Effects of positional error on modeling species distributions: a perspective using presettlement land survey records

Tulowiecki, Stephen J.; Larsen, C. T.; Wang, Yi-Chen

doi:10.1007/s11258-014-0417-9

Cited by 13 publications

(20 citation statements)

References 44 publications

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“…Using county mean climates in SDMs is akin to modelling species distributions with precise coordinates on lower resolution climate maps, where the small variations in specific habitat climates are lost, but the results can still be relevant for large‐scale predictions. All things being equal, the effects of locational uncertainty on SDM performance are likely to be more severe for specialist species, as approximate locations, such as county centroids, will frequently misrepresent their more specific environmental requirements compared with generalists with broader climatic niches (Tulowiecki et al ., ).…”

Section: Discussionmentioning

confidence: 97%

“…This practice commonly ignores the uncertainty inherent in georeferenced location data by treating these centroids as precise point occurrences, and by extension assumes accurate knowledge of the climate variables associated with the locality (Feeley & Silman, ). Such positional uncertainty can lead to spurious estimations of species–environment relationships (Dormann et al ., ; Beale & Lennon, ; Tulowiecki et al ., ), the magnitude of which is determined by the level of local spatial autocorrelation in the environmental variables (Naimi et al ., , ). For example, Montrose County in Colorado spans the depths of the Black Canyon to the peaks of the Rocky Mountains, and daily temperatures can vary over 20 ° C between these locations.…”

Section: Introductionmentioning

confidence: 99%

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Implications and alternatives of assigning climate data to geographical centroids

Park

Davis

2017

Journal of Biogeography

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Aim When precise coordinate data for training species distribution models (SDMs) are lacking, climatic variables are often assigned to centroids of geopolitically defined regions, frequently counties. This is problematic because approximations using centroids may not be representative of the regional climate or the locality from where species actually occur, thus leading to spurious conclusions. We evaluated county centroid climate versus simple alternatives for assigning climate to species observations in the absence of precise occurrence data.Location United States of America.Methods We assessed the disparity between the actual climate of all points within a county and metrics estimating county climate using the climate of geographical centroid, mean county climate and median county climate. To further evaluate the performance of these metrics, we generated SDMs of four common species using these estimates and compared the results with observed species distributions (red trillium, Pacific trillium, tall thistle and annual fleabane). Finally, we projected future ranges for annual fleabane to examine the difference in predicted range change between models.Results Mean and median climate metrics were significantly better fits for approximating the climate of specimen records than climate of the geographical centroid. Moreover, county mean climate SDMs were the most similar to SDMs using actual coordinate data. In contrast, models applying climate to county centroid significantly overpredicted species range. This had implications for future projections of annual fleabane SDMs: the county centroid model predicted a decrease in suitable habitats for this species while other models predicted an increase.Main conclusions County centroid climate, although commonly applied, is not suitable for SDMs as a means to approximate species climate when locality data are less precise. When only county level data are available, and more computationally intensive methods of accounting for spatial uncertainty cannot be readily implemented, we suggest considering mean county climate as an alternative.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Implications and alternatives of assigning climate data to geographical centroids

Park

Davis

2017

Journal of Biogeography

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“…The effect of imprecise records on niche breadth depends on how well precise records sample geographic and niche space (Moudrý & Šímová 2012). Generally, species that have few records and/or narrower niche breadth will be affected more by the inclusion of imprecise records (Tulowiecki et al 2015;Collins et al 2017;Velásquez-Tibatá et al 2016; but see Gábor et al 2020). This was indeed true in our case.…”

Section: Methodological Considerationsmentioning

confidence: 55%

“…To date, the general consensus has been to discard imprecise records because their use can inflate estimates of niche breadth and reduce the apparent accuracy of ecological niche models. This widespread aversion to using spatially imprecise records may be due in part to the fact that nearly all studies evaluating the effects of coordinate imprecision do so by adding spatial error to erstwhile precise records (e.g., Graham et al 2008;Fernandez et al 2009;Osborne & Leitão 2009;Gueta & Carmel 2016;Mitchell et al 2016;Hefley et al 2017;Soultan & Safi 2017;Tulowiecki et al 2015;Gábor et al 2020). Although this approach keeps sample sizes constant between treatments with or without spatially imprecise records, it is not reflective of real-world situations where assessors usually start with a mix of relatively precisely-and impreciselygeolocated records but must decide how to delineate the two groups and whether or not to discard the imprecise ones.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, decisions regarding the spatial precision of occurrence points create a trade-off between utilizing a sufficient number of records to represent the full geographic and environmental distribution of a species, and ensuring a high-fidelity match between occurrences and environmental data. On the one hand, the use of imprecise records can inflate niche breadth (Feeley & Silman 2010;Collins et al 2017) and reduce the predictive accuracy and inferential power of ecological niche models (Graham et al 2008;Fernandez et al 2009;Tulowiecki et al 2015;Osborne & Leitão 2009;Gábor et al 2020;Mitchell et al 2016;Cheng et al 2021). These issues arise because environmental values to which imprecise records should be matched remains uncertain.…”

