Previous studies have shown that climate change has significant cumulative effects on vegetation growth. However, there remains a gap in understanding the characteristics of cumulative climatic effects on different vegetation types and the underlying driving mechanisms. In this study, using the normalized difference vegetation index data from 1982 to 2015, along with accumulated temperature, precipitation, and solar radiation data, we quantitatively investigated the intra-annual cumulative effects of climatic factors on global vegetation biomes across climatic zones. We also explored the underlying driving mechanisms. The results indicate that precipitation has a longer intra-annual cumulative effect on vegetation, with effects lasting up to 12 months for large percentages of most vegetation biomes. The cumulative effect of solar radiation is mostly concentrated within 0–6 months. Temperature has a shorter cumulative effect, with no significant cumulative effect of temperature on large percentages of tree-type vegetation. Compared to other vegetation types, evergreen broadleaf forests, close shrublands, open shrublands, savannas, and woody savannas exhibit more complex cumulative climatic effects. Each vegetation type shows a weak-to-moderate correlation with accumulated precipitation while exhibiting strong-to-extremely-strong positive correlations with accumulated temperature and accumulated solar radiation. The climate-induced regulations of water, heat, and nutrient, as well as the intrinsic mechanisms of vegetation’s tolerance, resistance, and adaptation to climate change, account for the significant heterogeneity of cumulative climatic effects across vegetation biomes in different climatic zones. This study contributes to enriching the theoretical understanding of the relationship between vegetation growth and climate change. It also offers crucial theoretical support for developing climate change adaptation strategies and improving future “vegetation-climate” models.

Global climate warming has profoundly affected terrestrial ecosystems. The Tibetan Plateau (TP) is an ecologically vulnerable region that emerged as an ideal place for investigating the mechanisms of vegetation response to climate change. In this study, we constructed an annual synthetic NDVI dataset with 500 m resolution based on MOD13A1 products from 2000 to 2021, which were extracted by the Google Earth Engine (GEE) and processed by the Kalman filter. Furthermore, considering topographic and climatic factors, a thorough analysis was conducted to ascertain the causes and effects of the NDVI’s spatiotemporal variations on the TP. The main findings are: (1) The vegetation coverage on the TP has been growing slowly over the past 22 years at a rate of 0.0134/10a, with a notable heterogeneity due to its topography and climate conditions. (2) During the study period, the TP generally showed a “warming and humidification” trend. The influence of human activities on vegetation growth has exhibited a favorable trajectory, with a notable acceleration observed since 2011. (3) The primary factor influencing NDVI in the southeastern and western regions of the TP was the increasing temperature. Conversely, vegetation in the northeastern and central regions was mostly regulated by precipitation. (4) Combined with the principal component analysis, a PCA-CNN-LSTM (PCL) model demonstrated significant superiority in modeling NDVI sequences on the Tibetan Plateau. Understanding the results of this paper is important for the sustainable development and the formulation of ecological policies on the Tibetan Plateau.

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

The Hubao–Egyu Urban Agglomeration (HBEY) was a crucial ecological barrier in northern China. To accurately assess the impact of climate change on vegetation growth, it is essential to consider the effects of time lag and accumulation. In this study, we used a newly proposed kernel Normalized Difference Vegetation Index (kNDVI) as the metric for vegetation condition, and employed partial correlation analysis to ascertain the lag and accumulation period of vegetation response to climate by considering different scenarios (No/Lag/Acc/LagAcc) and various combinations. Moreover, we further modified the traditional residual analysis model. The results are as follows: (1) From 2000 to 2022, the HBEY experienced extensive and persistent greening, with a kNDVI slope of 0.0163/decade. Precipitation was identified as the dominant climatic factor influencing vegetation dynamics. (2) In HBEY, the lag effect of temperature was most distinct, particularly affecting the vegetation in cropland and grassland. The accumulation effect of precipitation was pronounced in grassland. (3) Incorporating lag and accumulation effects into models increases the explanatory power of climate impacts on vegetation dynamics by 6.95% compared to traditional residual models. Our findings hold essential implications for regional ecological regulation and climate change response research.

Tobacco blue mold is a serious tobacco disease, which has high risk of invasion in many areas, therefore, it is of particular importance to strengthen the quarantine work and to make a good prediction. Since each species has its own particular relatively stable ecological niche, one can predict the potential geographical distribution of tobacco blue mold in target areas like China. Based on the known distribution data of tobacco blue mold, 20 environmental factors were selected and combined support vector machine (SVM) with ecological niche model was used to predict the potential spatial distribution of tobacco blue mold and to obtain the optimal combination of SVM parameters through the brute force search algorithm. Search algorithm overcame the shortage of empirical method and improved the accuracy of prediction. The method was proved to be effective and feasible by cross validation which provided a robust theoretical basis for preventing the intrusion and spread of tobacco blue mold. Bangladesh J. Bot. 51(4): 883-888, 2022 (December) Special