Role of a geodatabase to assess the distribution of plants of conservation interest in a large protected area: A case study for a major national park in Italy

We use a geodatabase to investigate the distribution patterns of an important subset of floristic reports recorded for the Parco Nazionale delle Foreste Casentinesi, Monte Falterona, Campigna in the northern Apennines, Italy. This database was analysed using spatial statistical techniques and a digital elevation model. Significant relationships between species presence, sampling effort and species richness were then analysed in relation to topographical variables and to an existing vegetation map. Report-based rarefaction techniques were used to compare areas having different numbers of species recorded. Overall, the analysis shows that some areas of the park are richer in species of conservation interest than others, and that these have been more intensely investigated. Meanwhile, for other areas, botanical knowledge is scarce or even absent. This has led to clustering and redundancy of floristic data in some areas. The study confirms that the existence of a complete and up-to-date geodatabase creates a valuable resource which enables information gaps to be bridged. Such gaps often exist in biological databases for rare and narrowly distributed species. The wider application of these analyses should also give useful indications of how the incidences of these species of conservation interest are associated with particular environmental variables.     

Using Museum Collections to Estimate Diversity Patterns along Geographical Gradients

Quantifying species-richness patterns along geographical gradients (typically latitude and elevation) has a long history in ecology and can be based on more-or-less complete censuses from a specified area (plot sampling), selective collection within a specified area (e.g. museum collections), or general information about species distributions (e.g. observations of extremes along the gradient, distribution maps). All these approaches require complete sampling to give the true richness in an area, but the richness pattern (i.e., the relative changes in richness along the gradient) may be estimated without complete sampling, although equal sampling between areas is necessary. This is relatively easy to do for fine-scale plot sampling, but rarely easy for other types of data. For data extracted from museum collections, a correct perception of the species richness pattern therefore depends on post-sampling treatment of data. Two commonly applied techniques for quantifying species richness patterns with these types of data are discussed, namely interpolation of species ranges and rarefaction. Such treatment may correct for unequal sampling in some instances, but may in other cases introduce artificial patterns. With incomplete sampling interpolation introduces an artificial humped pattern and rarefaction requires similar species abundance distributions to make unbiased comparisons among samples. One must therefore be cautious when applying these methods for estimating species richness patterns along geographical gradients.     

Impact of biased sampling effort and spatial uncertainty of locations on models of plant invasion patterns in Croatia

Very frequently biological databases are used for analysing distribution of different taxa. These databases are usually the result of variable sampling effort and location uncertainty. The aim of this study was to test the influence of geographically biased sampling effort and spatial uncertainty of locations on models of species richness. For this purpose, we assessed the pattern of invasive alien plants in Croatia using the Flora Croatica Database. The procedure applied in testing of the sensitivity of models consisted of sample area sectioning into coherent ecological classes (hereinafter Gower classes). The quadrants were then ranked based on sampling effort per class. This resulted in creation of models using varying numbers of quadrants whose performance was tested with independent validation points. From this the best fitting model was determined, as well as a threshold of sampling effort. The data from quadrants with sampling effort below the threshold were considered too unreliable for modelling. Further, spatial uncertainty was simulated by adding a random term to each location and re-running the models using the simulated locations. Biased sampling effort and spatial uncertainty of locations had similar effects on model performance in terms of the magnitude of the affected area, as in both cases 7% of the quadrants showed statistically significant deviations in alien plant species richness. The model using only on the quandrants with the highest 35% quantile sampling effort best balanced the sampling effort per quadrant and overall geographical coverage. It predicted a mean number of 3.2 invasive alien plant species per quadrant for the Alpine region, 5.2 for the Continental, 6.1 for the Mediterranean and 5.3 for the Pannonian region of Croatia. Thus, the observational databases can be considered as a reliable source for species richness models and, most likely, for other types of species distribution models, given that their limitations are accounted for in the data selection process. In order to obtain precise estimates of species richness it is required to sample the whole range of ecological conditions of the study area.     

