The net result: evaluating species richness extrapolation techniques for littoral pond invertebrates

1. Total species richness for an assemblage or site is a valuable measure in conservation monitoring and assessment, but protocols for sampling and species richness determination in wetland habitats such as ponds, bogs or mires remain largely unrefined. 2. Techniques for estimation of total richness of an assemblage based upon replicated sampling offer the opportunity to derive useful estimates of total richness based upon small numbers of samples, and limit sampling‐derived disturbance which can be particularly problematic in small aquatic habitats. 3. We quantified the performance of eight of the most commonly encountered estimators of species richness for a variety of littoral zone macrofauna from ponds, comparing estimated richness to maximum richness derived from sampling. 4. Estimates using non‐parametric techniques based on species incidence provided the most accurate and precise estimates. The estimators Chao 2 and incidence‐based coverage estimator (ICE) from this category were reliable and consistent slight over‐estimators; the abundance‐based estimator Chao1 also performed well. 5. Species inventory based on relatively small numbers of samples might be significantly improved by use of non‐parametric estimators for quantification of species richness. 6. Use of non‐parametric estimators of species richness can assist biodiversity inventory by preventing erroneous rankings of habitat richness based upon observed species numbers from limited sampling.     

Application of species richness estimators for the assessment of fungal diversity

Species richness and distribution patterns of wood-inhabiting fungi and mycetozoans (slime moulds) were investigated in the canopy of a Central European temperate mixed deciduous forest. Species richness was described with diversity indices and species-accumulation curves. Nonmetrical multidimensional scaling was used to assess fungal species composition on different tree species. Different species richness estimators were used to extrapolate species richness beyond our own data. The reliability of the abundance-based coverage estimator, Chao, Jackknife and other estimators of species richness was evaluated for mycological surveys. While the species-accumulation curve of mycetozoans came close to saturation, that of wood-inhabiting fungi was continuously rising. The Chao 2 richness estimator was considered most appropriate to predict the number of species at the investigation site if sampling were continued. Gray's predictor of species richness should be used if statements of the number of species in larger areas are required. Multivariate analysis revealed the importance of different tree species for the conservation and maintenance of fungal diversity within forests, because each tree species possessed a characteristic fungal community. The described mathematical approaches of estimating species richness possess great potential to address fungal diversity on a regional, national, and global scale.     

The use of incidence-based species richness estimators,species accumulation curves and similarity measures to appraise ethnobotanical inventories from South Africa

The incorporation of suitable quantitative methods into ethnobotanical studies enhances the value of the research and the interpretation of the results. Prediction of sample species richness and the use of species accumulation functions have been addressed little in applied ethnobotany. In this paper, incidence-based species richness estimators, species accumulation curves and similarity measures are used to compare and predict species richness, evaluate sampling effort and compare the similarity of species inventories for ethnobotanical data sets derived from the trade in traditional medicine in Johannesburg and Mpumalanga, South Africa. EstimateS was used to compute estimators of species richness (e.g. Jackknife), rarefaction curves, species accumulation curves and complimentarity. Results showed that while the Michaelis–Menten Means estimator appeared to be the best estimator because the curve approached a horizontal asymptote, it was not able to accurately predict species richness for one of the data sets when two of its subsamples were individually tested. Instead, the first-order Jackknife estimator best approximated the known richness.     

Testing species richness estimation methods using museum label data on the Danish Asilidae

