揭示群落结构及其环境响应的联合物种分布模型的研究进展

物种分布模型通常用于基础生态和应用生态研究,用来确定影响生物分布和物种丰富度的因素,量化物种与非生物条件的关系,预测物种对土地利用和气候变化的反应,并确定潜在的保护区.在传统的物种分布模型中,生物的相互作用很少被纳入,而联合物种分布模型(JSDMs)作为近年提出的一种新的可行方法,可以同时考虑环境因素和生物交互作用,因而成为分析生物群落结构和种间相互作用过程的有力工具.JSDMs以物种分布模型(SDMs)为基础,通常采用广义线性回归模型建立物种对环境变量的多变量响应,以随机效应的形式获取物种间的关联,同时结合隐变量模型(LVMs),并基于Laplace近似和马尔科夫蒙脱卡罗模拟的最大似然估计或贝叶斯方法来估算模型参数.本文对JSDMs的产生及理论基础进行归纳总结,重点介绍了不同类型JSDMs的特点及其在现代生态学中的应用,阐述了JSDMs的应用前景、使用过程中存在的问题及发展方向.随着对环境因素与多物种种间关系研究的深入,JSDMs将是今后物种分布模型研究的重点.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

Evaluation of the DNA barcoding approach to develop a reference data-set for the threatened flora of Sicily

The Mediterranean Basin is one of the most significantly altered World Biodiversity Hotspots with extensive habitat loss and fast genetic population erosion, for which urgent biodiversity reconnaissance and preservation actions are required. In particular, Sicily has about 600 taxa classified as threatened or near-threatened. The correct recognition and identification of such biodiversity is required for supporting further activities. The objective of this work is to assess the ability of the DNA barcoding approach to identify different taxonomic groups from a collection of the most threatened plant taxa, throughout natural Sicilian populations. The evaluation of the DNA barcoding core markers, rbcL and matK, was carried out on 30 taxa belonging to 13 families. DNA barcode fragments were recovered from all taxa (100%). The rbcL gene was recovered from 97% of the taxa and matK gene from 73%. In this test, 19 taxa overall (63%) were totally resolved at the specific or subspecific level, by at least one of the core markers. Fourteen of the 17 most threatened taxa (EN, CR) included in this work were totally discriminated. The matK and rbcL locus, respectively, resolved 64% and 48% of the taxa successfully sequenced. The matK gene expressed the highest genetic distance (K2P value), from 0.4% to 8.6%, against a range of 0.1–2% of rbcL gene. However, the rbcL gene appeared a good compromise between PCR, sequencing success and species-level resolution. Cryptic groups suggest the implementation of additional barcoding markers or different primer combinations, particularly for matK, in order to increase the performances. However, this preliminary result confirms the potential of the barcoding approach for quick identification of unknown and heterogeneous plant groups to generate a dedicated reference data-set of the threatened Sicilian flora for a wide range of applications.     

Using a species distribution model to guide NSW surveys of the long‐footed potoroo (Potorous longipes)

Knowledge of threatened species’ distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species’ geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare.     

Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives

Aim Understanding the spatial patterns of species distribution and predicting the occurrence of high biological diversity and rare species are central themes in biogeography and environmental conservation. The aim of this study was to model and scrutinize the relative contributions of climate, topography, geology and land‐cover factors to the distributions of threatened vascular plant species in taiga landscapes in northern Finland. Location North‐east Finland, northern Europe. Methods The study was performed using a data set of 28 plant species and environmental variables at a 25‐ha resolution. Four different stepwise selection algorithms [Akaike information criterion (AIC), Bayesian information criterion (BIC), adaptive backfitting, cross selection] with generalized additive models (GAMs) were fitted to identify the main environmental correlates for species occurrences. The accuracies of the distribution models were evaluated using fourfold cross‐validation based on the area under the curve (AUC) derived from receiver operating characteristic plots. The GAMs were tentatively extrapolated to the whole study area and species occurrence probability maps were produced using GIS techniques. The effect of spatial autocorrelation on the modelling results was also tested by including autocovariate terms in the GAMs. Results According to the AUC values, the model performance varied from fair to excellent. The AIC algorithm provided the highest mean performance (mean AUC = 0.889), whereas the lowest mean AUC (0.851) was obtained from BIC. Most of the variation in the distribution of threatened plant species was related to growing degree days, temperature of the coldest month, water balance, cover of mire and mean elevation. In general, climate was the most powerful explanatory variable group, followed by land cover, topography and geology. Inclusion of the autocovariate only slightly improved the performance of the models and had a minor effect on the importance of the environmental variables. Main conclusions The results confirm that the landscape‐scale distribution patterns of plant species can be modelled well on the basis of environmental parameters. A spatial grid system with several environmental variables derived from remote sensing and GIS data was found to produce useful data sets, which can be employed when predicting species distribution patterns over extensive areas. Landscape‐scale maps showing the predicted occurrences of individual or multiple threatened plant species may provide a useful basis for focusing field surveys and allocating conservation efforts.     

Declining populations in one of the last refuges for threatened mammal species in northern Australia

Australia has contributed a disproportionate number of the world's mammal extinctions over the past 200 years, with the greatest loss of species occurring through the continent's southern and central arid regions. Many taxonomically and ecologically similar species are now undergoing widespread decline across the northern Australian mainland, possibly driven by predation by feral cats and changed fire regimes. Here, we report marked recent declines of native mammal species in one of Australia's few remaining areas that support an intact mammal assemblage, Melville Island, the largest island off the northern Australian coast. We have previously reported a marked decline on Melville Island of the threatened brush‐tailed rabbit‐rat (Conilurus penicillatus) over the period 2000–2015, linked to predation by feral cats. We now report a 62% reduction in small mammal trap‐success and a 36% reduction in site‐level species richness over this period. There was a decrease in trap‐success of 90% for the northern brown bandicoot (Isoodon macrourus), 64% for the brush‐tailed rabbit‐rat and 63% for the black‐footed tree‐rat (Mesembriomys gouldii), but no decline for the common brushtail possum (Trichosurus vulpecula). These results suggest that populations of native mammals on Melville Island are exhibiting similar patterns of decline to those recorded in Kakadu National Park two decades earlier, and across the northern Australian mainland more generally. Without the implementation of effective management actions, these species are likely to be lost from one of their last remaining strongholds, threatening to increase Australia's already disproportionate contribution to global mammal extinctions.     

Past and present potential distribution of the Iberian Abies species: a phytogeographic approach using fossil pollen data and species distribution models

Aim Quaternary palaeopalynological records collected throughout the Iberian Peninsula and species distribution models (SDMs) were integrated to gain a better understanding of the historical biogeography of the Iberian Abies species (i.e. Abies pinsapo and Abies alba). We hypothesize that SDMs and Abies palaeorecords are closely correlated, assuming a certain stasis in climatic and topographic ecological niche dimensions. In addition, the modelling results were used to assign the fossil records to A. alba or A. pinsapo, to identify environmental variables affecting their distribution, and to evaluate the ecological segregation between the two taxa. Location The Iberian Peninsula. Methods For the estimation of past Abies distributions, a hindcasting process was used. Abies pinsapo and A. alba were modelled individually, first calibrating the model for their current distributions in relation to the present climate, and then projecting it into the past—the last glacial maximum (LGM) and the Middle Holocene periods—in relation to palaeoclimate simulations. The resulting models were compared with Iberian‐wide fossil pollen records to detect areas of overlap. Results The overlap observed between past Abies refugia—inferred from fossil pollen records—and the SDMs helped to construct the Quaternary distribution of the Iberian Abies species. SDMs yielded two well‐differentiated potential distributions: A. pinsapo throughout the Baetic mountain Range and A. alba along the Pyrenees and Cantabrian Range. These results propose that the two taxa remained isolated throughout the Quaternary, indicating a significant geographical and ecological segregation. In addition, no significant differences were detected comparing the three projections (present‐day, Mid‐Holocene and LGM), suggesting a relative climate stasis in the refuge areas during the Quaternary. Main conclusions Our results confirm that SDM projections can provide a useful complement to palaeoecological studies, offering a less subjective and spatially explicit hypothesis concerning past geographic patterns of Iberian Abies species. The integration of ecological‐niche characteristics from known occurrences of Abies species in conjunction with palaeoecological studies could constitute a suitable tool to define appropriate areas in which to focus proactive conservation strategies.     