Section: Introductionmentioning

confidence: 99%

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Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

Smith

Henderson

et al. 2021

Preprint

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Conservation assessments frequently use occurrence records to estimate species' geographic distributions and environmental tolerances. Typically, records with imprecise geolocality information are discarded before analysis because they cannot be matched confidently to environmental conditions. However, removing records can artificially truncate species' environmental and geographic distributions. Here we evaluate the trade-offs between using versus discarding imprecise records when estimating species' ranges and climatic tolerances. We collated records from 44 species in the genus Asclepias (milkweeds). Records were designated "precise" if they could be matched confidently to environmental data, and "imprecise" if not. We compared estimates of extent of occurrence (EOO), climatic niche breadth, and exposure to climate change using precise records only, as well as precise plus imprecise records together. To estimate EOO, we conservatively assigned imprecise records to points within their area of likely collection that were closest to the centroid of precise records. Similarly, to estimate climatic tolerances and exposure to climate change, we matched imprecise records to climate values that were most similar to the mean across precise records. Across all species, including imprecise records increased EOO by 85% (median value; range across species: 0-2011%). Univariate niche breadth in mean annual temperature and precipitation increased by 25% (0-353%) and 28% (0-292%), respectively, while multivariate niche volume increased by 175% (8-13909%). Adding imprecise records increased suitable area in the present and area that remained suitable in the future. Imprecise records provide novel information about species' distributions and climatic niche tolerances. While the default practice of discarding imprecise records ensures that only accurate data are used, it dramatically reduces estimates of range size and overestimates exposure to climate change. The benefits of discarding imprecisely geolocated records must be balanced against the loss of information incurred by their elimination.

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Surveyor and analyst biases in forest density estimation from United States Public Land Surveys

Cogbill

2023

Ecosphere

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Accurate forest density estimates based on United States Public Land Surveys have long been questioned because of doubts about randomness of both the surveyors' selection of witness trees and the underlying tree dispersion. This study analyzes the surveyor sampling of witness trees in six Midwestern states in the mid‐1800s. It develops universal methods for identification, quantification, and correction of bias, and then calculation of unbiased density. Applying these techniques produces unbiased site‐specific densities before Euro‐American settlement, which are the essential baseline for determining historic changes in forest structure. Previous analysts used untested assumptions, inaccurate estimators, unknown or unrealistic sampling designs, and omitted or poorly corrected surveyor bias, resulting in hundreds of unreliable density estimates. The surveyors' recording of the empirical distance, bearing, and diameter of witness trees documented the exact sampling design. The intended design and deviation from it are investigated with a combination of descriptive statistics, probability theory, computer simulations, analogue geometric models, and modern stand conditions. Herein, analyst bias is eliminated using the robust Morisita II density estimator matching the predominant sampling design of two trees in opposing semicircles. Six widespread surveyor biases deviating from the nearest tree to the corner are evaluated. Quadrant bias and diameter bias for medium‐sized trees are subsumed under newly framed design and small tree biases. Two novel surveyor positional biases (pair angle and near‐post) are introduced here. Previously recognized azimuthal and species biases are analyzed with new techniques. Widely postulated surveyor bias for certain species was found to be minimal. Bias correction and density estimation are applied in detail over 68 townships in northern Wisconsin. The estimated historical forest density in northern Wisconsin, corrected for bias and small tree truncation, averaged 323 trees/ha ≥20 cm. Over 80 Midwestern subregions, surveyors bypassed an estimated 17% of the nearest trees due to their position, resulting in an average bias correction of +24% over the base density. If censored trees below a 20‐cm “veil‐line” are considered, the surveyors bypassed 48% of the nearest trees >12.7 cm in diameter. This study resolves a 70‐year‐old conundrum of surveyor and analyst biases in historical density estimation.

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Effects of positional error on modeling species distributions: a perspective using presettlement land survey records

Cited by 13 publications

References 44 publications

Implications and alternatives of assigning climate data to geographical centroids

Implications and alternatives of assigning climate data to geographical centroids

Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

Surveyor and analyst biases in forest density estimation from United States Public Land Surveys

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