Museum collections are treasure troves of biodiversity information thatcan potentially be used for species richness estimation. Using label data on theDanish Asilidae (Diptera), we test eight species richness estimation techniques(abundance-based coverage estimator (ACE), ICE, Chao1, Chao2, first and secondorder Jackknife, Bootstrap and MMMeans) by comparing the estimates to the numberof species likely to occur in Denmark based on distributional information,expert opinion, and a species–area curve. We are investigating which ofthe estimators are most suited for the task. Furthermore, through theuse of four different subsampling schemes we study which kind of label information isnecessary in order to apply these estimation procedures. The first and secondorder Jackknife estimators yield the most accurate estimate of the number ofcollectable species in Denmark, while ACE, Bootstrap and Chao1 only provideslight improvements over observed values. We find that all estimatorsunderestimate the true diversity of Danish Asilidae and speculate that thisperformance is due to a discrepancy between the total and the collectable faunain the region. Finally, we discuss the implications for species richnessestimation and emphasize that for most terrestrial arthropod taxa thesediscrepancies are of such a magnitude that estimated species richness values maybe dangerously low and of limited use in conservation decision making.     

Could we obtain better estimates of plot species richness from multiple‐observer plant censuses?

Question: Could we better estimate plot species richness by asking several botanists to survey the same plots and using non‐parametric estimators of richness? Location: Two French deciduous forests. Methods: Using replicated, independent censuses made by 11 professional botanists on the same eight 100‐m² forest plots, the relative performance of different richness estimators (Lincoln‐Petersen, Jackknife 1&2, Chao 1&2, Bootstrap, Chao Mth, Darroch) and the variation in their performance with the number of botanists involved (teams with two to eight botanists) were investigated. The sensitivity of these estimators to the presence of misidentifications in the data was also assessed. Results: When misidentifications are removed, Chao Mth estimators converged fastest to true richness, but none of the tested estimators correctly accounted for differences in exhaustiveness between the teams. Finally, all estimators were highly sensitive to misidentifications. Conclusions: Richness estimators are of little help in the presence of misidentifications and are ineffective at removing between‐team discrepancies, thus strongly limiting their usefulness in practice. Methods are presented to show how surveys can be designed to remove misidentifications and limit between‐team discrepancies. A sensible sampling design for 100‐m² plots in temperate forests would involve triplets of botanists and correcting data with the Chao N1. Pairs of botanists would already significantly improve the richness estimates, but such estimates would still be biased low. However, further research is needed to design new richness estimators that are more robust to observer effects.     

Performance of nonparametric species richness estimators in a high diversity plant community

Abstract. The efficiency of four nonparametric species richness estimators — first‐order Jackknife, second‐order Jackknife, Chao2 and Bootstrap — was tested using simulated quadrat sampling of two field data sets (a sandy ‘Dune’ and adjacent ‘Swale’) in high diversity shrublands (kwongan) in south‐western Australia. The data sets each comprised > 100 perennial plant species and > 10 000 individuals, and the explicit (x‐y co‐ordinate) location of every individual. We applied two simulated sampling strategies to these data sets based on sampling quadrats of unit sizes 1/400th and 1/100th of total plot area. For each site and sampling strategy we obtained 250 independent sample curves, of 250 quadrats each, and compared the estimators’ performances by using three indices of bias and precision: MRE (mean relative error), MSRE (mean squared relative error) and OVER (percentage overestimation). The analysis presented here is unique in providing sample estimates derived from a complete, field‐based population census for a high diversity plant community. In general the true reference value was approached faster for a comparable area sampled for the smaller quadrat size and for the swale field data set, which was characterized by smaller plant size and higher plant density. Nevertheless, at least 15–30% of the total area needed to be sampled before reasonable estimates of S_t (total species richness) were obtained. In most field surveys, typically less than 1% of the total study domain is likely to be sampled, and at this sampling intensity underestimation is a problem. Results showed that the second‐order Jackknife approached the actual value of S_t more quickly than the other estimators. All four estimators were better than S_obs (observed number of species). However, the behaviour of the tested estimators was not as good as expected, and even with large sample size (number of quadrats sampled) all of them failed to provide reliable estimates. First‐ and second‐order Jackknives were positively biased whereas Chao2 and Bootstrap were negatively biased. The observed limitations in the estimators’ performance suggests that there is still scope for new tools to be developed by statisticians to assist in the estimation of species richness from sample data, especially in communities with high species richness.     

Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot

Macro‐scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample‐size‐correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species‐rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second‐order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with “big data” collections.     

Evaluating the performance of species richness estimators: sensitivity to sample grain size

1. Fifteen species richness estimators (three asymptotic based on species accumulation curves, 11 nonparametric, and one based in the species-area relationship) were compared by examining their performance in estimating the total species richness of epigean arthropods in the Azorean Laurisilva forests. Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using different sampling units on species richness estimations. 2. Estimated species richness scores depended both on the estimator considered and on the grain size used to aggregate data. However, several estimators (ACE, Chao 1, Jackknifel and 2 and Bootstrap) were precise in spite of grain variations. Weibull and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly. 3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar scores in a number of estimators (the above-mentioned plus ICE, Chao2, Michaelis-Menten, Negative Exponential and Clench). The estimations from those four sample sizes were also highly correlated. 4. Contrary to other studies, we conclude that most species richness estimators may be useful in biodiversity studies. Owing to their inherent formulas, several nonparametric and asymptotic estimators present insensitivity to differences in the way the samples are aggregated. Thus, they could be used to compare species richness scores obtained from different sampling strategies. Our results also point out that species richness estimations coming from small grain sizes can be directly compared and other estimators could give more precise results in those cases. We propose a decision framework based on our results and on the literature to assess which estimator should be used to compare species richness scores of different sites, depending on the grain size of the original data, and of the kind of data available (species occurrence or abundance data).     

Assessing the performance of nonparametric estimators of species richness in meadows

To accurately measure the number of species in a biological community, a complete inventory should be performed, which is generally unfeasible; hopefully, estimators of species richness can help. Our main objectives were (i) to assess the performance of nonparametric estimators of plant species richness with real data from a small set of meadows located in the Basque campiña (northern Spain), and (ii) to apply the best estimator to a larger dataset to test the effects on plant species richness caused by environmental conditions and human practices. Two non-asymptotic and seven asymptotic accumulation functions were fitted to a randomized sample-based rarefaction curve computed with data from three well sampled meadows, and information theoretic methods were used to select the best fitting model; this was the Morgan-Mercer-Flodin, and its asymptote was taken as our best guess of true richness. Then, five nonparametric estimators were computed: ICE, Chao 2, Jackknife 1 and 2, and Bootstrap; MMRuns and MMMeans were also assessed. According to the criteria set for our performance assessment (i.e., bias, precision, and accuracy), the best estimator was Jackknife 1. Finally, Jackknife 1 was applied to assess the effects of terrain slope and soil parent material, and also fertilization, grazing, and mowing, on plant species richness from a larger dataset (20 meadows). Results suggested that grass cutting was causing a loss of richness close to 30%, as compared to unmowed meadows. It is concluded that the use of nonparametric estimators of species richness can improve the evaluation of biodiversity responses to human management practices.     

Accuracy of species richness estimators applied to fish in small and large temperate lowland rivers

The performance of non-parametric species richness estimators (Program SPADE) was assessed by applying them to fish from two lowland waterways in Poland: (1) a stream sampled annually at one site for 23 years (13 of which were after the stream was turned into a canal), and (2) a river sampled twice annually at two sites (one natural, the other impounded) for 16 years. On each sampling occasion consecutive electrofishing runs were made and the species richness of the total sample (obtained in all the runs) was predicted by each estimator from the sub-sample of the first run. The estimators were applied to all of the samples collected in each waterway, which were referred to as the ‘rich group survey’ selection, and to two smaller selections, named the ‘improved survey’ and ‘complete survey’. The performance was evaluated using the measures PAR (percent of actual richness) and SRMSE (scaled root mean square error). Overall, the HM and Chao1-bc estimators were decisively better than others, and ACE1 and ACE were decisively worse both in terms of PAR and SRMSE. In the stream, the bed regulation little affected the performance of the estimators, but they were more correct when applied to the ‘improved survey’ selection rather than to the ‘rich group survey’ selection. In the river, the performance of most of the estimators, both in terms of PAR and SRMSE, was much improved only by selecting those samples for analysis that complied with the Chao-2 criterion (i.e., ‘complete survey’ selection).     