Effect of salinity,temperature and hypersaline conditions on the seed germination in Limonium mansanetianum an endemic and threatened Mediterranean species

Abstract

Limonium mansanetianum is catalogued as critically threatened (CR) species and it is included in Valencian Catalogue of Threatened Plant Species. Limonium mansanetianum is a gypsicolous species, which only lives in a restricted area to south-centre of Valencia province (Spain). The species is a low-branched woody shrub with summer flowering. The influence of incubation temperature (10°, 15°, 20° and 25°/20?°C) and salinity (0%–3.0% NaCl) on seed germination of L. mansanetianum was studied. Best seed germination was obtained in distilled water controls. Seed germination decreased with an increase in salinity and few seeds germinated at 2.5% and 3.0% NaCl. Optimal temperature regime for germination was 15?°C where germination in 0.5% and 1.0% NaCl was not affected. Recovery and hypersaline conditions experiments showed that L. mansanetianum seeds displayed a greater tolerance to high salinity and temperature stress before germination.     

列入《濒危野生动植物物种国际贸易公约》附录的物种的新标准

ＣＩＴＥＳ（濒危野生动植物国际贸易公约）是一个通过控制贸易的方法来维护物种生存及其持续利用的国际法规,其附录中所列的物种是重点管理的对象,主要根据现有的Ｂｅｒｎｅ标准来确定。但是,长期的实践证明,它已不能满足实际的需要。１９９２年６月ＣＩＴＥＳ常务委员会要求世界保护联盟协助制定一个简明、实用、科学和客观的新标准,本文对此作一简介和讨论。     

Relationships between seed germination requirements and ecophysiological characteristics aid the recovery of threatened native plant species in Western Australia

Summary One of the foremost technical issues addressed in reintroduction and restoration projects is the feasibility of establishing living plants. To advance the recovery process, the germination requirements of 201 threatened Western Australian seed‐bearing taxa representing a range of life forms, families and ecophysiological characteristics were studied. Procedures used to stimulate germination in otherwise dormant seed involved pretreatment using thermal shock, scarification, seed coat removal, soaking in an aqueous smoke solution and/or additions of the growth hormone gibberellic acid (GA₃). Sixty‐one taxa germinated under the basic trial conditions of light (12‐ h photoperiod), temperature (constant 15°C) and moisture, without additional pretreatments. These taxa were generally those with canopy‐stored seeds in the families Proteaceae and Casuarinaceae, and small‐seeded taxa in Myrtaceae. Taxa with soil‐stored seeds required single or multiple cues to stimulate germination. Seeds in the families Fabaceae and Mimosaceae were dependent on cracking of the seed coat, mechanically through nicking of the testa or through thermal shock, as were several non‐leguminous species of the Sterculiaceae and Liliaceae. Complete or partial removal of seed coats, in conjunction with GA₃ enhanced germination percentage in some taxa of the Myoporaceae, Lamiaceae and Myrtaceae. Application of GA₃ also enhanced germination percentage in members of the Epacridaceae. Several taxa previously stimulated by aqueous smoke solutions were equally responsive to additions of GA₃ after complete seed coat removal. In general, species with seed weights greater than 10 mg germinated better under a range of conditions than those with lighter seeds. There was no difference in germinability between resprouter and seeder species, and no obvious relationship between seed weight and germination rate. In the light of previous studies these results indicate that the relationship between germination requirements and ecophysiological characteristics is similar for both threatened and common species. These data will enable better prediction of likely dormancy breaking cues for other related species and will greatly assist the process of recovery and restoration work for mining operations and community bushland regeneration as well as single species reintroductions.     