The influence of sampling intensity on the perception of the spatial distribution of tropical diversity and endemism: a case study of ferns from Bolivia

The distribution of tropical plant and animal diversity is still poorly documented, especially at spatial resolutions of practical use for conservation. In the present study, we evaluated the level to which geographical incomplete data availability of species occurrence affects the perception of biodiversity patterns (species richness and endemism) among pteridophytes in Bolivia. We used a data base of Bolivian pteridophytes (27,501 records), divided it into three time periods (1900–70, up to 1990 and up to 2006), and created grid-files at 15'-resolution for species richness and endemism. For each of these biodiversity properties we estimated the species richness (Chao 2) and the index of sampling completeness (C index) per grid, and then all these variables at both species richness and endemism were correlated. Patterns of richness were fairly consistent along all periods; the richest areas were placed along the humid-montane forest, even though they were strongly influenced by collecting intensity. Endemism had a lower degree of correlation with collecting intensity, but varied much more strongly through time than species richness. According to the C index, which gives the ratio between estimated (by Chao 2) and recorded values of species richness and endemism, both biodiversity properties tended to be undersampled in the richest grid cells. Inter-temporal correlations showed sharper differences of correlations for endemism than species richness. Consequently, already in 1970, botanists had a correct idea of the spatial distribution of pteridophyte richness in Bolivia (even though the magnitude was grossly underestimated). In contrast, patterns of endemism, which are of high conservation importance, may not even today be reliably known.     

Constant tree species richness along an elevational gradient of Mt. Bokor,a table-shaped mountain in southwestern Cambodia

Some previous studies along an elevational gradient on a tropical mountain documented that plant species richness decreases with increasing elevation. However, most of studies did not attempt to standardize the amount of sampling effort. In this paper, we employed a standardized sampling effort to study tree species richness along an elevational gradient on Mt. Bokor, a table-shaped mountain in southwestern Cambodia, and examined relationships between tree species richness and environmental factors. We used two methods to record tree species richness: first, we recorded trees taller than 4 m in 20 uniform plots (5 × 100 m) placed at 266–1048-m elevation; and second, we collected specimens along an elevational gradient from 200 to 1048 m. For both datasets, we applied rarefaction and a Chao1 estimator to standardize the sampling efforts. A generalized linear model (GLM) was used to test the relationship of species richness with elevation. We recorded 308 tree species from 20 plots and 389 tree species from the general collections. Species richness observed in 20 plots had a weak but non-significant correlation with elevation. Species richness estimated by rarefaction or Chao1 from both data sets also showed no significant correlations with elevation. Unlike many previous studies, tree species richness was nearly constant along the elevational gradient of Mt. Bokor where temperature and precipitation are expected to vary. We suggest that the table-shaped landscape of Mt. Bokor, where elevational interval areas do not significantly change between 200 and 900 m, may be a determinant of this constant species richness.     

Elevational species richness patterns for vascular plants on Mount Kinabalu, Borneo

Aim  We quantify the elevational patterns of species richness for all vascular plants and some functional and taxonomic groups on a regional scale on a tropical mountain and discuss some possible causes for the observed patterns.
Location  Mount Kinabalu, Sabah, Borneo.
Methods  A data base containing elevational information on more than 28,000 specimens was analysed for vascular plant distribution, taking into account sampling effort. The total species richness pattern was estimated per 300-m elevational interval by rarefaction analyses. The same methods were also applied to quantify species richness patterns of trees, epiphytes, and ferns.
Results  Total species richness has a humped relationship with elevation, and a maximum species richness in the interval between 900 and 1200 m. For ferns and epiphytes the maximum species richness is found at slightly higher elevations, whereas tree species did not have a statistically significant peak in richness above the lowest interval analysed.
Main conclusions  For the first time a rigorous estimate of an elevational pattern in species richness of the whole vascular plant flora of a tropical mountain has been quantified. The pattern observed depends on the group studied. We discuss the differences between the groups and compare the results with previous studies of elevational patterns of species richness from other tropical areas. We also discuss the methods used to quantify the richness pattern and conclude that rarefaction gives an appropriate estimate of the species richness pattern.     

