sampling intensity is known. Using case studies of Antarctic mosses and temperate rain forest trees, we demonstrate additional insights into biotic change that can be gained using this method. While species becoming locally extinct or colonising new areas are extreme and obviously important impacts of global environmental change, changes in abundance could still signal important changes in biological systems and be an early warning indicator of larger future changes.     

A grid‐based method for sampling and analysing spatially ambiguous plants

Abstract. Spatial data can provide much information about the interrelations of plants and the relationship between individuals and the environment. Spatially ambiguous plants, i.e. plants without readily identifiable loci, and plants that are profusely abundant, present non‐trivial impediments to the collection and analysis of vegetation data derived from standard spatial sampling techniques. Sampling with grids of presence/absence quadrats can ameliorate much of this difficulty. Our analysis of 10 fully‐mapped grassland plots demonstrates the applicability of the grid‐based approach which revealed spatial dependence at a much lower sampling effort than mapping each plant. Ripley's K‐function, a test commonly used for point patterns, was effective for pattern analysis on the grids and the gridded quadrat technique was an effective tool for quantifying spatial patterns. The addition of spatial pattern measures should allow for better comparisons of vegetation structure between sites, instead of sole reliance on species composition data.     

Without quality presence–absence data,discrimination metrics such as TSS can be misleading measures of model performance

The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the area under the receiving operating characteristic curve (AUC), the Kappa statistic and the true skill statistic (TSS). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence‐only or presence‐background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F‐measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F‐measures provide adequate estimations of model discrimination capacity. Second, we show that in real‐world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence‐pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence‐pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination metrics when quality presence–absence data are lacking, and we recommend obtaining such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F‐measures, which were not biased by prevalence, contrary to TSS.     

利用分布有/无数据预测物种空间分布的研究方法综述

详细的物种地理分布信息是生态学研究和制定保护策略的基础。相比较于直接估测种群数量,获取物种分布的有/无数据更为实用。因此,利用分布有/无数据并结合环境变量建立模型预测物种空间分布的方法在近年来得到了长足发展,并被广泛应用。利用分布有/无数据预测物种分布,关键的步骤包括：1)构建总体概念模型,2)收集物种分布有/无数据,并准备环境变量图层;3)选择合适的统计模型和算法,以及4)对模型进行评估。概念模型提出研究假设,并确定数据收集及模型方法。收集物种分布数据有系统调查及非系统调查方法。筛选并准备与物种分布相关的环境变量,利用GIS工具处理,使之成为符合模型条件的具有合适的空间尺度的数字化图层。利用环境变量和物种分布有/无的数据,选择合适的方法及软件建立模型,并对模型进行检验和评估。我们总结了用于构建物种分布模型的不同算法和软件。本文将针对以上各个环节,阐述利用物种分布有/无数据进行研究所需要的技术细节,以期望为读者提供借鉴。     

Exploiting quantitative information in the analysis of dominant markers

Dominant genetic markers such as AFLPs and RAPDs are usually analyzed based on the presence or absence of a band on an electrophoretic gel. This type of analysis does not allow a distinction among dominant homozygotes and heterozygotes. Such a distinction is possible based on the quantitative measurement of band intensities. In the present paper, we consider the problem of analyzing dominant markers based on band-intensity data. The basic step for mapping a marker is to assess its recombination frequency with other markers. Ordering markers on a map can then be done using a number of standard procedures. For this reason estimation of the recombination frequency is the main focus of the present paper. The method is demonstrated for the case of an F₂ population. By simulation we investigate its accuracy and compare it to the standard estimation based on dominant scoring for band presence/absence. There are a number of potential applications. For example, the map may be used to locate quantitative trait loci (QTLs), applying standard procedures modified to account for uncertainty of the marker genotype. Moreover, map information can be used to determine the most likely genotype at a marker, given its band intensity and the band intensities at flanking markers. Received: 2 May 2000 / Accepted: 6 December 2000     

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.     

