Maximizing plant species inventory efficiency by means of remotely sensed spectral distances

Aim Inventorying plant species in an area based on randomly placed quadrats can be quite inefficient. The aim of this paper is to test whether plant species richness can be inventoried more efficiently by means of a spectrally‐based ordering of sites to be sampled. Location The study area was a complex wetland ecosystem, the Lake Montepulciano Nature Reserve, central Italy. This is one of the most important wetland areas of central Italy because of the diverse plant communities and the seasonal avifauna. Methods Field sampling, based on a random stratified sampling design, was performed in June 2002. Plant species composition was recorded within sampling units of 100 m² (plots) and 1 ha (macroplots). A QuickBird multispectral image of the same date was acquired and corrected both geometrically and radiometrically. Species accumulation curves based on spectral information were obtained by ordering sites to be sampled according to a maximum spectral distance criterion (i.e. by ordering sampling units based on the maximum distances among them in a four‐dimensional spectral space derived from the remotely sensed data). Different distance measures based on mean and maximum spectral distances among sampling units were tested. The performance of the species accumulation curve derived by the spectrally‐based ordering of sampling units was tested against a rarefaction curve obtained from the mean of 10,000 accumulation curves based on randomly ordered sampling units. Results The spectrally‐derived curve based on the maximum spectral distance among sampling units showed the most rapid accumulation of species, well above the rarefaction curve, at both the plot and the macroplot scales. Other ordering criteria of sampling units captured less richness over most of the species accumulation curves at both the spatial scales. The accumulation curves based on other measurements of distance were much closer to the random curve and did not show differences with respect to the species rarefaction curve based on random ordering of sampling units. Main conclusions The present investigation demonstrated that spectral‐based ordering of sites to be sampled can lead to the maximization of the efficiency of plant species inventories, an activity usually driven by the ‘botanist's internal algorithm’ (intuition), without any formalized rule to drive field sampling. The proposed approach can reduce costs of plant species inventorying through a more efficient allotment of time and sampling.     

Incorporating functional dissimilarities into sample‐based rarefaction curves: from taxon resampling to functional resampling

Question: Indices of functional diversity have been seen as the key for integrating information on species richness with measures that focus on those components of community composition related to ecosystem functioning. For comparing species richness among habitats on an equal‐effort basis, so‐called sample‐based rarefaction curves may be used. Given a study area that is sampled for species presence and absence in N plots, sample‐based rarefaction generates the expected number of accumulated species as the number of sampled plots increases from 1 to N. Accordingly, the question for this study is: can we construct a ‘functional rarefaction curve’ that summarizes the expected functional dissimilarity between species when n plots are drawn at random from a larger pool of N plots? Methods: In this paper, we propose a parametric measure of functional diversity that is obtained by combining sample‐based rarefaction techniques that are usually applied to species richness with Rao's quadratic diversity. For a given set of N presence/absence plots, the resulting measure summarizes the expected functional dissimilarity at an increasingly larger cumulative number of plots n (n≤N). Results and Conclusions: Due to its parametric nature, the proposed measure is progressively more sensitive to rare species with increasing plot number, thus rendering this measure adequate for comparing the functional diversity of species assemblages that have been sampled with variable effort.     

DNA barcoding‐based species delimitation increases species count of Eois (Geometridae) moths in a well‐studied tropical mountain forest by up to 50%

Abstract The genus Eois comprises an important part of megadiverse assemblages of geometrid moths in mountain rainforests of southern Ecuador. In this study we report: (i) on the construction of a DNA barcode library of Eois for identification purposes; and (ii) the exploration of species diversity through species delimitation by pair‐wise distance thresholds. COI barcode sequences were generated from 408 individuals (at least 105 species) collected on a narrow geographic scale (～40 km²) in the Reserva Biológica San Francisco. Analyses of barcode sequence divergence showed that species delimitations based solely on external morphology result in broad overlap of intra‐ and interspecific distances. Species delimitation at a 2% pair‐wise distance threshold reveals a clear barcoding gap. Fifty‐two previously unrecognized species were identified, 31 of which could only be distinguished by an integrative taxonomy approach. Twelve additional putative species could only be recognized by threshold‐based delimitation. Most splits resulted in two or three newly perceived cryptic taxa. The present study increased the number of Eois species recorded from that small area of Andean mountain forest from 102 to 154 (morphology‐ plus integrative taxonomy‐based) or even 166 (sequence‐based), leaving the species accumulation curve still far from reaching an asymptote. Notably, in no case did two or more previously distinguished morphospecies have to be lumped. This barcode inventory can be used to match larvae to known adult samples without rearing, and will therefore be of vital help to extend the currently limited knowledge about food plant relationships and host specialization.     