Estimating species richness using environmental DNA

The foundation for any ecological study and for the effective management of biodiversity in natural systems requires knowing what species are present in an ecosystem. We assessed fish communities in a stream using two methods, depletion‐based electrofishing and environmental DNA metabarcoding (eDNA) from water samples, to test the hypothesis that eDNA provides an alternative means of determining species richness and species identities for a natural ecosystem. In a northern Indiana stream, electrofishing yielded a direct estimate of 12 species and a mean estimated richness (Chao II estimator) of 16.6 species with a 95% confidence interval from 12.8 to 42.2. eDNA sampling detected an additional four species, congruent with the mean Chao II estimate from electrofishing. This increased detection rate for fish species between methods suggests that eDNA sampling can enhance estimation of fish fauna in flowing waters while having minimal sampling impacts on fish and their habitat. Modern genetic approaches therefore have the potential to transform our ability to build a more complete list of species for ecological investigations and inform management of aquatic ecosystems.     

Efficient sampling of plant diversity in arid deserts using non-parametric estimators

Surveying plant diversity in arid desert areas is extremely difficult because of the harsh climate, hostile terrain, lack of roads, and insecurity, which is why it is particularly important to improve the sampling efficiency, but few relevant studies have been done. The performance of non-parametric estimators was assessed with first-hand field data to determine (a) the threshold of the proportion of uniques (number of species that occur in exactly one plot divided by the number of species sampled) that involves the least sampling effort and (b) the method of locating plots to obtain a more reliable estimate of species richness. The study area (Gurbantunggut desert, China) was divided into five sub-regions based on variation in physical environment and vegetation. The following common correction factors were selected: ACE, Chao1, Bootstrap, Chao2, ICE, Jack1, and Jack2. The estimates for each sub-region (partition) and for the entire region (without partition), the threshold of proportion of uniques, and the method of determining sampling locations (including prior sampling of plots that show large differences in habitats) were compared in terms of their ability to predict the number of species more accurately. We found that ACE and Chao1 (which use abundance data) showed more biased estimates than the other factors (incidence data), and best estimator is Jack1. Species richness was significantly underestimated for the region, but the non-parametric estimators could estimate the species richness for each sub-region reliably. Sampling locations affected the performance of non-parametric estimators significantly. The threshold of minimum sampling was 15% and that of uniques was 30%; the two were able to limit the bias within 5 and 10%, respectively. It is concluded that the non-parametric estimators can estimate the plant diversity of arid deserts reliably from the data on incidence. The study area (on the scale of a region) should be partitioned to improve the performance of the non-parametric estimators. The plots with larger differences in habitats should be sampled more extensively based on the threshold of the proportion of uniques.     

Testing species-richness estimation methods on single-sample collection data using the Danish Diptera

Estimating species richness through extrapolation is becomingincreasingly important for conservation decision making. We present the resultsof a first test of four abundance-based estimation procedures, ACE, Chao1, Lognormal and Poisson lognormal based on single-sample museum collection data consisting of more than 150000specimens of 47 families of Danish Diptera. All four estimators considerablyunderestimate true species richness as assessed by species distributions, expertopinions, and a species–area curve. In our samples 3326 species wererepresented. The different estimators predicted the Danish fauna to consist of3490–3805 species, although at least 4361 are already known from theliterature. Expert opinion and the species–area curve indicate that theDanish fauna likely contains 5400–5800 species. The Poisson lognormalmethod displays a rather erratic behavior, but nonetheless performs slightlybetter than the other estimators. We discuss the inherent problems concerningthe use of collection data in this context as well as the influence of patchydistributions and sample size on estimator performance. We conclude thatabundance-based estimators should preferably be applied to almost completesamples of randomly distributed organisms.     