The limited role of differential fractionation in genome content variation and function in maize (Zea mays L.) inbred lines

Maize is a diverse paleotetraploid species with considerable presence/absence variation and copy number variation. One mechanism through which presence/absence variation can arise is differential fractionation. Fractionation refers to the loss of duplicate gene pairs from one of the maize subgenomes during diploidization. Differential fractionation refers to non‐shared gene loss events between individuals following a whole‐genome duplication event. We investigated the prevalence of presence/absence variation resulting from differential fractionation in the syntenic portion of the genome using two whole‐genome de novo assemblies of the inbred lines B73 and PH207. Between these two genomes, syntenic genes were highly conserved with less than 1% of syntenic genes being subject to differential fractionation. The few variably fractionated syntenic genes that were identified are unlikely to contribute to functional phenotypic variation, as there is a significant depletion of these genes in annotated gene sets. In further comparisons of 60 diverse inbred lines, non‐syntenic genes were six times more likely to be variable than syntenic genes, suggesting that comparisons among additional genome assemblies are not likely to result in the discovery of large‐scale presence/absence variation among syntenic genes.     

Testing the predictive performance of distribution models

Distribution models are used to predict the likelihood of occurrence or abundance of a species at locations where census data are not available. An integral part of modelling is the testing of model performance. We compared different schemes and measures for testing model performance using 79 species from the North American Breeding Bird Survey. The four testing schemes we compared featured increasing independence between test and training data: resubstitution, random data hold‐out and two spatially segregated data hold‐out designs. The different testing measures also addressed different levels of information content in the dependent variable: regression R² for absolute abundance, squared correlation coefficient r² for relative abundance and AUC/Somer’s D for presence/absence. We found that higher levels of independence between test and training data lead to lower assessments of prediction accuracy. Even for data collected independently, spatial autocorrelation leads to dependence between random hold‐out test data and training data, and thus to inflated measures of model performance. While there is a general awareness of the importance of autocorrelation to model building and hypothesis testing, its consequences via violation of independence between training and testing data have not been addressed systematically and comprehensively before. Furthermore, increasing information content (from correctly classifying presence/absence, to predicting relative abundance, to predicting absolute abundance) leads to decreasing predictive performance. The current tests for presence/absence distribution models are typically overly optimistic because a) the test and training data are not independent and b) the correct classification of presence/absence has a relatively low information content and thus capability to address ecological and conservation questions compared to a prediction of abundance. Meaningful evaluation of model performance requires testing on spatially independent data, if the intended application of the model is to predict into new geographic or climatic space, which arguably is the case for most applications of distribution models.     

Spatial variation and biogeography of sand forest avian assemblages in South Africa

Aim To examine biogeographical affiliations, habitat‐associated heterogeneity and endemism of avian assemblages in sand forest patches and the savanna‐like mixed woodland matrix. Location Two reserves in the Maputaland Centre of Endemism (MC) on the southern Mozambique Coastal Plain of northern KwaZulu‐Natal, South Africa. Methods Replicated surveys were undertaken in each of the two habitat types in each reserve, providing species abundance data over a full year. Vegetation structure at each of the survey sites was also quantified. Differences between the bird assemblages and the extent to which vegetation structure explained these differences were assessed using multi‐variate techniques. Biogeographical comparisons were based on species presence/absence data and clustering techniques. Results Bird assemblages differed significantly between habitats both within a given reserve and between reserves, and also between reserves for a given habitat. Differences in vegetation structure contributed substantially to differences between the avian assemblages. Of the four species endemic to the MC, three (Neergaard’s sunbird, Rudd’s apalis, and Woodward’s batis) were consistently present in sand forest. The fourth (pink‐throated twinspot) preferred mixed woodland. None of these endemic species was classed as rare. In the biogeographical analysis, both the sand forest and the mixed woodland bird assemblages were most similar to bird assemblages found in the forest biome or the Afromontane forest biome, depending on the biome classification used. Main conclusions The close affinities of sand forest and mixed woodland assemblages to those of the forest biome are most likely due to similarities in vegetation structure of these forests. Bird assemblages differ between the sand forest and mixed woodland habitats both within a given reserve and between reserves, and also between reserves for a given habitat. These differences extend to species endemic to the MC. Thus, conservation of sand forest habitat in a variety of areas is necessary to ensure the long‐term persistence of the biota.     