Performance of individual vs. group sampling for inferring dispersal under isolation‐by‐distance

Models of isolation‐by‐distance formalize the effects of genetic drift and gene flow in a spatial context where gene dispersal is spatially limited. These models have been used to show that, at an appropriate spatial scale, dispersal parameters can be inferred from the regression of genetic differentiation against geographic distance between sampling locations. This approach is compelling because it is relatively simple and robust and has rather low sampling requirements. In continuous populations, dispersal can be inferred from isolation‐by‐distance patterns using either individuals or groups as sampling units. Intrigued by empirical findings where individual samples seemed to provide more power, we used simulations to compare the performances of the two methods in a range of situations with different dispersal distributions. We found that sampling individuals provide more power in a range of dispersal conditions that is narrow but fits many realistic situations. These situations were characterized not only by the general steepness of isolation‐by‐distance but also by the intrinsic shape of the dispersal kernel. The performances of the two approaches are otherwise similar, suggesting that the choice of a sampling unit is globally less important than other settings such as a study's spatial scale.     

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.     

Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models

Aim Environmental niche models that utilize presence‐only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ‘training’ and ‘testing’ purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods Two types of niche modelling algorithms – the genetic algorithm for rule‐set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) – were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence‐only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions The findings indicate that care should be taken when interpreting the results from presence‐only niche models when training and test data have been randomly partitioned but occurrence data were non‐randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.     

Evaluating and presenting uncertainty in model‐based unconstrained ordination

Variability in ecological community composition is often analyzed by recording the presence or abundance of taxa in sample units, calculating a symmetric matrix of pairwise distances or dissimilarities among sample units and then mapping the resulting matrix to a low‐dimensional representation through methods collectively called ordination. Unconstrained ordination only uses taxon composition data, without any environmental or experimental covariates, to infer latent compositional gradients associated with the sampling units. Commonly, such distance‐based methods have been used for ordination, but recently there has been a shift toward model‐based approaches. Model‐based unconstrained ordinations are commonly formulated using a Bayesian latent factor model that permits uncertainty assessment for parameters, including the latent factors that correspond to gradients in community composition. While model‐based methods have the additional benefit of addressing uncertainty in the estimated gradients, typically the current practice is to report point estimates without summarizing uncertainty. To demonstrate the uncertainty present in model‐based unconstrained ordination, the well‐known spider and dune data sets were analyzed and shown to have large uncertainty in the ordination projections. Hence to understand the factors that contribute to the uncertainty, simulation studies were conducted to assess the impact of additional sampling units or species to help inform future ordination studies that seek to minimize variability in the latent factors. Accurate reporting of uncertainty is an important part of transparency in the scientific process; thus, a model‐based approach that accounts for uncertainty is valuable. An R package, UncertainOrd , contains visualization tools that accurately represent estimates of the gradients in community composition in the presence of uncertainty.     

Bird diversity patterns in Neotropical temperate farmlands: The role of environmental factors and trophic groups in the spring and autumn

Productivity, habitat heterogeneity and environmental similarity are of the most widely accepted hypotheses to explain spatial patterns of species richness and species composition similarity. Environmental factors may exhibit seasonal changes affecting species distributions. We explored possible changes in spatial patterns of bird species richness and species composition similarity. Feeding habits are likely to have a major influence in bird–environment associations and, given that food availability shows seasonal changes in temperate climates, we expect those associations to differ by trophic group (insectivores or granivores). We surveyed birds and estimated environmental variables along line‐transects covering an E‐W gradient of annual precipitation in the Pampas of Argentina during the autumn and the spring. We examined responses of bird species richness to spatial changes in habitat productivity and heterogeneity using regression analyses, and explored potential differences between seasons of those responses. Furthermore, we used Mantel tests to examine the relationship between species composition similarity and both the environmental similarity between sites and the geographic distance between sites, also assessing differences between seasons in those relationships. Richness of insectivorous birds was directly related to primary productivity in both seasons, whereas richness of seed‐eaters showed a positive association with habitat heterogeneity during the spring. Species composition similarity between assemblages was correlated with both productivity similarity and geographic proximity during the autumn and the spring, except for insectivore assemblages. Diversity within main trophic groups seemed to reflect differences in their spatial patterns as a response to changes between seasons in the spatial patterns of food resources. Our findings suggest that considering different seasons and functional groups in the analyses of diversity spatial pattern could contribute to better understand the determinants of biological diversity in temperate climates.     

