Predicting Suitable Environments and Potential Occurrences for Cinnamomum camphora (Linn.) Presl.
TL;DR: In this paper, the authors employed Maxent and a GARP to establish a potential distribution model for Cinnamomum camphora (Linn.) Presl., an evergreen tree growing in tropical and subtropical Asia, as well as factors influencing its distribution.
Abstract: Global climate change has created a major threat to biodiversity. However, little is known about the habitat and distribution characteristics of Cinnamomum camphora (Linn.) Presl., an evergreen tree growing in tropical and subtropical Asia, as well as the factors influencing its distribution. The present study employed Maxent and a GARP to establish a potential distribution model for the target species based on 182 known occurrence sites and 17 environmental variables. The results indicate that Maxent performed better than GARP. The mean diurnal temperature range, annual precipitation, mean air temperature of driest quarter and sunshine duration in growing season were important environmental factors influencing the distribution of C. camphora and contributed 40.9%, 23.0%, 10.5%, and 7.2% to the variation in the model contribution, respectively. Based on the models, the subtropical and temperate regions of Eastern China, where the species has been recorded, had a high suitability for this species. Under each climate change scenario, the potential geographical distribution shifted farther north and toward a higher elevation. The predicted spatial and temporal distribution patterns of this species can provide guidance for the development strategies for forest management and species protection.
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TL;DR: In this article , the authors used a maximum entropy model to simulate the changes in its distribution area from historical periods to future periods, showing that temperature is an indispensable factor affecting the presence and suitable habitats of A. chensiensis.
Abstract: Simple Summary The adaptation, migration, and extinction of species are closely associated with climate changes and shape the distribution of biodiversity. Plants in alpine ecosystems are particularly sensitive to climate change. In recent decades, the loss and fragmentation of suitable habitats for species due to climate change have caused alpine plants to become extinct or to be replaced by other species. Thus, to predict how climate change will influence the survival and suitable habitats of the rare and endangered tree species Abies chensiensis in East Asia, we used a maximum entropy model to simulate the changes in its distribution area from historical periods to future periods. Our results illustrate that temperature is an indispensable factor affecting the presence and suitable habitats of A. chensiensis. In the future (the 2050s and 2070s), the suitable distribution area will contract significantly, and the migration routes of the centroids will tend to migrate toward the southern high-altitude mountains. These results may contribute to a more comprehensive understanding of potential geographical distribution patterns and the distribution of suitable habitats for some rare and endangered plant species in East Asia and may help implement long-term conservation and the reintroduction of these species. Abstract Globally, increasing temperatures due to climate change have severely affected natural ecosystems in several regions of the world; however, the impact on the alpine plant may be particularly profound, further raising the risk of extinction for rare and endangered alpine plants. To identify how alpine species have responded to past climate change and to predict the potential geographic distribution of species under future climate change, we investigated the distribution records of A. chensiensis, an endangered alpine plant in the Qinling Mountains listed in the Red List. In this study, the optimized MaxEnt model was used to analyse the key environmental variables related to the distribution of A. chensiensis based on 93 wild distribution records and six environmental variables. The potential distribution areas of A. chensiensis in the last interglacial (LIG), the last glacial maximum (LGM), the current period, and the 2050s and 2070s were simulated. Our results showed that temperature is critical to the distribution of A. chensiensis, with the mean temperature of the coldest quarter being the most important climatic factor affecting the distribution of this species. In addition, ecological niche modeling analysis showed that the A. chensiensis distribution area in the last interglacial experiencing population expansion and, during the last glacial maximum occurring, a population contraction. Under the emission scenarios in the 2050s and 2070s, the suitable distribution area would contract significantly, and the migration routes of the centroids tended to migrate toward the southern high-altitude mountains, suggesting a strong response from the A. chensiensis distribution to climate change. Collectively, the results of this study provide a comprehensive and multidimensional perspective on the geographic distribution pattern and history of population dynamics for the endemic, rare, and endangered species, A. chensiensis, and it underscores the significant impact of geological and climatic changes on the geographic pattern of alpine species populations.
1 citations
08 Nov 2021
TL;DR: In this paper , the authors analyzed the composition, diversity, and functions of the fungal communities in the bulk soil, rhizosphere, and root endosphere of Cinnamomum camphora at different slope positions by high-throughput sequencing.