Pairwise versus presence–absence approaches for analysing biodiversity patterns

It has been proposed that the study of co‐occurrence of species, which is traditionally performed using full presence–absence matrices of sets of many species, could benefit from simply testing for random co‐occurrence between pairs of species, and that use of a full presence–absence matrix is tantamount to regarding it as having some real ecological identity. Here I argue that although there are valid questions that can be answered using a pairwise approach, there are many others that naturally require the analysis of entire sets of species in a joint way, as provided for through the use of full presence–absence matrices. Moreover, there are theoretical and mathematical advantages to the use of presence–absence matrices, a few of which are briefly discussed in this short note.     

Improving comparability between qPCR‐based telomere studies

Comparability of findings from qPCR‐based telomere studies is hampered by such measurement results being assay‐specific, precluding a direct quantitative comparisons of observed differences and/or slopes of associations between studies. It is proposed that this can be partially alleviated by expressing qPCR‐based telomere data as Z‐scores.     

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.     

A probability-based analysis of temporal and spatial co-occurrence in grassland birds

Aim To test for non‐random co‐occurrence in 36 species of grassland birds using a new metric and the C‐score. The analysis used presence–absence data of birds distributed among 305 sites (or landscapes) over a period of 35 years. This analysis departs from traditional analyses of species co‐occurrence in its use of temporal data and of individual species’ probabilities of occurrence to derive analytically the expected co‐occurrence between paired species. Location Great Plains region, USA. Methods Presence–absence data for the bird species were obtained from the North American Breeding Bird Survey. The analysis was restricted to species pairs whose geographic ranges overlapped. Each of 541 species pairs was classified as a positive, negative, or non‐significant association depending on the mean difference between the observed and expected frequencies of co‐occurrence over the 35‐year time‐span. Results Of the 541 species pairs that were examined, 202 to 293 (37–54%) were positively associated, depending on which of two null models was used. However, only a few species pairs (<5%) were negatively associated. An additional 89 species pairs did not have overlapping ranges and hence represented de facto negative associations. The results from analyses based on C‐scores generally agreed with the analyses based on the difference between observed and expected co‐occurrence, although the latter analyses were slightly more powerful. Main conclusions Grassland birds within the Great Plains region are primarily distributed among landscapes either independently or in conjunction with one another. Only a few species pairs exhibited repulsed or segregated distributions. This indicates that the shared preference for grassland habitat may be more important in producing coexistence than are negative species interactions in preventing it. The large number of non‐significant associations may represent random associations and thereby indicate that the presence/absence of other grassland bird species may have little effect on whether a given focal species is also found within the landscape. In a broader context, the probability‐based approach used in this study may be useful in future studies of species co‐occurrence.     

A new species of Liropus (Crustacea, Amphipoda, Caprellidae) from Le Danois bank (southern Bay of Biscay)

A new species of the genus Liropus (Crustacea, Amphipoda, Caprellidae) is described based on specimens collected from Le Danois bank (‘El Cachucho’ fishing grounds), Bay of Biscay. Liropus cachuchoensis n. sp. can be distinguished from all its congeners mainly by the absence of eyes and by the presence of a dorsal projection proximally on pereonites 3, 4 and 5 in males, on 3 and 5 in females. The new species has been found living on muddy bottoms on the southern flank of the bank and adjacent continental slope, between 619 and 1062 m depth, with a maximum abundance (56.1 ind./100 m²) recorded at 1044–1062 m. Morphological comparisons among the world's members of Liropus, a key to species, and data on their distribution are presented.