Effect of characteristics of butterfly species on the accuracy of distribution models in an arid environment

Newbold, Tim; Reader, Tom; Zalat, Samy; El‐Gabbas, Ahmed; Gilbert, Francis

doi:10.1007/s10531-009-9668-5

Cited by 28 publications

(25 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our investigation supports the suggestion that the completeness of sampling with respect to the environmental gradients is more important for performance than sample size [56]. Models trained on the small herbarium dataset outperformed those trained on the large-but-biased NVS dataset, even after correcting for geographical bias: AUC and COR values were higher for the herbarium models, POC curves departed little from the expected line, and errors rates in the North Island were lower (Figure 5).…”

Section: Discussionsupporting

confidence: 84%

The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

2013

View full text Add to dashboard Cite

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as “feature types” in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

show abstract

Section: Discussionsupporting

confidence: 84%

The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

2013

View full text Add to dashboard Cite

show abstract

“…There have been attempts to assess differences among species in the accuracy of their distribution models (Kadmon et al 2003, Berg et al 2004, Seoane et al 2005, Hernandez et al 2006, Newbold et al 2009b). These studies have often found that species that are more narrowly distributed produce more accurate distribution models, possibly because small‐ranged species have better‐defined habitat requirements and tend to inhabit a greater proportion of the suitable environment, or because in species with larger ranges, populations show local adaptation to the environment in different areas (Stockwell and Peterson 2002, Brotons et al 2004, Segurado and Araújo 2004, Hernandez et al 2006, Newbold et al 2009b). On the other hand, effects of range size could be a statistical artefact associated with the use of pseudo‐absence data (Lobo et al 2008).…”

mentioning

confidence: 99%

Testing the accuracy of species distribution models using species records from a new field survey

Newbold

Reader

El‐Gabbas

et al. 2010

Oikos

Self Cite

View full text Add to dashboard Cite

Species distribution models are a very popular tool in ecology and biogeography and have great potential to help direct conservation efforts. Models are traditionally tested by using half the original species records to build the model and half to evaluate it. However, this can lead to overly optimistic estimates of model accuracy, particularly when there are systematic biases in the data. It is better to evaluate models using independent data. This study used independent species records from a new to survey to provide a more rigorous evaluation of distribution‐model accuracy. Distribution models were built for reptile, amphibian, butterfly and mammal species. The accuracy of these models was evaluated using the traditional approach of partitioning the original species records into model‐building and model‐evaluating datasets, and using independent records collected during a new field survey of 21 previously unvisited sites in diverse habitat types. We tested whether variation in distribution‐model accuracy among species could be explained by species detectability, range size, number of records used to build the models, and body size. Estimates of accuracy derived using the new species records correlated positively with estimates generated using the traditional data‐partitioning approach, but were on average 22% lower. Model accuracy was negatively related to range size and number of records used to build the models, and positively related to the body size of butterflies. There was no clear relationship between species detectability and model accuracy. The field data generally validated the species distribution models. However, there was considerable variation in model accuracy among species, some of which could be explained by the characteristics of species.

show abstract

“…Host plant distributions strongly influence the occurrence and abundance of specialist insect herbivores (Newbold et al., ). However, the results of the present study showed that climatic factors are important in identifying the geographical limitations of the specialist species occurrences.…”

Section: Discussionmentioning

confidence: 99%

Inferring climatic controls of rice stem borers’ spatial distributions using maximum entropy modelling

Jalaeian

Golizadeh

Sarafrazi

et al. 2018

J Applied Entomology

View full text Add to dashboard Cite

Accurate assessment of pest potential distributions is needed to identify their establishment risks that play a key role in pest management in agricultural ecosystems. We used a correlative niche modelling method (Maxent) to predict and map the spatial distributions of two important rice stem borers, Chilo suppressalis and Sesamia cretica, in paddy fields of Iran. In total, 195 presence occurrence records (101 records for C. suppressalis and 94 records for S. cretica) were compiled. A set of environmental and topographic variables, with the highest effects on the species distributions and the lowest correlations among themselves, were used. The results showed that mainly the northern parts of Iran were the most suitable areas for C. suppressalis, and north, north‐east and south‐west of Iran as the most suitable areas for S. cretica. Both models performed well, with an area under the receiver operating characteristic curve (AUC) of 0.983 and 0.786 for C. suppressalis and S. cretica, respectively. The Maxent models showed higher accuracy for predicting the distribution of the specialist pest with the small range sizes compared to the generalist species. Assessing the importance of environmental variables, which were derived from the jackknife test, showed the precipitation as the variable with the highest contribution (66%) in explaining the spatial distribution of C. suppressalis compared to the other variables. The distribution of S. cretica was influenced by a set of variables derived from both the precipitation and temperature. The Maxent predictions were useful to map the geographical distributions of the risk for both rice stem borers that is needed to develop effective management strategies.

show abstract

Effect of characteristics of butterfly species on the accuracy of distribution models in an arid environment

Cited by 28 publications

References 54 publications

The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

Testing the accuracy of species distribution models using species records from a new field survey

Inferring climatic controls of rice stem borers’ spatial distributions using maximum entropy modelling

Contact Info

Product

Resources

About