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

Smith, Adam B.; Murphy, Stephen J.; Henderson, David W.; Erickson, Kelley D.

doi:10.1111/geb.13628

Cited by 28 publications

(22 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, for some purposes, positionally inaccurate records need not be discarded (as is common practice; Watcharamongkol et al 2018, Gueta and Carmel 2016). This finding is particularly fortuitous because discarding positionally uncertain occurrence data can limit our ability to estimate range sizes and overestimates exposure to climate change (Smith et al 2023).…”

Section: Discussionmentioning

confidence: 99%

“…For example, Gábor et al (2020) demonstrated that increased sample sizes do not reduce the negative effects of positional uncertainty. Similarly, Smith et al (2023) showed that discarding data with high positional uncertainty limits our ability to determine species' distribution and climatic niche tolerances properly. In particular, they demonstrated that using only accurate data dramatically reduces range size estimates and overestimates exposure to climate change.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Species distribution models affected by positional uncertainty in species occurrences can still be ecologically interpretable

et al. 2023

View full text Add to dashboard Cite

Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of positional uncertainty on model predictive performance, but its consequences for inference about species–environment relationships remain largely unknown. Here we use over 12 000 combinations of virtual and real environmental variables and virtual species, as well as a real case study, to investigate how accurately SDMs can recover species–environment relationships after applying known positional errors to species occurrence data. We explored a range of environmental predictors with various spatial heterogeneity, species' niche widths, sample sizes and magnitudes of positional error. Positional uncertainty decreased predictive model performance for all modeled scenarios. The absolute and relative importance of environmental predictors and the shape of species–environmental relationships co‐varied with a level of positional uncertainty. These differences were much weaker than those observed for overall model performance, especially for homogenous predictor variables. This suggests that, at least for the example species and conditions analyzed, the negative consequences of positional uncertainty on model performance did not extend as strongly to the ecological interpretability of the models. Although the findings are encouraging for practitioners using SDMs to reveal generative mechanisms based on spatially uncertain data, they suggest greater consequences for applications utilizing distributions predicted from SDMs using positionally uncertain data, such as conservation prioritization and biodiversity monitoring.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Species distribution models affected by positional uncertainty in species occurrences can still be ecologically interpretable

et al. 2023

View full text Add to dashboard Cite

show abstract

“…To this aim, each model was first implemented using the specific set of best environmental variables from the R enmsdm package (Smith, 2021). The best models were identified using as a tuning tool the ‘enmsdm’ R package and its evolution ‘enmsdmX’ (Smith et al, 2023), a complement to the dismo package for R (Hijmans et al, 2017) that provides methods for implementing SDMs and ecological niche models through a set of ‘training’ functions that automatically tune and parameterize models based on several popular algorithms (e.g., Maxent, GLMs, GAMs, etc.). The selection among models with different specifications of predictors can be done using some criteria for instance the Akaike Information Criterion (AICc) (Akaike, 1973).…”

Section: Methodsmentioning

confidence: 99%

Section: Model Settingsmentioning

confidence: 99%

Looking for reliable species distribution models for low‐density cetacean species: Compared effectiveness of SDMs for G. griseus, G. melas, Z. cavirostris in the Mediterranean Sea based on long‐term fixed‐transect data