Species distribution models contribute to determine the effect of climate and interspecific interactions in moving hybrid zones

Climate is a major factor delimiting species’ distributions. However, biotic interactions may also be prominent in shaping geographical ranges, especially for parapatric species forming hybrid zones. Determining the relative effect of each factor and their interaction of the contact zone location has been difficult due to the lack of broad scale environmental data. Recent developments in species distribution modelling (SDM) now allow disentangling the relative contributions of climate and species’ interactions in hybrid zones and their responses to future climate change. We investigated the moving hybrid zone between the breeding ranges of two parapatric passerines in Europe. We conducted SDMs representing the climatic conditions during the breeding season. Our results show a large mismatch between the realized and potential distributions of the two species, suggesting that interspecific interactions, not climate, account for the present location of the contact zone. The SDM scenarios show that the southerly distributed species, Hippolais polyglotta, might lose large parts of its southern distribution under climate change, but a similar gain of novel habitat along the hybrid zone seems unlikely, because interactions with the other species (H. icterina) constrain its range expansion. Thus, whenever biotic interactions limit range expansion, species may become ‘trapped’ if range loss due to climate change is faster than the movement of the contact zone. An increasing number of moving hybrid zones are being reported, but the proximate causes of movement often remain unclear. In a global context of climate change, we call for more interest in their interactions with climate change.     

Testing the utility of species distribution modelling using Random Forests for a species in decline

Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae

Species distribution models (SDM) are a useful tool for predicting species range shifts in response to global warming. However, they do not explore the mechanisms underlying biological processes, making it difficult to predict shifts outside the environmental gradient where the model was trained. In this study, we combine correlative SDMs and knowledge on physiological limits to provide more robust predictions. The thermal thresholds obtained in growth and survival experiments were used as proxies of the fundamental niches of two foundational marine macrophytes. The geographic projections of these species’ distributions obtained using these thresholds and existing SDMs were similar in areas where the species are either absent‐rare or frequent and where their potential and realized niches match, reaching consensus predictions. The cold‐temperate foundational seaweed Himanthalia elongata was predicted to become extinct at its southern limit in northern Spain in response to global warming, whereas the occupancy of southern‐lusitanic Bifurcaria bifurcata was expected to increase. Combined approaches such as this one may also highlight geographic areas where models disagree potentially due to biotic factors. Physiological thresholds alone tended to over‐predict species prevalence, as they cannot identify absences in climatic conditions within the species’ range of physiological tolerance or at the optima. Although SDMs tended to have higher sensitivity than threshold models, they may include regressions that do not reflect causal mechanisms, constraining their predictive power. We present a simple example of how combining correlative and mechanistic knowledge provides a rapid way to gain insight into a species’ niche resulting in consistent predictions and highlighting potential sources of uncertainty in forecasted responses to climate change.     

甘南亚高寒草甸稀有种对物种多样性和物种多度分布格局的贡献

稀有种不仅影响群落的物种多度分布格局, 同时也是α多样性的重要贡献者。本研究主要通过加性分配和Fortran软件的RAD程序包拟合的方法, 研究了甘南亚高寒草甸不同坡向物种多样性及多度分布格局的变化, 分析了物种多度分布格局及其α多样性的变化特征, 确定了稀有种在物种多度分布格局中的相对贡献。结果表明: (1)在南坡到北坡的变化中, 环境因子差异比较明显, 其中, 土壤全磷、有机碳、速效磷、碳氮比及含水量呈递增趋势; 土壤氮磷比和pH值呈递减趋势; 土壤全氮在西坡显著低于其他坡向, 而速效氮在所有坡向上差异不显著。(2)稀有种对群落物种多样性的影响在南-北坡向梯度上依次增大, 去除稀有种的影响在各坡向均高于去除非稀有种, 可见, 稀有种在甘南亚高寒草甸物种多样性中的相对贡献高于非稀有种。(3)各坡向的稀有种资源获取模式以随机分配占领模式(random fraction模型)为主, 而非稀有种则以生态位优先占领模式(geometric series模型)为主。由于稀有种有较大的扩散率, 在物种多样性较高的生态系统中, 物种之间的生态位重叠会更加明显, 从而抑制物种多样性的增加, 因此能达到维持原有物种多样性的目的。     