Transferability of trait‐based species distribution models

The need for reliable prediction of species distributions dependent upon traits has been hindered by a lack of model transferability testing. We tested the predictive capacity of trait‐SDMs by fitting hierarchical generalised linear models with three trait and four environmental predictors for 20 eucalypt taxa in a reference region. We used these models to predict occurrence for a much larger set of taxa and target areas (82 taxa across 18 target regions) in south‐eastern Australia. Median predictive performance for new species in target regions was 0.65 (area under receiver operating curve) and 1.24 times random (area under precision recall curve). Prediction in target regions did not worsen with increasing geographic, environmental or community compositional distance from the reference region, and was improved with reliable trait–environment relationships. Transfer testing also identified trait–environment relationships that did not transfer. These results give confidence that traits and transfer testing can assist in the hard problem of predicting environmental responses for new species, environmental conditions and regions.     

Population‐specific deviations of global human craniometric variation from a neutral model

Past studies have revealed that much of human craniometric variation follows a neutral model of population relationships. At the same time, there is evidence for the influence of natural selection in having shaped some global diversity in craniometrics. In order to partition these effects, and to explore other potential population‐specific influences, this article analyzes residuals of craniometric distances from a geographically based neutral model of population structure. W.W. Howells' global craniometric data set was used for these analyses, consisting of 57 measurements for 22 populations around the world, excluding Polynesia and Micronesia because of the relatively recent settlement of these regions. Phenotypic and geographic distances were derived between all pairs of populations. Three‐dimensional multidimensional scaling configurations were obtained for both distance matrices, and compared using a Procrustes rotation method to show which populations do not fit the geographic model. This analysis revealed three major deviations: the Buriat, Greenland Inuit, and Peru. The deviations of the Buriat and Greenland Inuit appear to be related to long‐term adaptation to cold environments. The Peruvian sample is more similar to other New World populations than expected based on geographic distance alone. This deviation likely reflects the evolutionarily recent movement of human populations into South America, such that these populations are further from genetic equilibrium. This same pattern is seen in South American populations in a comparative analysis of classical genetic markers, but not in a comparative analysis of STR loci, perhaps reflecting the higher mutation rate for the latter. Am J Phys Anthropol, 2010. © 2009 Wiley‐Liss, Inc.     

Diversity and diversification of Eumolpinae (Coleoptera: Chrysomelidae) in New Caledonia

Contemporary taxonomic work on New Caledonian Eumolpinae (Chrysomelidae) has revealed their high species richness in this Western Pacific biodiversity hotspot. To estimate total species richness in this community, we used rapid DNA‐based biodiversity assessment tools, exploring mtDNA diversity and phylogenetic structure in a sample of 840 specimens across the main island. Concordance of morphospecies delimitation with units delimited by phenetic and phylogenetic algorithms revealed some 98–110 species in our sample, twice as many as currently described. Sample‐based rarefaction curves and species estimators using these species counts doubled this figure (up to 210 species), a realistic estimate considering taxonomic coverage, local endemism, and characteristics of sampling design, amongst others. New Caledonia, compared with larger tropical islands, stands out as a hotspot for Eumolpinae biodiversity. Molecular dating using either chrysomelid specific rates or tree calibration using palaeogeographical data dated the root of the ingroup tree (not necessarily a monophyletic radiation) at 38.5 Mya, implying colonizations after the Cretaceous breakage of Gondwana. Our data are compatible with the slowdown in diversification rates through time and are also consistent with recent faunal origins, possibly reflecting niche occupancy after an initial rapid diversification. Environmental factors (e.g. soil characteristics) seemingly played a role in this diversification process. © 2013 The Linnean Society of London     