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.     

Incorporating spatial autocorrelation in rarefaction methods: Implications for ecologists and conservation biologists

Recently, methods for constructing Spatially Explicit Rarefaction (SER) curves have been introduced in the scientific literature to describe the relation between the recorded species richness and sampling effort and taking into account for the spatial autocorrelation in the data. Despite these methodological advances, the use of SERs has not become routine and ecologists continue to use rarefaction methods that are not spatially explicit. Using two study cases from Italian vegetation surveys, we demonstrate that classic rarefaction methods that do not account for spatial structure can produce inaccurate results. Furthermore, our goal in this paper is to demonstrate how SERs can overcome the problem of spatial autocorrelation in the analysis of plant or animal communities. Our analyses demonstrate that using a spatially-explicit method for constructing rarefaction curves can substantially alter estimates of relative species richness. For both analyzed data sets, we found that the rank ordering of standardized species richness estimates was reversed between the two methods. We strongly advise the use of Spatially Explicit Rarefaction methods when analyzing biodiversity: the inclusion of spatial autocorrelation into rarefaction analyses can substantially alter conclusions and change the way we might prioritize or manage nature reserves.     

Diversity and distribution of reptiles in Romania

The reptile fauna of Romania comprises 23 species, out of which 12 species reach here the limit of their geographic range. We compiled and updated a national database of the reptile species occurrences from a variety of sources including our own field surveys, personal communication from specialists, museum collections and the scientific literature. The occurrence records were georeferenced and stored in a geodatabase for additional analysis of their spatial patterns. The spatial analysis revealed a biased sampling effort concentrated in various protected areas, and deficient in the vast agricultural areas of the southern part of Romania. The patterns of species richness showed a higher number of species in the warmer and drier regions, and a relatively low number of species in the rest of the country. Our database provides a starting point for further analyses, and represents a reliable tool for drafting conservation plans.     

Sampling effort and species richness assessment: a case study on Brazilian spiders

The knowledge on the geographical distribution of species is essential for building biogeographical and macroecological hypotheses. However, information on this regard is not distributed uniformly in space and usually come from biased sampling. The aim of this study is to quantify the influence of spatial distribution of sampling effort on the assessment of spider species richness in Brazil. We used a database of spider distribution records in Brazil, based on the taxonomic and biodiversity survey literature. The results show that the Atlantic Forest was better sampled and had the highest spider species richness among the Brazilian biomes. The Amazon, though having large collecting gaps and high concentration of records around major cities and rivers, showed the second highest number of species. The Pampa had a large number of records, but these are concentrated near a major city in the transition zone with the Atlantic Forest. The Cerrado, Caatinga and Pantanal had shown to be poorly sampled and, consequently, were among the lesser known biomes regarding the spider fauna. A linear regression analysis showed that the spider species richness in Brazil is strongly correlated to the number of records. However, we have identified areas potentially richest in species, which strongly deviate from the predicted by our analyses. Our results show that it is possible to access the spatial variation in species richness, as long as the variation in sampling effort is taken into account.     

Species richness and rarity of crane flies (Diptera, Tipuloidea) in a boreal mire