Comparative performance of species-richness estimators using data from a subtropical forest tree community

We used survey data collected from a large plot (20 ha) of sub-tropical forest in the Dinghushan Nature Reserve, Guangdong Province, southern China, in 2005 to test the comparative performance of nine species-richness estimators (number of observed species, three species-individual curve models, five nonparametric estimators). As the true species richness, we used the 210 free-standing shrub and tree species of >1 cm diameter at breast height recorded during the survey. This true species richness was then used to calculate performance measures of bias, accuracy, and precision for each estimator, whereby we distinguished performance for low, medium, and high sampling intensity. Unsurprisingly, all estimators performed better than the number of observed species in terms of bias and accuracy. Surprisingly, however, two curve models (logistic and logarithm) outperformed all other estimators in terms of bias, accuracy, and precision, which is in contrast to most other previous studies, in which nonparametric methods usually outperform curve models. Intriguingly, relative estimator performance changed between low, medium, and high sampling intensity, sometimes dramatically, reinforcing the assertion that the influence of sampling intensity on estimator performance is an important aspect to investigate and to consider when choosing estimators for ecological surveys. Because these results are based on only one dataset, the results should be treated with caution, both because (1) the generality of these results needs to be confirmed with simulated datasets and (2) more work is needed to establish what “true” species richness is extrapolated by each of the tested estimators in both the statistical and the practical sense. Nevertheless, the two curve estimators, namely Logistic and Logarithm, should be considered in future studies of comparative performance of species-richness estimators because of their outstanding performance in this study.     

Species richness patterns of obligate subterranean beetles (Insecta: Coleoptera) in a global biodiversity hotspot – effect of scale and sampling intensity

We studied species richness patterns of obligate subterranean (troglobiotic) beetles in the Dinaric karst of the western Balkans, using five grid sizes with cells of 80 × 80, 40 × 40, 20 × 20, 10 × 10, and 5 × 5 km. The same two hotspots could be recognized at all scales, although details differed. Differences in sampling intensity were not sufficient to explain these patterns. Correlations between number of species and number of sampled localities increased with increasing cell size. Additional species are expected to be found in the region, as indicated by jackknife 1, jackknife 2, Chao2, bootstrap, and incidence‐based coverage (ICE) species richness estimators. All estimates increased with increasing cell size, except Chao2, with the lowest prediction at the intermediate 20 × 20 km cell size. Jackknife 2 and ICE gave highest estimates and jackknife 1 and bootstrap the lowest. Jackknife 1 and bootstrap estimates changed least with cell size, while the number of single cell species increased. In the highly endemic subterranean fauna with many rare species, bootstrap may be most appropriate to consider. Positive autocorrelation of species numbers was highest at 20 × 20 km scale, so we used this cell size for further analyses. At this scale we added 137 localities with less positional accuracy to 1572 previously considered, and increased 254 troglobiotic species considered to 276. Previously discovered hotspots and their positions did not change, except for a new species‐rich cell which appeared in the south‐eastern region. There are two centres of troglobiotic species richness in the Dinaric karst. The one in the north‐west exhibited high species richness of Trechinae (Carabidae), while in the south‐east, the Leptodirinae (Cholevidae) were much more diverse. These centres of species richness should serve as the starting point for establishing a conservation network of important subterranean areas in Dinaric karst.     

