How to identify future sampling areas when information is biased and scarce: An example using predictive models for species richness of Iberian water beetles

Sánchez‐Fernández, David; Lobo, Jorge M.; Abellán, Pedro; Millán, Andrés

doi:10.1016/j.jnc.2010.05.003

Cited by 17 publications

(24 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These areas can then be targeted for surveying. Due to the current budget limitations for inventorying and completing databases with new records, it is imperative to design suitable surveys (Sánchez-Fernández et al, 2011) and monitoring programs (Boero et al, 2015) that optimize the available resources into a significant increase of biodiversity information. Our results showed that records from abundant species are prone to a lower decrease whereas records from rare species show a greater rate of attrition.…”

Section: Discussionmentioning

confidence: 99%

Biodiversity data obsolescence and land uses changes

Escribano

Ariño

Galicia

2016

PeerJ

View full text Add to dashboard Cite

BackgroundPrimary biodiversity records (PBR) are essential in many areas of scientific research as they document the biodiversity through time and space. However, concerns about PBR quality and fitness-for-use have grown, especially as derived from taxonomical, geographical and sampling effort biases. Nonetheless, the temporal bias stemming from data ageing has received less attention. We examine the effect of changes in land use in the information currentness, and therefore data obsolescence, in biodiversity databases.MethodsWe created maps of land use changes for three periods (1956–1985, 1985–2000 and 2000–2012) at 5-kilometres resolution. For each cell we calculated the percentage of land use change within each period. We then overlaid distribution data about small mammals, and classified each data as ‘non-obsolete or ‘obsolete,’ depending on both the amount of land use changes in the cell, and whether changes occurred at or after the data sampling’s date.ResultsA total of 14,528 records out of the initial 59,677 turned out to be non-obsolete after taking into account the changes in the land uses in Navarra. These obsolete data existed in 115 of the 156 cells analysed. Furthermore, more than one half of the remaining cells holding non-obsolete records had not been visited at least for the last fifteen years.ConclusionLand use changes challenge the actual information obtainable from biodiversity datasets and therefore its potential uses. With the passage of time, one can expect a steady increase in the availability and use of biological records—but not without them becoming older and likely to be obsolete by land uses changes. Therefore, it becomes necessary to assess records’ obsolescence, as it may jeopardize the knowledge and perception of biodiversity patterns.

show abstract

Section: Discussionmentioning

confidence: 99%

Biodiversity data obsolescence and land uses changes

Escribano

Ariño

Galicia

2016

PeerJ

View full text Add to dashboard Cite

show abstract

“…The discrimination and recognition of poorly surveyed areas as well as the study of the biases associated with the available information are key tasks necessary for the design of more efficient survey strategies (Sánchez-Fernández et al 2011). In this sense, these results provide a basis for the design of future sampling efforts by enabling the identification of under-sampled regions.…”

Section: Some Conservation Implicationsmentioning

confidence: 99%

Assessing the quality and usefulness of different taxonomic groups inventories in a semiarid Mediterranean region

et al. 2012

Self Cite

View full text Add to dashboard Cite

Extensive biological databases are valuables ecological research tools that form the basis of biodiversity studies. However, it is essential to perform an assessment of the inventories' completeness for their use in ecological and conservational research, and this is especially true for non-emblematic groups. Using four exhaustive databases compiled for four taxonomic groups (aquatic beetles, aquatic bugs, bryophytes and orchids), in a semiarid Mediterranean region, the aim of this study was to estimate the degree of completeness for the inventory of each taxa and to identify those spatial units that could be considered to be sufficiently-surveyed (UTM 10 9 10 km squares). Then, the degree of environmental representativeness of the databases was assessed, as well as those factors that could have caused biased sampling efforts. Lastly, the usefulness of each database for conservational purposes was discussed. The results of the present study highlighted the lack of complete and extensive inventory data; as the best sampled group did not even reach 25% of sufficiently-surveyed squares in the territory (in the case of aquatic bugs) and none of the squares presented reliable inventories in the case of bryophytes. Although these results suggested that recording was skewed by relatively simple climatic variables, the sufficiently-surveyed squares were evenly distributed across physioclimatic subregions, what enables their use in further ecological studies. The authors would like to emphasise the potential of these procedures to locate areas in need of further sampling as well as to aid in the design of more effective regional conservation schemes.Electronic supplementary material The online version of this article (

show abstract

“…Most of these studies, however, were performed at rather fine spatial scales (e.g. regional or landscape), while less attention has been devoted to broad-scale analyses of data quality, and to the impacts on macroecological analyses (but see Sánchez-Fernández et al, 2011;Foody, 2011;Kéry, 2011;Comte & Grenouillet, 2013;Yang et al, 2013;Lahoz-Monfort et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Sampling bias inverts ecogeographical relationships in island reptiles

Ficetola

Cagnetta

Padoa‐Schioppa

et al. 2014

Global Ecology and Biogeography

View full text Add to dashboard Cite

Aim Species richness is one of the commonest measures of biodiversity, and is a basis for analyses at multiple scales. Data quality may affect estimations of species richness, but most broad‐scale studies do not take sampling biases into account. We analysed reptile richness on islands that have received different sampling efforts, and assessed how inventory completeness affects the results of ecogeographical analyses. We also used simulations to evaluate under what circumstances insufficient sampling can bias the outcome of biodiversity analyses. Location Mediterranean islands. Methods We gathered data on reptile richness from 974 islands, assuming better sampling in islands with specific inventories. We used Moran's eigenvector mapping to analyse the factors that determine whether an island has been surveyed, and to identify the relationships between reptile richness, geographical parameters and anthropic parameters. We simulated islands, mimicking patterns of true data, and sampled them with varying effort. Simulated richness was analysed using the same approach used for real‐world data. Results The probability that islands were sampled for reptiles was higher in large, human‐populated islands. The relationship between human impact and reptile richness was negative in well‐surveyed islands, but positive in islands that had not been systematically surveyed, because densely populated and accessible islands receive better sampling. In simulations, analyses successfully retrieved the relationships between species richness and human presence only if the average species detection probability was ≥75%. Poorer sampling resulted in biased regression results. Main conclusions Human activities may strongly affect biodiversity, but human presence and accessibility improve sampling effort and thus the quality of biodiversity information. Therefore, regressing known species richness on parameters representing human presence may result in apparent positive relationships. These two facets of human presence (positive on biodiversity knowledge, negative on actual biodiversity) represent a major challenge for ecogeographical studies, as not taking them into account would bias analyses and underestimate human impact.

show abstract

How to identify future sampling areas when information is biased and scarce: An example using predictive models for species richness of Iberian water beetles

Cited by 17 publications

References 57 publications

Biodiversity data obsolescence and land uses changes

Biodiversity data obsolescence and land uses changes

Assessing the quality and usefulness of different taxonomic groups inventories in a semiarid Mediterranean region

Sampling bias inverts ecogeographical relationships in island reptiles

Contact Info

Product

Resources

About