A method for predicting plant species abundance changes under seasonal drought conditions based on timber density

Abstract

This invention discloses a method for predicting changes in plant species abundance under seasonal drought conditions based on timber density. The method involves obtaining the timber density of sun-facing branches of a species; surveying individual species; extracting the species abundance of each species from the survey data using the "stringr" and "dplyr" packages in the R language and converting it into relative abundance; standardizing the values ​​of relative abundance, timber density, and survey year using the "scale" package in R; and calculating a generalized linear mixture model using the "MuMIn" and "lme4" packages in R, with relative abundance as the response variable, timber density and year as fixed effects, and year and species as random effects. The method examines the change in relative abundance of species with year as a function of timber density, showing that species with higher timber density increase in abundance with increasing year. This invention helps reveal the changing trends and response mechanisms of species abundance under climate change, providing a scientific basis for forestry practices such as afforestation of barren mountains and forest management.

Description

Technical Field

[0001] This invention belongs to the field of forestry engineering, specifically relating to a method for predicting changes in plant species abundance under seasonal drought conditions based on timber density. Background Technology

[0002] Forests are among the most important ecosystems on Earth, and their biodiversity has significant ecological, economic, and social value. Currently, global climate change is ongoing, with rising greenhouse gas concentrations, continued warming of the atmosphere, oceans, and land, and an increase in extreme weather events such as heat waves, heavy rainfall, droughts, and tropical cyclones worldwide, threatening forest biodiversity. Under the influence of global climate change, precipitation patterns are altering globally and in specific regions, and extreme droughts are causing widespread forest degradation. There is a growing body of research, both domestically and internationally, on predicting the vulnerability of plant species to water limitation using physiological characteristics (such as leaf turgor pressure loss point and stem plugging resistance) to understand the potential changes in plant distribution and ecosystem structure.

[0003] Wood density is an important determinant of the water conductivity and water storage capacity of the xylem. It can be used to estimate the aboveground biomass and carbon storage of trees. Species with low wood density usually have higher growth rates and higher water conductivity, while species with high wood density can tolerate more frequent leaf water loss or more extreme drought. In other words, wood density is a functional trait that can simultaneously reflect the growth rate and drought resistance of a plant species.

[0004] South China, located on the southeastern edge of Eurasia, has a pronounced monsoon climate. In recent years, under the influence of global warming, extreme precipitation events have become more frequent. With increasing precipitation intensity and decreasing number of rainy days, annual precipitation is becoming increasingly uneven, leading to a higher probability of extreme drought events and a trend towards wetter wet seasons and drier dry seasons. Quantifying the differences in drought resistance among species due to variations in timber density under seasonal drought conditions, and further quantifying the temporal changes in species richness under these conditions, helps in studying changes in community biodiversity and its response to climate change, providing a theoretical basis for forestry production operations and biodiversity conservation. Summary of the Invention

[0005] This invention aims to determine the impact of seasonal drought on species abundance and assess the response of species abundance to seasonal drought in order to predict changes in species abundance. To this end, this invention provides a method for predicting changes in plant species abundance under seasonal drought conditions based on timber density.

[0006] The method of the present invention for predicting changes in plant species abundance under seasonal drought conditions based on timber density includes the following steps:

[0007] Step 1: In the early morning, use high-branch shears to collect sun-facing branches from at least 5 individuals of the same species, place them in a damp black plastic bag for preservation and bring them back to the laboratory. After removing the pith and bark, measure the volume of the branch stem segments using the water displacement method, and dry them to obtain the wood density.

[0008] Step 2: Conduct routine community surveys of individual species, including measurement, location, identification, tagging, and recording.

[0009] Step 3: Use the "stringr" and "dplyr" packages in the R language to extract the species abundance of each species in the survey data and convert it into relative species abundance. The relative species abundance is (abundance of a certain species / sum of abundance of all species) × 100%.

[0010] Step 4: Use the "scale" package in the R language to standardize the values ​​of relative abundance of species, species wood density, and survey year; among them, the relative abundance of species is standardized at the species level to remove the influence of the difference in absolute abundance of species.

[0011] Step 5: Use the "MuMIn" and "lme4" packages in the R language to calculate the generalized linear mixture model. Species relative abundance is the response variable, wood density and year are fixed effects, and year and species are random effects. Test the change of species relative abundance in response to year with wood density. As the year increases, the species abundance of species with higher wood density increases more.

[0012] Preferably, the wood density is obtained by drying at 70°C for 72 hours, measuring the dry weight, and dividing the dry weight by the volume to obtain the wood density.