Estimating the abundance of rare and elusive carnivores from photographic-sampling data when the population size is very small

Conservation and management agencies require accurate and precise estimates of abundance when considering the status of a species and the need for directed actions. Due to the proliferation of remote sampling cameras, there has been an increase in capture–recapture studies that estimate the abundance of rare and/or elusive species using closed capture–recapture estimators (C–R). However, data from these studies often do not meet necessary statistical assumptions. Common attributes of these data are (1) infrequent detections, (2) a small number of individuals detected, (3) long survey durations, and (4) variability in detection among individuals. We believe there is a need for guidance when analyzing this type of sparse data. We highlight statistical limitations of closed C–R estimators when data are sparse and suggest an alternative approach over the conventional use of the Jackknife estimator. Our approach aims to maximize the probability individuals are detected at least once over the entire sampling period, thus making the modeling of variability in the detection process irrelevant, estimating abundance accurately and precisely. We use simulations to demonstrate when using the unconditional-likelihood M ₀ (constant detection probability) closed C–R estimator with profile-likelihood confidence intervals provides reliable results even when detection varies by individual. If each individual in the population is detected on average of at least 2.5 times, abundance estimates are accurate and precise. When studies sample the same species at multiple areas or at the same area over time, we suggest sharing detection information across datasets to increase precision when estimating abundance. The approach suggested here should be useful for monitoring small populations of species that are difficult to detect.     

Estimating species richness: still a long way off!

Xu et al., in this issue of the Journal of Vegetation Science, compare several species richness estimators. All the non‐parametric estimators, such as Chao and jackknife estimators, underestimated the true number, whereas all the area‐based models, based on species–area curves, overestimated it. No reliable method yet exists to predict the number of species in an area that is appreciably larger than the one(s) sampled.     

Responses of the soil fungal communities to the co‐invasion of two invasive species with different cover classes

• Soil fungal communities play an important role in the successful invasion of non‐native species. It is common for two or more invasive plant species to co‐occur in invaded ecosystems.
• This study aimed to determine the effects of co‐invasion of two invasive species (Erigeron annuus and Solidago canadensis) with different cover classes on soil fungal communities using high‐throughput sequencing.
• Invasion of E. annuus and/or S. canadensis had positive effects on the sequence number, operational taxonomic unit (OTU) richness, Shannon diversity, abundance‐based cover estimator (ACE index) and Chao1 index of soil fungal communities, but negative effects on the Simpson index. Thus, invasion of E. annuus and/or S. canadensis could increase diversity and richness of soil fungal communities but decrease dominance of some members of these communities, in part to facilitate plant further invasion, because high soil microbial diversity could increase soil functions and plant nutrient acquisition. Some soil fungal species grow well, whereas others tend to extinction after non‐native plant invasion with increasing invasion degree and presumably time. The sequence number, OTU richness, Shannon diversity, ACE index and Chao1 index of soil fungal communities were higher under co‐invasion of E. annuus and S. canadensis than under independent invasion of either individual species.
• The co‐invasion of the two invasive species had a positive synergistic effect on diversity and abundance of soil fungal communities, partly to build a soil microenvironment to enhance competitiveness of the invaders. The changed diversity and community under co‐invasion could modify resource availability and niche differentiation within the soil fungal communities, mediated by differences in leaf litter quality and quantity, which can support different fungal/microbial species in the soil.

     

Species richness and abundance among macromoths: A comparison of taxonomic,temporal and spatial patterns in Oregon and South Korea

The present study aims to establish a long‐term intercontinental collaboration based on a sampling protocol using standardized repeated measures at permanent sites to document macromoth species richness and abundance through time and across the landscape. We pooled the data from two continental regions providing a total of 12 trap sites: Mt. Jirisan National Park in South Korea (2005–2007) and HJ Andrews Experimental Forest in Oregon, USA. (2004–2006). A synthesis of our data indicated that: (i) noctuids (43–52%) and geometrids (33–39%) dominated the measures of species richness; (ii) using our sampling protocols more than three years would be needed to obtain a value of 90% of empirical species richness relative to Chao‐1 estimated species richness; (iii) temperature alone could not explain the peak pattern in moth abundance and species richness; (iv) the highest/lowest proportion of species richness and abundance were present in similar elevation and forest sites. These observations established a foundation for developing a network‐oriented database for assessing biotic impact of environmental and contributed to identifying species at high risk to environmental change based on empirical measures of temporal and spatial breadth.