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Figure S1 in Genetic analysis and ecological niche modeling delimit species boundary of the Przewalski's scorpion (Scorpiones: Buthidae) in arid Asian inland

Figure S1. Bayesian consensus tree of the Mesobuthus caucasicus complex reconstructed from mitochondrial DNA sequences.

Figure 9 in Genetic analysis and ecological niche modeling delimit species boundary of the Przewalski's scorpion (Scorpiones: Buthidae) in arid Asian inland

Figure 9. Phylogeny the Mesobuthus caucasicus complex reconstructed using mitochondrial DNA sequences. The Przewalski's scorpion (M. przewalskii) is deeply diverged from other species and the Chinese scorpion (M. martensii) belongs to the species complex. Node supports are shown by bootstrapping probabilities from 1000 replicates and Bayesian posterior probabilities.

Figures 1–8 in Genetic analysis and ecological niche modeling delimit species boundary of the Przewalski's scorpion (Scorpiones: Buthidae) in arid Asian inland

Figures 1–8. Mesobuthus przewalskii stat. nov., from Qiemo, Xinjiang. 1. Male, dorsal view. 2. Male, ventral view. 3. Female, dorsal view. 4. Female, ventral view. 5. Male, dentition of pedipalp chela movable finger. 6. Male, dentition of pedipalp chela fixed finger. 7. Male, ventral aspect of genital operculum and pectines. 8. Female, ventral aspect of genital operculum and pectines. Scale bars: 1–4 = 5.0 mm; 5–8 = 2.0 mm.

Figure 11 in Genetic analysis and ecological niche modeling delimit species boundary of the Przewalski's scorpion (Scorpiones: Buthidae) in arid Asian inland

Figure 11. Ecological niche models of Mesobuthus scorpions. Potential distribution areas for the Przewalski's scorpion M. przewalsii (purple) is shown together with the Chinese scorpion M. martensii (green) and other species of the M. caucasicus complex (yellow). The entire Tarim Basin and adjacent Gobi region are suitable for survival of M. przewalskii. No area to the west of the Tianshan Mountains and the Pamir Plateau is suitable for M. przewalskii, and similarly no area to the east of the Tianshan Mountains and the Pamir Plateau is suitable for other species of the M. caucasicus complex. There are overlaps in predicted suitable distribution areas between M. przewalskii and M. martensii along the northeast edge of the Qinghai-Tibet Plateau. The suitable areas in the Junggar Basin and to the north of the Tianshan Mountains are likely due to over prediction of the model, because M. przewalskii does not occur in these regions. Ecological niche model for M. martensii was adopted from Shi et al. 2007.

Figure 10 in Genetic analysis and ecological niche modeling delimit species boundary of the Przewalski's scorpion (Scorpiones: Buthidae) in arid Asian inland

Figure 10. Phylogenetic network for the Mesobuthus caucasicus species complex. Although the interrelationships between species is poorly resolved, no reticulations have occurred in the most recent common ancestors for each species. The Przewalski's scorpion M. przewalskii is clearly diverged from other member of the species complex and warrants a species rank. The divergence of the Chinese scorpion M. martensii is comparable to the divergences among the members of the species complex.

opencc-zeroDec 2022View details →

Ecological signal in the size and shape of marine amniote teeth – 3D models and landmarks

Amniotes have been a major component of marine trophic chains from the beginning of the Triassic to present day, with hundreds of species. However, inferences of their (palaeo)ecology have mostly been qualitative, making it difficult to track how dietary niches have changed through time and across clades. Here, we tackle this issue by applying a novel geometric morphometric protocol to 3D models of tooth crowns across a wide range of raptorial marine amniotes. Our results highlight the phenomenon of dental simplification and widespread convergence in marine amniotes, implying strong functional constraints which limit the range of tooth crown morphologies. Importantly, we quantitatively demonstrate that tooth crown form (shape plus size) is strongly associated with diet, whereas crown surface complexity is not. The maximal range of tooth shapes in both mammals and reptiles is seen in medium-sized taxa; large crowns are simple and restricted to a fraction of the morphospace. We recognise four principle raptorial guilds within toothed marine amniotes (durophages, generalists, meat cutters, and flesh piercers). Moreover, even though all these feeding guilds have been convergently colonised over the last 200 million years, a series of dental morphologies are unique to the Mesozoic period, probably reflecting a distinct ecosystem structure.

opencc-zeroFeb 2023View details →

Predicting daily activity time through ecological niche modeling and microclimatic data