Abstract: Plant-associated microbial communities play essential roles in the vegetative cycle, growth, and development of plants. Cinnamomum camphora is an evergreen tree species of the Lauraceae family with high ornamental, medicinal, and economic values. The present study analyzed the composition, diversity, and functions of the fungal communities in the bulk soil, rhizosphere, and root endosphere of C. camphora at different slope positions by high-throughput sequencing. The results showed that the alpha diversity of the fungal communities in the bulk soil and rhizosphere of the downhill plots was relatively higher than those uphill. A further analysis revealed that Mucoromycota, the dominant fungus at the phylum level, was positively correlated with soil bulk density, total soil porosity, mass water content, alkaline-hydrolyzable nitrogen, maximum field capacity, and least field capacity. Meanwhile, the prevalent fungus at the class level, Mortierellomycetes, was positively correlated with total phosphorus and available and total potassium, but negatively with alkaline-hydrolyzable nitrogen. Finally, the assignment of the functional guilds to the fungal operational taxonomic units (OTUs) revealed that the OTUs highly enriched in the downhill samples compared with the uphill samples, which were saprotrophs. Thus, this study is the first to report differences in the fungal community among the different soil/root samples and between C. camphora forests grown at different slope positions. We also identified the factors favoring the root-associated beneficial fungi in these forests, providing theoretical guidance for managing C. camphora forests.
TL;DR: In this article , the authors assessed the present-day distribution and predicted the potential effect of climate change on the distribution of 15 Vigna crop wild relative taxa in Benin under two future climate change scenarios (RCP 4.5 and RCP 8.5).
Abstract: Sustainable conservation of crop wild relatives is one of the pathways to securing global food security amid climate change threats to biodiversity. However, their conservation is partly limited by spatio-temporal distribution knowledge gaps mostly because they are not morphologically charismatic species to attract conservation attention. Therefore, to contribute to the conservation planning of crop wild relatives, this study assessed the present-day distribution and predicted the potential effect of climate change on the distribution of 15 Vigna crop wild relative taxa in Benin under two future climate change scenarios (RCP 4.5 and RCP 8.5) at the 2055-time horizon. MaxEnt model, species occurrence records, and a combination of climate- and soil-related variables were used. The model performed well (AUC, mean = 0.957; TSS, mean = 0.774). The model showed that (i) precipitation of the driest quarter and isothermality were the dominant environmental variables influencing the distribution of the 15 wild Vigna species in Benin; (ii) about half of the total land area of Benin was potentially a suitable habitat of the studied species under the present climate; (iii) nearly one-third of the species may shift their potentially suitable habitat ranges northwards and about half of the species may lose their suitable habitats by 5 to 40% by 2055 due to climate change; and (iv) the existing protected area network in Benin was ineffective in conserving wild Vigna under the current or future climatic conditions, as it covered only about 10% of the total potentially suitable habitat of the studied species. The study concludes that climate change will have both negative and positive effects on the habitat suitability distribution of Vigna crop wild relatives in Benin such that the use of the existing protected areas alone may not be the only best option to conserve the wild Vigna diversity. Integrating multiple in situ and ex situ conservation approaches taking into account “other effective area-based conservation measures” is recommended. This study provides a crucial step towards the development of sustainable conservation strategies for Vigna crop wild relatives in Benin and West Africa.
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TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Abstract: We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright 2005 Royal Meteorological Society.
17,977 citations
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
13,120 citations
University of Melbourne1, Stony Brook University2, City University of New York3, Princeton University4, University of Lausanne5, University of California, Berkeley6, University of Alaska Fairbanks7, National Institute of Water and Atmospheric Research8, Commonwealth Scientific and Industrial Research Organisation9, University of São Paulo10, University of Missouri11, Consejo Nacional de Ciencia y Tecnología12, University of Kansas13, Landcare Research14, AT&T15, McGill University16, James Cook University17, Swiss Federal Institute for Forest, Snow and Landscape Research18
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.
7,589 citations
TL;DR: Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models and a new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed.
Abstract: Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.
6,044 citations
TL;DR: In this article, the authors provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of Kappa.
Abstract: Summary
1In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion.
2In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel.
3The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence.
4When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence.
5Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.
3,518 citations