Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models

By Tim Newbold. 2010

381 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Social Psychology

View Paper: https://www.semanticscholar.org/paper/25f2d5013845074d726a79a745f44dfc5eb77302

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Citations in the corpus

The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models (2013, background): “Species location data are increasingly made available by museums, herbaria, and other scientific institutions [6,7,8] via open-access data portals such as the Global Biodiversity Information Facility (GBIF; www.”
The unrealized potential of herbaria for global change biology (2017, background): “More recently, ecologists routinely use herbarium specimens as occurrence records for determining species distributions (Graham et al., 2004; Newbold 2010) and as records of leafing, flowering, and fruiting phenology (Miller-Rushing et al., 2006; Primack et al., 2004; Bolmgren & Lonnberg 2005;…”
Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China (2017, background): “However, occurrence data tend to be very scarce for the vast majority of species, especially in the cases of rare species where such information is either non-existent or very poor (Newbold, 2010; Marcer et al., 2013).”
Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation (2013, background): “However, for the vast majority of species such information is either non-existent or very poor at any given scale (Newbold, 2010); this is known as the Wallacean shortfall (Lomolino et al.”
Climate change may drive cave spiders to extinction (2018, background): “Within each cell in the grid – which had a width equal to the grain of the bioclimatic rasters – all the points apart from one were randomly removed (Newbold 2010).”
Environmental filtering improves ecological niche models across multiple scales (2019, background): “While raw occurrence data include misidentifications, georeferencing errors and sampling bias (Newbold, 2010), these data anchor our current knowledge of biodiversity and species’ distributions.”
Selecting thresholds for the prediction of species occurrence with presence‐only data (2013, background): “Spatial bias in the records may not be a problem if the data are not environmentally biased (Newbold, 2010).”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “We therefore address these two sources of sampling bias because both can occur in museum records because these opportunistic datasets rarely stem from systematic sampling regimes (Newbold 2010).”
A statistical explanation of MaxEnt for ecologists (2011, methodology): “MaxEnt relies on an unbiased sample (as do all species modelling methods), so efforts in collecting a comprehensive set of presence records (cleaned for duplicates and errors) and dealing with biases are critical (Newbold, 2010).”
Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities (2011, background): “In terrestrial systems, global sampling has been limited in tropical and arid environments (Newbold, 2010).”
Comparing species distribution models constructed with different subsets of environmental predictors (2015, background): “A common suggestion is to select predictors with direct and proximal effect on species distribution and ecology (Austin, 2002, 2007), resulting in biologically informative and generalizable SDMs (Newbold, 2010).”
Online solutions and the ‘Wallacean shortfall’: what does GBIF contribute to our knowledge of species' ranges? (2013, background): “Global biodiversity information facility (GBIF) data for sphingids (as for many other invertebrate groups) make no substantial contribution outside the industrialized countries (Newbold, 2010), which are currently GBIF’s main data providers.”