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Oversampled areas, as a form of sampling bias, can generate model overfit [ 27 ].

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To prevent this, we calibrated present-day models using occurrences filtered to one-per-cell according to the spatial resolution of cells in our environmental layers [ 28 ]. All the remaining occurrences were used for modeling. From the initial pool of 2, occurrences, 84 single occurrences i. Models were calibrated in three regions red lines in A, B, and C based on the distribution of starry stonewort populations green points. Model calibration region based on an invasive population approach focused on starry stonewort populations in the invaded area of the United States and a high dispersal potential i.

The selection of M , the model calibration region, has a strong influence on ecological niche model predictions [ 29 ]. For instance, considering only invasive populations can result in incomplete information about the environmental preferences of the species [ 13 ], or be insufficient to characterize environmental tolerances [ 30 ]. Explicitly testing different extents of the calibration region facilitates comparison of models and informs interpretation of results [ 31 ].

Recent new records for starry stonewort in North America suggest that it may be expanding in North America from east to west and from south to north [ 8 ]. As a proxy of the dispersal potential of the species we used two distances for three M scenarios. Considering that the species has been dispersing between distant lakes, we assumed that spatial barriers could be overcome in the model calibration regions. We used this distance as a buffer around starry stonewort occurrences to generate a model calibration region for the invaded range in the US M i.

This area corresponds to a model based on the invasive populations. Furthermore, to account for starry stonewort environmental preferences across its entire range, we focused on two additional model calibration areas, including both native Europe and Japan and invasive populations US. The M i scenario encompasses inland and coastal regions of central and eastern Canada and all states in the continental US except those in the far west: California, Nevada, Oregon, Washington, and western portions of Arizona and Idaho.

All M scenarios included the area of interest for this study Minnesota. As a proxy of A , we used the present-day Ecoclimate dataset — at km spatial resolution [ 32 ]. Since starry stonewort occurs in both coastal and inland areas, we used climate variables covering both regions. This climate dataset is derived from the Coupled Model Inter-comparison Project CMIP5 and combines climatic patterns from multiple general circulation models from inland and marine ecosystems; thus, final climatic layers have global coverage.

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The role of oceanic dispersal in the invasion process of this species remains uncertain, however, we assumed that marine dispersal could play a role and include climate conditions in terrestrial and marine ecosytems in our model calibration regions. Climate models are considerably variable, thus, adding more scenarios of future climate would provide more information regarding the plausible variability in forecasts. Future climatic conditions for the end of the 21 st century — were obtained from Ecoclimate, including four representative concentration pathways RCPs; i.

Each RCP scenario represents potential trajectories of greenhouse gas emissions projected to the future, ranging from the most optimistic i. We explored areas with non-analogous novel climatic conditions between present-day climate in the calibration regions vs. This resulted in a present vs. This analysis was done using the extrapolation detection Exdet tool developed by Mesgaran et al. Exdet identifies non-analogous environments between calibration and projection regions denoted as type I novelty [ sensu 34]. Accounting for these non-analogous or novel environments enables a more confident interpretation of models [ 18 , 35 , 36 ].


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Qiao et al. We used Maxent to perform niche modeling because it enables the use of different variable transformations features , i. In addition, Maxent allows automatic truncation in novel climates to avoid predictions in non-analogous environments. Maxent is an occurrences-background algorithm, which estimates the most uniform probable distribution of the occurrences across a selected calibration region [ 13 , 38 ]. The background represents the summary of environmental conditions across the model calibration region. We developed models based on 5, and also 10, background samples.

Here, we tested 20 different regularization coefficient values ranging from 0. The regularization coefficients regulate the complexity of the model, higher values penalize for complexity and thus, produce simpler models avoiding complex relationships between the data and the variables that, in general, tend to have larger predictions [ 39 ]. In addition, because low AICc does not represent the ability of the model to predict independent data, we also assessed predictive performance based on the full AUC total and mean AUC mean of the area under the curve of the receiver-operating characteristic AUC and the difference between training and testing AUC and its variability.