Species richness and abundance are central in biodiversity inventories and in measuring the structure of communities. Neglecting the assessment of sampling efficiency may lead to spurious estimates of species richness and conservation value. Our aim was to examine species richness, sampling effectiveness, species-abundance distribution (SAD) and rarity of a boreal, mire-dwelling crane fly (Diptera, Tipuloidea) assemblage in western Finland. 12 Malaise traps dispersed in 4 subplots and standardized sweep net samples were used to collect adult flies from the mire. A total of 23 species and 1,569 specimens were identified. In general all species richness estimators were highly correlated and indicated rather good sampling effort. Sample completeness, expressed as percentage of observed richness divided by estimated richness, was higher for mire-dwellers (mean 75 %) than for all species (mean 63 %). Crane fly assemblages of subplots and combined data fitted best with log-series SAD. Species spatial distribution was positively correlated with average abundance. In other words, the most abundant species occurred in the most of Malaise traps. Seven mire-dwelling species greatly outnumbered (94 % of the collected specimens) all other members in the assemblage, and only one observed species was rare by several definitions (local abundance, extent of occurrence in Finland and area of occupancy). Although the studied assemblage was characterized by commonness, five of the species have threatened status in Europe south of Finland. Separate species richness estimation of all species (vagrants and occasional species included) and focal species (here mire-dwellers) is supported if ecological information is available on the taxonomic group being studied.     

The challenge of accurately documenting bee species richness in agroecosystems: bee diversity in eastern apple orchards

Bees are important pollinators of agricultural crops, and bee diversity has been shown to be closely associated with pollination, a valuable ecosystem service. Higher functional diversity and species richness of bees have been shown to lead to higher crop yield. Bees simultaneously represent a mega‐diverse taxon that is extremely challenging to sample thoroughly and an important group to understand because of pollination services. We sampled bees visiting apple blossoms in 28 orchards over 6 years. We used species rarefaction analyses to test for the completeness of sampling and the relationship between species richness and sampling effort, orchard size, and percent agriculture in the surrounding landscape. We performed more than 190 h of sampling, collecting 11,219 specimens representing 104 species. Despite the sampling intensity, we captured <75% of expected species richness at more than half of the sites. For most of these, the variation in bee community composition between years was greater than among sites. Species richness was influenced by percent agriculture, orchard size, and sampling effort, but we found no factors explaining the difference between observed and expected species richness. Competition between honeybees and wild bees did not appear to be a factor, as we found no correlation between honeybee and wild bee abundance. Our study shows that the pollinator fauna of agroecosystems can be diverse and challenging to thoroughly sample. We demonstrate that there is high temporal variation in community composition and that sites vary widely in the sampling effort required to fully describe their diversity. In order to maximize pollination services provided by wild bee species, we must first accurately estimate species richness. For researchers interested in providing this estimate, we recommend multiyear studies and rarefaction analyses to quantify the gap between observed and expected species richness.     

Current knowledge of fungi from Neotropical montane cloud forests: distributional patterns and composition

Montane cloud forests in the Neotropics harbor a great wealth of biological diversity and a large number of endemic species. Here, we present (i) a comprehensive data mining exercise of fungi from Neotropical montane cloud forests (NMCF), (ii) an extensive review of the current knowledge of fungal richness, distribution and composition, and (iii) a preliminary analysis of fungal endemicity in Mexican montane cloud forests. Based on a survey of literature and other sources, we assembled a database of 6349 records representing 2962 fungal species in NMCF. The computed individual-based species rarefaction curve remained non-asymptotic, and the extrapolation curve estimated an expected increment of 42% in the number of species by doubling the sampling effort. Fungal species richness was highest in NMCF from Mesoamerica, particularly from Mexico and Costa Rica. Fungi from Mesoamerica, Caribbean and South America are significantly different at diverse taxonomic levels, and there is a little overlap in the fungal species recorded from these regions. The analyses of endemicity of the Mexican dataset performed with parsimony and Bayesian methods were highly complementary. They showed the following areas of endemicity supported by the congruent distribution of fungal species: (i) two main regions in the Trans-Mexican Volcanic Belt (TMVB); (ii) a region in the southern part of Veracruz; and (iii) a region located in the eastern part of TMVB with affinities with Sierra Madre Oriental and the Chiapan-Guatemala Highlands. This last area was supported by five species of Glomeromycota and is consistent with an area of endemicity previously found in vascular plants. In this study, we provide a perspective on gaps in knowledge regarding the diversity and distribution of fungi in NMCF, and provide a full dataset of fungal records with geographical, bibliographic and taxonomic information.     