[0013] Preferably, the survey of species individuals using conventional community surveys refers to the survey of species individuals with a diameter at breast height (DBH) of 1 cm or more using conventional community surveys.

[0014] The present invention has the following beneficial effects:

[0015] 1. The method for determining the functional traits of species used in this invention is simple and the data is easy to obtain.

[0016] 2. This invention helps to reveal the changing trends and response mechanisms of species abundance under climate change, and provides a scientific basis for forestry practices such as afforestation of barren mountains and forest management.

[0017] 3. The generalized linear mixture model of species abundance variation and wood density obtained in this invention can be applied to the field of forestry engineering. Attached image description:

[0018] Figure 1 This is a graph showing the results of the fitted model. Detailed implementation method:

[0019] The following embodiments are further illustrations of the present invention, but not limitations thereof.

[0020] Example 1:

[0021] The study site in this embodiment is Dinghushan in Zhaoqing City, Guangdong Province, located on the southern edge of the subtropical monsoon climate zone. Dinghushan experienced an extreme drought event in 2013, and its precipitation pattern from 2005 to 2018 showed a trend of more precipitation in the wet season and less precipitation in the dry season. A 20-ha dynamic monitoring site of subtropical evergreen broad-leaved forest in southern Dinghushan was selected. The 20-ha site was divided into 500 small quadrats of 20m×20m using a total station to facilitate subsequent investigations.

[0022] The method for determining wood density is as follows: In the early morning, use high-branch pruning shears to collect sun-facing branches from at least 5 individuals of the same species, place them in a moist black plastic bag for preservation, and bring them back to the laboratory as soon as possible. After removing the pith and bark, measure the volume of the branch stem segments using the water displacement method, and measure the dry weight after drying at 70°C for 72 hours. Divide the dry weight by the volume to obtain the wood density.

[0023] We conducted a routine community survey of individuals of species with a diameter at breast height (DBH) of 1 cm or more, and carried out measurement, location, identification, tagging and recording. This survey was conducted once in 2005, 2010, 2015 and 2020, and a total of 4 survey data were obtained.

[0024] The species abundance of each species in the survey data was extracted using the "stringr" and "dplyr" packages in R, and then converted into relative species abundance (abundance of a specific species / sum of abundance of all species) × 100%. The values ​​of relative species abundance, species wood density, and year were standardized using the "scale" package. A generalized linear mixture model was calculated using the "MuMIn" and "lme4" packages in R, with relative species abundance as the response variable, wood density and year as fixed effects, and year and species as random effects. The model was tested to examine the change in the response of relative species abundance to year with wood density, as follows: model <- lmer(abundance ~ year * trait + (year | species)). The fitted model results are shown below. Figure 1 As shown:

[0025] The significance test result, sig value, is less than 0.1, indicating that at a 90% confidence level, the abundance of species with higher wood density increases more with increasing years.

Claims

1. A method for predicting plant species abundance changes under seasonal drought conditions based on timber density, characterized in that, Includes the following steps: Step 1: In the early morning, use high-branch shears to collect sun-facing branches from at least 5 individuals of the same species, place them in a damp black plastic bag for preservation and bring them back to the laboratory. After removing the pith and bark, measure the volume of the branch stem segments using the water displacement method, and dry them to obtain the wood density. Step 2: Conduct routine community surveys of individual species, including measurement, location, identification, tagging, and recording. Step 3: Use the "stringr" and "dplyr" packages in the R language to extract the species abundance of each species in the survey data and convert it into relative species abundance. The relative species abundance is (abundance of a certain species / sum of abundance of all species) × 100%. Step 4: Use the "scale" package in the R language to standardize the values ​​of relative abundance of species, species wood density, and year of survey. Step 5: Use the "MuMIn" and "lme4" packages in the R language to calculate the generalized linear mixture model. Species relative abundance is the response variable, wood density and year are fixed effects, and year and species are random effects. Test the change of species relative abundance in response to year with wood density. As the year increases, the species abundance of species with higher wood density increases more.

2. The method according to claim 1, characterized in that, The wood density obtained by drying is determined by measuring the dry weight after drying at 70℃ for 72 hours, and then dividing the dry weight by the volume to obtain the wood density.

3. The method according to claim 1, characterized in that, The aforementioned survey of species individuals using conventional community surveys refers to the survey of species individuals with a diameter at breast height (DBH) of 1 cm or more using conventional community surveys.

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