Climate-based models of spatial patterns of species richness in Egypt's butterfly and mammal fauna

Aim  Identifying areas of high species richness is an important goal of conservation biogeography. In this study we compared alternative methods for generating climate-based estimates of spatial patterns of butterfly and mammal species richness.
Location  Egypt.
Methods  Data on the occurrence of butterflies and mammals in Egypt were taken from an electronic database compiled from museum records and the literature. Using M axent , species distribution models were built with these data and with variables describing climate and habitat. Species richness predictions were made by summing distribution models for individual species and by modelling observed species richness directly using the same environmental variables.
Results  Estimates of species richness from both methods correlated positively with each other and with observed species richness. Protected areas had higher species richness (both predicted and actual) than unprotected areas.
Main conclusions  Our results suggest that climate-based models of species richness could provide a rapid method for selecting potential areas for protection and thus have important implications for biodiversity conservation.     

Predicting and mapping the spatial distribution of Chamaecyparis formosensis in central Taiwan in a GIS with species distribution models

Analyzing the relationship between species and environment is always a focal question of ecological research. In recent years species distribution models (SDMs) has been widely used to predict the spatial distribution of species. SDMs are numerical tools that combine observations and species occurrence or abundance with environmental variables to predict the spatial distribution of species across landscapes, sometimes requiring extrapolation in space and time. Chamaecyparis formosensis (Taiwan red cypress, TRCs) is a coniferous species endemic to Taiwan, where it natural grows in the central mountains at moderate to high altitudes of 800–2800 m, and most stands in the range of 1500–2150 m. It is threatened by habitat loss and over-cutting for its valuable timber. To preserve TRCs species and achieve sustainable use of biological resources, we choose TRCs as a target for the study to predict its distribution in central Taiwan.The pure forests of TRCs in the study area were mainly located in Pachsienshan (P), Shouchentashan (S) and Baigou Mountain (B) in central Taiwan, and the distribution data were originally obtained by The Third Survey of Forest Resources and Land Use in Taiwan. Elevation, slope, aspect, and three vegetation indices were derived from both SPOT-5 satellite images and DEM. GIS technique was used to overlay those factors. Discriminant analysis (DA), decision tree (DT) and maximum entropy (MAXENT), three commonly used SDMs, were applied based on above-mentioned six variables to predict the suitable habitat of TRCs, and to evaluate which the best model is in terms of accuracy and efficiency. Three experiment designs (ED1, ED2 and ED3) with different combinations of samples were used for model building and validation. The 200 target samples were collected from the site P–B, B–S and P–S for model building under ED1, ED2 and ED3 respectively, while the 100 samples were collected from the site S, P and B for model validation. All experiment designs had same 1350 background samples. The results showed that the overall accuracy and kappa coefficient of DT (96%, 0.88) was higher than that of MAXENT (91%, 0.70), and their accuracies were better than that of DA (84%, 0.58). All the three models were highly efficient in implementation of model construction and evaluation, while the DT model was difficult for generating the entire predicted map of potential habitat due to its complex conditional sentence. Vegetation indices derived from SPOT-5 satellite images could not improve model accuracy because of its insufficiency of spectral resolution and spatial resolution. High spatial resolution and spectral resolution remotely sensed imagery should be used in our future research to improve model performance and reliability.