These metrics enable the proportion of independent occurrences predicted incorrectly to be quantified [ 40 ]. Evaluation of model predictions was performed using independent data obtained via dividing the occurrences in two sets, one for model calibration and one for evaluation. Calibration and evaluation data sets were developed based on four different data splitting configurations: i using one point at a time for model evaluation i. Model evaluations were conducted using the R package ENMeval [ 40 ].


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Once the best regularization coefficient, feature configuration, and number of background points were determined for the calibration regions Fig 2 , the three selected models were projected to environmental conditions in Minnesota. This practice prevents unrealistic extrapolations of models into non-analogous novel environments that could be present in the projection region but absent from the calibration region [ 46 ]. In all, to identify the best model by calibration region M i vs. To inform interpretation of forecasts, we also estimated uncertainty of all final models.

For final models, we selected the logistic output format in Maxent with clamping and extrapolation deactivated. Finally, we created an ensemble of models for different future climate scenarios in Minnesota.

We averaged the final logistic models and calculated the standard deviations to identify areas where models were consistent low SD or diverged high SD. There is debate about use of model ensembles, due to issues regarding interpretation of continuous units from different algorithms e. Maxent see [ 13 ]. Here, we overcame such discrepancies by using the same suitability value i. Selected regularization coefficients differed by model calibration region: a regularization coefficient of 1.

Our evaluations revealed that 10, background points provided good model fit and performance for the three model calibration regions explored.

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Model uncertainty was higher in the model calibrated in M i M i vs. In present-day models, we found potential areas for starry stonewort distribution in southeast and central Minnesota and also in the Minneapolis-St. Paul metro region.

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The portion of Minnesota where starry stonewort has been confirmed to date was predicted to have high suitability for the model calibrated based on M g and M d Fig 3. Models from the global range M g containing all the occurrences produced predictions with lower uncertainty. The M d model calibrated based on the entire species range but with reduced dispersal potential predicted suitability resembling something between M i and M g Fig 3. Prediction of starry stonewort suitability from M d showed the highest uncertainty in western Minnesota.

All MRI emission scenarios showed Minnesota having analogous climates. Other climate models and emission scenarios showed different non-analogous climate configuration according to the M scenarios employed Figs 4 — 6. Exdet tool identified analogous climates between present-day climate in the calibration region from the invaded range and future climate scenarios in the projection region of Minnesota. Areas with analogous green and non-analogous environments in Minnesota grey were identified for five future climate models i.

Legend as in Fig 3. Additionally, based on M i and M d , models did not predict suitability under the IPSL climate model or predicted moderate suitability in small areas under the MIROC climate model Figs 7 and 8 , due to the absence of analogous environments Figs 4 and 6. Ecological niche model predictions based on model calibration region M i projected to Minnesota. Areas with high red or low blue environmental suitability Suitability, left and high pink or low light blue model uncertainty Standard deviation, right were identified for five future climate models i.

Ecological niche model predictions based on model calibration region M d projected to Minnesota. Legend as in Fig 7. High variability was found for CCSM 2.

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Some future climate scenarios indicated lack of suitability for starry stonewort throughout Minnesota Fig 9. In general, climatic suitability is predicted to decrease under future climate conditions relative to present-day conditions Fig 3 vs. Fig The model ensemble showed a lack of agreement in predicted suitability among M calibration areas and RCP scenarios, with suitability values ranging from 0.


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Areas with high values of suitability were also areas with high uncertainty in the model ensemble Fig In general, climatic suitability is predicted to decrease in the number of lakes of Minnesota under future climate conditions relative to present-day conditions except for the scenario RCP 2. Ecological niche model predictions based on model calibration region M g projected to Minnesota. Top : Models calibrated in M i and projected to future climate scenarios in Minnesota. Mid : Models calibrated in M g and projected to future climate scenarios in Minnesota.

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Bottom : Models calibrated in M d and projected to future climate scenarios in Minnesota.