Combining data from different monitoring schemes is the only realistic option to assess the global state and trends of biodiversity, and an essential step in the progress towards unified, appropriately scaled, adaptive management of biodiversity. These data could be from several sources and from monitoring schemes launched for very different objectives that differ in their level of standardization and in their temporal and spatial scales. Their integration consequently raises several issues.
Benefits of data integration across monitoring schemes
Integration is a way to increase sample size, precision of estimates, and, eventually, statistical power, without increasing the per scheme sampling effort.
Integration across monitoring schemes allows the acquisition of complementary information on ecological processes that determine status and trends of species. This may concern (i) a single biological process for a single (set of) species, (ii) different biological processes for a single (set of) species, (iii) a single biological process for different (sets of) species and taxonomic groups, or (iv) integrating monitoring according to causes of change. Integration of existing monitoring schemes across space has three main benefits: (i) it increases spatial coverage with distributed sampling efforts, (ii) it secures that spatial variation in biodiversity components can be accounted for, and (iii) it facilitates directing new monitoring schemes to areas not yet covered. Integration also allows an increase in temporal coverage.
Main constraints for the integration of monitoring schemes: data heterogeneity and accessibility
Data heterogeneity concerns: (i) biological coverage, such as the recorded variables and the population part surveyed by each monitoring scheme, which may differ among schemes (types of recorded variables, phenology, behaviour, observer skills) (ii) spatial resolution: monitoring schemes may have spatial gaps and/or large differences in spatial coverage, but also biases towards certain habitats, (iii) temporal heterogeneity, which arises from differences in the number and frequency of visits among sites, i.e. in the temporal design among monitoring schemes. Discontinuities in time series are a major issue.
Limited accessibility of data, due to socio-economic or technical reasons, is also a major constraint for spatial and temporal integration of data from heterogeneous monitoring schemes.
General principles for the integration of monitoring data
Combining raw data into a single dataset is possible when data are compatible, i.e. when they are measured in the same unit (or can be reduced to the same unit) and quantify the same biological process. In most cases, however, data are not compatible across monitoring schemes, but temporal trends or other estimates can be combined across datasets.
Regardless of whether raw data or estimates are used, differences in biological, spatial and temporal coverage between monitoring schemes have to be accounted for in statistical analyses by appropriate weighting or averaging if contributions are not equal among schemes, species, or regions. Weighting can have three purposes: (1) formally adjust for differences in precision; (2) compensate for biases (e.g. over-/undersampling of certain habitats) or (3) intentionally alter the contribution of a subset of the data to an indicator. If estimates have different precisions (measured e.g. in standard errors), each estimate can be weighted by its squared standard error. If standard error estimates are not available, surrogates of precision, such as the squared number of monitored sites, or the monitored area per scheme, may be substituted for standard errors. If habitats are not equally represented, weights and post-stratification can be applied. If different species or taxonomic groups are to be combined, several weighting rationales can be considered: no weight or weights to give priority to a given characteristic (e.g. biological property: degree of specialization, rarity, originality, ecosystem function, or trophic level; conservation priorities; phylogenetic non-independence across species). Finally, data from regions with contrasted trends need to be weighted appropriately so that the overall estimate is an unbiased combination of spatial variations in the trend. For species monitoring, if population sizes differ across monitored geographical regions that are combined, a suitable weight would be the proportion of the total population size held per region. Weighting rules for more complex, multispecies biodiversity indicators are less straightforward. See the pages Combining data or estimates for distribution and abundance: single and multiple species and Meta-analyses analytical for suitable methods for integration across monitoring schemes.
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EuMon core team; August 2014