1. Climate temporality is a phenomenon that affects species' activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g., < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. 2. The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate if ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster (Heloderma suspectum) as a study system. Further, we identified if the activity patterns are related to physiological constraints. 3. We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum-volume ellipsoids. We generated hourly microclimatic data for each occurrence site for ten years using the NicheMapR package. For ecological niche modeling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested if unimodal and bimodal activity patterns for each season could be recreated through ecological niche modeling and if these patterns followed known physiological constraints. 4. The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered by using niche modeling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. 5. We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at fine temporal scales, particularly on ectotherm species of arid zones coping with rapid climate modifications. Further, the use of fine temporal scale variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.

opencc-by-4.0Feb 2023View details →

Codes in R for spatial statistics analysis, ecological response models and spatial distribution models

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022). It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running: In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.).  Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017). Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009). After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable. Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021). Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included. On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017). Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission). <table align="center"> <tbody> <tr> <td>                                      </td> <td> Validation set </td> </tr> <tr> <td> Model </td> <td> True </td> <td> False </td> </tr> <tr> <td> Presence </td> <td> A </td> <td> B </td> </tr> <tr> <td> Background </td> <td> C </td> <td> D </td> </tr> </tbody> </table> We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006). The last code (i.e., <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model. Regarding the model evaluation and estimation, we selected the following estimators: 1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020). 2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

opencc-zeroFeb 2023View details →

Incorporating ecology into gene drive modeling

Gene drive technology, in which fast-spreading engineered drive alleles are introduced into wild populations, represents a promising new tool in the fight against vector-borne diseases, agricultural pests, and invasive species. Due to the risks involved, gene drives have so far only been tested in laboratory settings while their population-level behavior is mainly studied using mathematical and computational models. The spread of a gene drive is a rapid evolutionary process that occurs over timescales similar to many ecological processes. This can potentially generate strong eco-evolutionary feedback that could profoundly affect the dynamics and outcome of a gene drive release. We therefore argue for the importance of incorporating ecological features into gene drive models. We describe the key ecological features that could affect gene drive behavior, such as population structure, life-history, environmental variation, and mode of selection. We review previous gene drive modeling efforts and identify areas where further research is needed. As gene drive technology approaches the level of field experimentation, it is crucial to evaluate gene drive dynamics, potential outcomes, and risks realistically by including ecological processes.

opencc-by-4.0May 2023View details →

The Prairie State: Using Ecological Niche Modeling to Predict Distributions of Early Land Plants

This data includes raw data of over 12,000 occurrences were downloaded from the Consortium of Bryophyte Herbaria (<a href="http://www.bryophyteportal.org/portal">www.bryophyteportal.org/portal</a>), that were listed to be in Illinois and included longitude and latitude data. This data set was screened and cleaned to investigate species distribution models as well as generate models of selected bryophytes investigating future changes in distribution across climate change scenarios.

opencc-zeroJul 2023View details →

Data for: A model of ecological abundance: Terrestrial species inventories

Counts of species in ecological samples are important for two reasons: they tell us about community assembly processes and they form the basis of species diversity estimates. Previous models of count distributions are either complex, widely rejected, not grounded in population dynamics, or not able to predict high unevenness. I present a new one-parameter model assuming that individual counts track the geometric series. The series' governing parameter p is set to vary randomly among species. Communities differ only in the centering of the distribution of p. To find the probability distribution, a vector of evenly-spaced initial values called q is drawn from the range 0 to 1. Values are then scaled by (1) transforming each q into the odds o = q/(1 – q), (2) multiplying each o by a fitted parameter m, and (3) back-computing each p as m o/(m o + 1). This skews the values to match the centering of the actual counts. The distribution is consistent with a population dynamics model in which the number of offspring produced in each interval by each species is distributed geometrically, rising with the number of adults. Large-scale surveys of corals, fishes, butterflies, and trees are consistent with the distribution, as are local-scale inventories of trees and assorted vertebrate and insect groups. Each local survey is used to predict counts within biogeographically and taxonomically matched surveys. When only decisive differences are considered, the model's predictions outperform those of each rival in at least 86% of all pairwise comparisons. The new distribution's estimates haves no substantial sample size bias. Thus, it is preferable to other species diversity estimation methods in the frequent cases where it is a good fit to count data.

opencc-by-4.0Aug 2023View details →

Output raster datasets from Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)

Output raster datasets from the 2023 Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative (ARRI) area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities. Output raster datasets include ecological condition for canopy, midstory and groundcover/shrub layers as well as overall ecological condition. Each raster contains ranked scores of estimated ecological condition: 1- Excellent, 2- Good, 3-Fair, and 4-Poor.  NOTE- These outputs were created using tools stored in this repository: <a href="https://doi.org/10.5281/zenodo.8236853">https://doi.org/10.5281/zenodo.8236853</a> as well as several raster input layers stored in this repository: https://doi.org/10.5281/zenodo.8234220.

opencc-by-4.0Aug 2023View details →

Input raster datasets for Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)

Input raster datasets used to create an Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.