| Literature DB >> 29955095 |
Elena Castellanos-Frías1,2, Nuria García3, Emilio Virgós4.
Abstract
Climate change is not only evident, but its implications on biodiversity are already patent. The scientific community has delved into the limitations and capabilities of species to face changes in climatic conditions through experimental studies and, primarily, Species Distribution Models (SDMs). Nevertheless, the widespread use of SDMs comes with some intrinsic assumptions, such as niche conservatism, which are not always true. Alternatively, the fossil record can provide additional data to solve the uncertainties of species' responses to climate change based on their history. Using a combined environmental (niche overlap indices) and geographical approach (temporal transferability of SDMs), we assessed the niche conservatism of Microtus cabrerae throughout its evolutionary history: the Late Pleistocene and the Holocene. The set of analyses performed within this timeframe provides a broad view pointing to a shift in the realized climatic niche of the species. Specifically, M. cabrerae exhibited a broader niche during glacial times than interglacial times, expanding towards novel conditions. Hence, the species might have developed an adaptive ability, as a consequence of mechanisms of local adaptation or natural pressures, or just be preadapted to cope with the novel environment, due to expansion into an unfilled portion of the niche. Nevertheless, the more restricted realized niche during last interglacial times reveals that the species could be close to its physiological limits.Entities:
Year: 2018 PMID: 29955095 PMCID: PMC6023864 DOI: 10.1038/s41598-018-28000-0
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Modelling algorithms used to identify the paleo-niches of M. cabrerae from the current distribution. Models are calibrated under current conditions and projected to current and paleoclimate scenarios. Model performance is evaluated through a double approach: the statistical assessment of the model with the variance explained and the predictive ability of each model in current (internal validation) and past scenarios (external validation) with AUC.
| Model | Dataset | Explained variance | AUC | ||||
|---|---|---|---|---|---|---|---|
| Variance | Gain | Current | MIS1 | MIS2 | MIS5e | ||
| GLM | pseudo | 0.373 | 0.878 | 0.662 | 0.613 | 0.402 | |
| RF | 0.530 | 0.967 | 0.670 | 0.414 | 0.302 | ||
| Maxent | bg | 0.594 | 0.836 | 0.634 | 0.598 | 0.462 | |
| GLM | 0.325 | 0.849 | 0.653 | 0.625 | 0.408 | ||
pseudo: pseudoabsences; bg: background.
Both GLM deviance (R function D-squared, library modEva[83]) and RF variance (R library randomForest[73]) are expressed as a decimal. Whereas, gain value from Maxent defines the exponent to generate the average likelihood of presences, therefore the maximization of gain results in a model that best discriminates presence and background data[77,78]. The AUC of the current scenario was obtained from a bootstrapping evaluation; on the other hand, the AUCs from past transferabilities were calculated using fossil records as independent occurrence points.
Figure 1Random forest model projection to (a) current, (b) Mid-Holocene, (c) Last Glacial Maximum and (d) Last Interglacial Period climatic scenario. The shaded areas represent current occurrences (a) or fossil record of M. cabrerae for each epoch (b–d). Model is calibrated with the current distribution, thus it is restricted to the Iberian Peninsula, however the paleo-distribution included also France territories. Figure was created in R v3.3.2[72] (https://www.R-project.org/).
Figure 2Importance of bioclimatic variables used in the random forest SDM from the geographical approach (R function varImpPlot, library randomForest[73]). The percentage of increase in mean square errors (MSE) of model predictions is collected for each variable when that variable is randomly permuted, hence its importance in the accuracy of the model. Bioclimatic variables: temperature seasonality (bio 4), mean temperature of the wettest quarter (bio 8), mean temperature of the driest quarter (bio 9), precipitation in the warmest quarter (bio 18) and precipitation in the coldest quarter (bio 19).
Cross-projections. Modelling algorithms were calibrated in each paleoclimatic scenario and evaluated with AUC from the bootstrapping process (AUC of calibrated models). The evaluation of transferred models from paleo-scenarios to current climate was measured with AUC values using current occurrences as independent points (AUC of projection to current climate).
| Model | Dataset | AUC (Calibrated Models) | AUC (Projection to Current Climate) | ||||
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| GLM | pseudo | 0.925 | 0.813 | 0.816 | 0.599 | 0.553 | 0.589 |
| RF | 0.955 | 0.899 | 0.958 | 0.560 | 0.561 | 0.593 | |
| Maxent | bg | 0.801 | 0.799 | 0.749 | 0.491 | 0.601 | 0.597 |
| GLM | 0.860 | 0.799 | 0.766 | 0.578 | 0.566 | 0.587 | |
pseudo: pseudoabsences; bg: background.
Figure 3Climatic occupancy of M. cabrerae under Current and Paleo-climate: (a) MIS1; (b) MIS2 and (c) MIS5e periods. The arrows visualize the shift of the niche centroids between the current and paleo-scenario: continuous lines represent the shift in the centroid of current and paleo-distribution, while the dashed line is related to current and paleo extent. The correlation circle collects the contribution of bioclimatic variables to PCA axes.
Niche overlap comparisons between current and paleo- scenarios of M. cabrerae. The statistical significance of niche overlap to evaluate the hypothesis of niche equivalency and the hypothesis of niche similarity were conducted with randomization tests. For niche equivalency, a significant result implies that the overlap between the current and the paleo-scenario is less than would be expected if niches were identical. The significance for similarity tests means that current and paleo-niches are more similar than would be expected from a random distribution of the species created inside the paleoclimate-scenario (CC-Paleo) or created inside the current climate (Paleo-CC).
| Scenario | Niche Overlap | Overlap index | Equivalency test | Similarity test | |||
|---|---|---|---|---|---|---|---|
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| Expansion | Stability | Unfilling | CC to Paleo- | Paleo- to CC | ||
| CC - MIS1 | 0.166 | 0.257 | 0.743 | 0.316 | ** |
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| CC - MIS2 | 0.151 | 0.451 | 0.549 | 0.169 | ** |
| ** |
| CC - MIS5e | 0.480 | 0.019 | 0.981 | 0.312 | ** | ** | ** |
CC: Current Climate.
ns (non-significant): p > 0.05; *p ≤ 0.05; **p ≤ 0.01.