EstimateS turns 20: statistical estimation of species richness and shared species from samples,with non‐parametric extrapolation

EstimateS offers statistical tools for analyzing and comparing the diversity and composition of species assemblages, based on sampling data. The latest version computes a wide range of biodiversity statistics for both sample‐based and individual‐based data, including analytical rarefaction and non‐parametric extrapolation, estimators of asymptotic species richness, diversity indices, Hill numbers, and (for sample‐based data) measures of compositional similarity among assemblages. In the first 20 yr of its existence, EstimateS has been downloaded more than 70 000 times by users in 140 countries, who have cited it in 5000 publications in studies of taxa from microbes to mammals in every biome.     

Similarities between rarefaction methods

The number of species in a community is one of the most commonly used measures of diversity. This measure is, however, affected by sample size. The rarefaction method attempts to correct sample size bias by assuming an underlying sampling model. Several rarefaction models are shown to be similar analytically. This similarity holds not only for the expected number of species but also for the variance of the number of species.     

Nondetection sampling bias in marked presence‐only data

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A model‐based solution for observational errors in laboratory studies

Molecular techniques for detecting microorganisms, macroorganisms and infectious agents are susceptible to false‐negative and false‐positive errors. If left unaddressed, these observational errors may yield misleading inference concerning occurrence, prevalence, sensitivity, specificity and covariate relationships. Occupancy models are widely used to account for false‐negative errors and more recently have even been used to address false‐positive errors, too. Current modelling options assume false‐positive errors only occur in truly negative samples, an assumption that yields biased inference concerning detection because a positive sample could be classified as such not because the target agent was successfully detected, but rather due to a false‐positive test result. We present an extension to the occupancy modelling framework that allows false‐positive errors in both negative and positive samples, thereby providing unbiased inference concerning occurrence and detection, as well as reliable conclusions about the efficacy of sampling designs, handling protocols and diagnostic tests. We apply the model to simulated data, showing that it recovers known parameters and outperforms other approaches that are commonly used when confronted with observation errors. We then apply the model to an experimental data set on Batrachochytrium dendrobatidis, a pathogenic fungus that is implicated in the global decline or extinction of hundreds of amphibian species. The model‐based approach we present is not only useful for obtaining reliable inference when data are contaminated with observational errors, but also eliminates the need for establishing arbitrary thresholds or decision rules that have hidden and unintended consequences.     

Local‐ to continental‐scale variation in the richness and composition of an aquatic food web

Aim We investigated patterns of species richness and composition of the aquatic food web found in the liquid‐filled leaves of the North American purple pitcher plant, Sarracenia purpurea (Sarraceniaceae), from local to continental scales. Location We sampled 20 pitcher‐plant communities at each of 39 sites spanning the geographic range of S. purpurea– from northern Florida to Newfoundland and westward to eastern British Columbia. Methods Environmental predictors of variation in species composition and species richness were measured at two different spatial scales: among pitchers within sites and among sites. Hierarchical Bayesian models were used to examine correlates and similarities of species richness and abundance within and among sites. Results Ninety‐two taxa of arthropods, protozoa and bacteria were identified in the 780 pitcher samples. The variation in the species composition of this multi‐trophic level community across the broad geographic range of the host plant was lower than the variation among pitchers within host‐plant populations. Variation among food webs in richness and composition was related to climate, pore‐water chemistry, pitcher‐plant morphology and leaf age. Variation in the abundance of the five most common invertebrates was also strongly related to pitcher morphology and site‐specific climatic and other environmental variables. Main conclusions The surprising result that these communities are more variable within their host‐plant populations than across North America suggests that the food web in S. purpurea leaves consists of two groups of species: (1) a core group of mostly obligate pitcher‐plant residents that have evolved strong requirements for the host plant and that co‐occur consistently across North America, and (2) a larger set of relatively uncommon, generalist taxa that co‐occur patchily.     

Towards robust and repeatable sampling methods in eDNA‐based studies

DNA‐based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three‐year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA‐based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.     