Additive partitioning of rarefaction curves: Removing the influence of sampling on species-diversity in vegetation surveys

Vegetation surveys collect species-diversity information, a potentially valuable ecological indicator. However, the number of species recorded by vegetation surveys is influenced by several factors including inherent species-diversity, sampling method and sampling effort. The process of rarefaction is commonly used to control for variation in sampling effort. We aimed to use a combination of rarefaction and additive partitioning to control for sampling effort and extract vegetation α-, β- and γ-diversity respectively. The study focused on the Stony Plains region of the South Australian rangelands. Vegetation quadrat survey data were collected for land condition monitoring and species inventory by two government agencies. The analysis revealed a strong residual influence of sampling effort on β- and γ-diversity after rarefaction, a finding not previously reported in the literature. A method was developed to model then remove the residual influence of sampling effort on β- and γ-diversity. Thus the method outlined in this paper allows for the extraction from standard vegetation survey data of α-, β- and γ-diversity and the concurrent removal of sampling effort influence. This method is transferable to any other region for which there is vegetation survey data.     

Temporal Patterns of Species Accumulation in a Survey of Lepidoptera in a Beech-Maple Forest

One of the most significant challenges to insect conservation is lack of information concerning species diversity and distribution. Because a complete inventory of all species in an area is virtually impossible, interest has turned to developing statistical techniques to guide sampling design and to estimate total species richness within a site. We used two such techniques, diversity partitioning and non-parametric richness estimation, to determine how variation in sampling effort over time affected species accumulation for a survey of Lepidoptera in an old-growth beech-maple forest. Temporal scaling of sampling effort had significant effects on two measures of species diversity. Increases in species richness were primarily driven by changes in species occurrences with season, while Shannon diversity was largely determined at the scale of individual sampling units (i.e. by spatial effects). Variation in sampling effort affected the values of the two most widely regarded richness estimators (ICE and Chao 2); neither diversity estimator achieved stable values across a range of sampling efforts. Even after 52 trap-nights and accounting for seasonality, rare species (singletons and uniques) remained a significant component of the moth community. To the extent that moth communities in other forest systems are similarly comprised of many rare species, non-parametric richness estimators should be expected to yield variable estimates with increased effort and should only be used to provide a minimum benchmark for predicting the number of species remaining to be sampled. Our results suggest the best strategy for a short-term survey of forest Lepidoptera should emphasize spreading sampling intervals throughout a given year rather than focusing on intensive sampling during a short time period or prolonged sampling over many years.     

Iberian–Balearic fern regions and their explanatory variables

This article delineates the compositional regions present in the Iberian–Balearic fern flora and compares these regions to previously proposed biogeographic units. It also assesses the extent to which environmental variables could explain the regions and the fern species richness gradients found within them. A combination of 40 previously published and new maps were used to compile the distribution of 123 pteridophytes on a 50 × 50 km UTM grid. Cluster analysis of the resulting 257 squares was used to classify 10 regions based on fern species assemblages. Discriminant function analysis identified the environmental variables that best explained these fern composition regions. Using generalized linear models; the number of species in each square was regressed against topography, climate, geology, environmental diversity, land use and spatial variables within each region. Two main latitudinal pteridophyte zones can be recognized in the Iberian Peninsula. These two zones are longitudinally subdivided into two sub zones. The 10 regions established significantly differ both in species richness and influential environmental variables. Climatic variables discriminate the most among regions, followed by topography, heterogeneity and geology. Pteridophyte richness varies, with richer areas being located along the coast and the main mountain ranges and the poorest areas being in the central plateaus and some north eastern and south western river basins. Species richness variation in Iberia is positively correlated with altitude range, precipitation, maximum altitude and area with siliceous soils. It is negatively correlated with the total annual days of sun, however. The fact that species richness is explained by different variables within each of the 10 regions indicates that the specific factors determining the spatial distribution of species richness vary from region to region. Some coastal regions are poorly explained by the model, and display a negative correlation with the selected causal factors. This finding suggests that persistent historic effects might play a local role in determining species assemblages in these regions. An erratum to this article can be found at     