Arcangeli,

Azzolin,

Babey

et al. 2024

Aquatic Conservation

View full text Add to dashboard Cite

The EU regulative framework for the protection of marine biodiversity and habitats requires the assessment of species' conservation status and the identification of core habitats to design adequate conservation and management plans. However, the identification of distribution range and habitat‐use of pelagic large‐range, migratory species, such as cetaceans, is challenging. Species distribution models (SDMs) are increasingly used in conservation planning to identify species priority areas. However, the quality of SDMs varies widely depending on the representativeness of data and the appropriateness of the modelling techniques. Since 2007, the Fixed Line Transect Mediterranean Monitoring Network (FLT Med Net) has been continuously monitoring cetaceans throughout the year in the Mediterranean basin using passenger ferries as observation platforms that perform repetitive surveys along fixed trans‐border transects. With the aim of defining a standard analytical approach, the data collected by the FLT Med Net on rarer Mediterranean cetacean species (i.e., Grampus griseus, Globicephala melas, Ziphius cavirostris) are used here to assess the performance of commonly used SDMs, including GLM, GAM, GAM‐Negative Binomial, GAM‐tweedy, MaxEnt and Random Forest. Models were built and evaluated using a total of 296 FLT Med Net sighting data and cross‐validated using 145 independent data points. Under testing conditions, almost all methods exhibited good performance, with Random Forest being the best model in several cases. However, when evaluated with the independent dataset, many models yielded inconsistent results or notably low performance. Only MaxEnt demonstrated consistent efficiency and reliability in both cases, showing results less affected by unequal sampling or small sample size. Results confirm the importance of robust standard SDM approaches based on representative data such as the FLT Med Net data to identify reliable priority areas for the species and to assess the long‐term coherence and effectiveness of spatial protection measures.

show abstract

“…was developed using the selected hyper‐parameters, thinned presence data and 10,000 random pseudo‐absence data points described above. The model was specified using the trainMaxnet function, enmSdmX package (Smith et al, 2023). Thresholds for the final model output were determined using the evalThreshold function, enmSdmX package.…”

Section: Methodsmentioning

confidence: 99%

Estimating the potential distribution range of the invasive South American suckermouth armoured catfishes Pterygoplichthys spp. in the Indo‐Burma biodiversity hotspot using MaxEnt

Marr,

Patoka,

Zworykin

2024

Aquatic Conservation

View full text Add to dashboard Cite

The Indo‐Burma biodiversity hotspot has one of the world's most species‐rich inland waters. However, its freshwater biodiversity faces habitat loss and species extinction due to large‐scale development of water resources, pollution, exploitation of native biota, and introduced non‐native species. These threats include the South American suckermouth armoured catfishes, Pterygoplichthys spp., that have invaded parts of this biodiversity hotspot. Pterygoplichthys spp. are among the most impactful introduced fishes globally and pose a ‘very high risk’ of becoming invasive once introduced. This paper aims to compile a distribution database for Pterygoplichthys spp., map their current global distribution, and estimate their potential distribution extent in the Indo‐Burma biodiversity hotspot using MaxEnt. In the Indo‐Burma region, Pterygoplichthys spp. have already been reported from China, Myanmar, Thailand and Vietnam, with populations also present in neighbouring India, Bangladesh, Malaysia, Singapore and Indonesia. No populations were verified for Cambodia and Laos. A maximum entropy model was developed based on the current global distribution of Pterygoplichthys spp. The species distribution model showed that the entire biodiversity hotspot, except the spine of the Arkan Mountains and the Hengduan Mountains from Three Gorges to the Hong River Valley, is suitable for Pterygoplichthys spp. An effective temperature of 15°C was identified as the lower limit of Pterygoplichthys spp. habitat suitability. Pterygoplichthys spp. were identified as a high‐risk taxon for freshwaters of almost the entire Indo‐Burma biodiversity hotspot. A coordinated management plan for Pterygoplichthys spp. is required to safeguard the freshwater biota of the Indo‐Burma freshwater ecosystems.

show abstract

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

Cited by 28 publications

References 64 publications

Species distribution models affected by positional uncertainty in species occurrences can still be ecologically interpretable

Species distribution models affected by positional uncertainty in species occurrences can still be ecologically interpretable

Looking for reliable species distribution models for low‐density cetacean species: Compared effectiveness of SDMs for G. griseus, G. melas, Z. cavirostris in the Mediterranean Sea based on long‐term fixed‐transect data

Estimating the potential distribution range of the invasive South American suckermouth armoured catfishes Pterygoplichthys spp. in the Indo‐Burma biodiversity hotspot using MaxEnt

Contact Info

Product

Resources

About