Single nucleotide polymorphism‐based dispersal estimates using noninvasive sampling

Quantifying dispersal within wild populations is an important but challenging task. Here we present a method to estimate contemporary, individual‐based dispersal distance from noninvasively collected samples using a specialized panel of 96 SNPs (single nucleotide polymorphisms). One main issue in conducting dispersal studies is the requirement for a high sampling resolution at a geographic scale appropriate for capturing the majority of dispersal events. In this study, fecal samples of brown bear (Ursus arctos) were collected by volunteer citizens, resulting in a high sampling resolution spanning over 45,000 km² in Gävleborg and Dalarna counties in Sweden. SNP genotypes were obtained for unique individuals sampled (n = 433) and subsequently used to reconstruct pedigrees. A Mantel test for isolation by distance suggests that the sampling scale was appropriate for females but not for males, which are known to disperse long distances. Euclidean distance was estimated between mother and offspring pairs identified through the reconstructed pedigrees. The mean dispersal distance was 12.9 km (SE 3.2) and 33.8 km (SE 6.8) for females and males, respectively. These results were significantly different (Wilcoxon's rank‐sum test: P‐value = 0.02) and are in agreement with the previously identified pattern of male‐biased dispersal. Our results illustrate the potential of using a combination of noninvasively collected samples at high resolution and specialized SNPs for pedigree‐based dispersal models.     

Effects of Single‐ and Mixed‐Species Group Composition on the Flight Initiation Distances of Plains and Grevy's Zebras

Zebras, as prey species, attend to the behavior of nearby conspecifics and heterospecifics when making decisions to flee from predators. Plains zebras (Equus quagga) and Grevy's zebras (E. grevyi) frequently form mixed‐species groups in zones where their ranges overlap in Kenya. Although anecdotal observations suggest that Plains zebras are more flighty around humans than Grevy's zebras are, this has not been empirically confirmed, and relatively little is known about how they may influence each other's flight behavior. We addressed these questions by examining the flight initiation distances (FIDs) of Plains and Grevy's zebras in single‐species and mixed‐species groups from an approaching human. One target individual per group was approached steadily on foot, with start distance, alert distance, and FID recorded from this target. Using start distance and alert distance separately as covariates, 22 Plains zebras in single‐species groups exhibited a significantly longer mean FID than 15 Grevy's zebras in single‐species groups. The FIDs of 7 Plains zebras and 5 Grevy's zebras tested in mixed‐species groups were virtually equivalent and intermediate to those of Plains and Grevy's zebras in single‐species groups, suggesting a bidirectional moderating influence of heterospecifics on risk assessment. This effect was most pronounced for Plains zebras in mixed‐species groups that exhibited an FID that was significantly shorter than that of Plains zebras in single‐species groups. Our findings underscore the importance of recognizing that related equids may be differently impacted by anthropogenic stress.     

Rarefaction and extrapolation of species richness using an area‐based Fisher's logseries

Fisher's logseries is widely used to characterize species abundance pattern, and some previous studies used it to predict species richness. However, this model, derived from the negative binomial model, degenerates at the zero‐abundance point (i.e., its probability mass fully concentrates at zero abundance, leading to an odd situation that no species can occur in the studied sample). Moreover, it is not directly related to the sampling area size. In this sense, the original Fisher's alpha (correspondingly, species richness) is incomparable among ecological communities with varying area sizes. To overcome these limitations, we developed a novel area‐based logseries model that can account for the compounding effect of the sampling area. The new model can be used to conduct area‐based rarefaction and extrapolation of species richness, with the advantage of accurately predicting species richness in a large region that has an area size being hundreds or thousands of times larger than that of a locally observed sample, provided that data follow the proposed model. The power of our proposed model has been validated by extensive numerical simulations and empirically tested through tree species richness extrapolation and interpolation in Brazilian Atlantic forests. Our parametric model is data parsimonious as it is still applicable when only the information on species number, community size, or the numbers of singleton and doubleton species in the local sample is available. Notably, in comparison with the original Fisher's method, our area‐based model can provide asymptotically unbiased variance estimation (therefore correct 95% confidence interval) for species richness. In conclusion, the proposed area‐based Fisher's logseries model can be of broad applications with clear and proper statistical background. Particularly, it is very suitable for being applied to hyperdiverse ecological assemblages in which nonparametric richness estimators were found to greatly underestimate species richness.