Geographical sampling bias and its implications for conservation priorities in Africa

Aim To design and apply statistical tests for measuring sampling bias in the raw data used to the determine priority areas for conservation, and to discuss their impact on conservation analyses for the region. Location Sub‐Saharan Africa. Methods An extensive data set comprising 78,083 vouchered locality records for 1068 passerine birds in sub‐Saharan Africa has been assembled. Using geographical information systems, we designed and applied two tests to determine if sampling of these taxa was biased. First, we detected possible biases because of accessibility by measuring the proximity of each record to cities, rivers and roads. Second, we quantified the intensity of sampling of each species inside and surrounding proposed conservation priority areas and compared it with sampling intensity in non‐priority areas. We applied statistical tests to determine if the distribution of these sampling records deviated significantly from random distributions. Results The analyses show that the location and intensity of collecting have historically been heavily influenced by accessibility. Sampling localities show dense, significant aggregation around city limits, and along rivers and roads. When examining the collecting sites of each individual species, the pattern of sampling has been significantly concentrated within and immediately surrounding areas now designated as conservation priorities. Main conclusions Assessment of patterns of species richness and endemicity at the scale useful for establishing conservation priorities, below the continental level, undoubtedly reflects biases in taxonomic sampling. This is especially problematic for priorities established using the criterion of complementarity because the estimated spatial costs of this approach are highly sensitive to sampling artefacts. Hence such conservation priorities should be interpreted with caution proportional to the bias found. We argue that conservation priority setting analyses require (1) statistical tests to detect these biases, and (2) data treatment to reflect species distribution rather than patterns of collecting effort.     

Forecasting insect species richness scores in poorly surveyed territories: the case of the Portuguese dung beetles (Col. Scarabaeinae)

Large-scale biodiversity assessment of faunal distribution is needed in poorly sampled areas. In this paper, Scarabaeinae dung beetle species richness in Portugal is forecasted from a model built with a data set from areas identified as well sampled. Generalized linear models are used to relate the number of Scarabaeinae species in each Portuguese UTM 50 × 50 grid square with a set of 25 predictor variables (geographic, topographic, climatic and land cover) extracted from a geographic information system (GIS). Between-squares sampling effort unevenness, spatial autocorrelation of environmental data, non-linear relationships between variables and an assessment of the models' predictive power, the main shortcomings in geographic species richness modelling, are addressed. This methodological approach has proved to be reliable and accurate enough in estimating species richness distribution, thus providing a tool to identify areas as potential targets for conservation policies in poorly inventoried countries.     

Roles of Spatial Scale and Rarity on the Relationship between Butterfly Species Richness and Human Density in South Africa

Wildlife and humans tend to prefer the same productive environments, yet high human densities often lead to reduced biodiversity. Species richness is often positively correlated with human population density at broad scales, but this correlation could also be caused by unequal sampling effort leading to higher species tallies in areas of dense human activity. We examined the relationships between butterfly species richness and human population density at five spatial resolutions ranging from 2'' to 60'' across South Africa. We used atlas-type data and spatial interpolation techniques aimed at reducing the effect of unequal spatial sampling. Our results confirm the general positive correlation between total species richness and human population density. Contrary to our expectations, the strength of this positive correlation did not weaken at finer spatial resolutions. The patterns observed using total species richness were driven mostly by common species. The richness of threatened and restricted range species was not correlated to human population density. None of the correlations we examined were particularly strong, with much unexplained variance remaining, suggesting that the overlap between butterflies and humans is not strong compared to other factors not accounted for in our analyses. Special consideration needs to be made regarding conservation goals and variables used when investigating the overlap between species and humans for biodiversity conservation.