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墨西哥西部的整体投影模型与可持续森林管理

Integral Projection Models and Sustainable Forest Management of in Western Mexico.

作者信息

Torres-García Ignacio, León-Jacinto Alejandro, Vega Ernesto, Moreno-Calles Ana Isabel, Casas Alejandro

机构信息

Environmental Transdisciplinary Studies, Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de México, Morelia, México.

Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, México.

出版信息

Front Plant Sci. 2020 Aug 11;11:1224. doi: 10.3389/fpls.2020.01224. eCollection 2020.

DOI:10.3389/fpls.2020.01224
PMID:32903574
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7438764/
Abstract

In México, at least 37 species are extracted from wild populations for producing distilled spirits. This activity involves harvesting mature agaves just before producing their inflorescences, which cancels sexual reproduction of plants used. The increasing demand of agaves spirits in national and international markets is determining a strong pressure on wild populations, most of them lacking adequate management. In addition, the dynamics of agave populations may be affected by natural phenomena like oscillation of rainfall regimes, which affects the recruitment of agave seedlings, or the scarcity of pollinators that may affect seed production and general population dynamics. We studied the demography of wild populations of to analyze critical conditions for populations recovery, modelling the effects of rainfall trends on the demographic performance of this species, and exploring response of populations to hypothetical extraction regimes and reforestation efforts. Our study was performed in four well-conserved wild populations in Central Western Mexico, each population was sampled in a plot of about one hectare composed by 10 subplots 50 x 5 m (2500 m). Populations were monitored yearly between 2011 and 2013, measuring plant size, reproductive individuals, and fecundity. Data were analyzed through integral projection models by using the IPMpack for R, to perform prospective analyses. We in addition constructed stochastic models to explore the possible influence of rainfall variation on species demography, using data for the drier and wetter years of the study period. Population growth varied from λ=1.003 to λ=0.899 among populations and years, and exceptionally λ=0.559 after a fire event. Low rainfall decreases λ values, indicating especial limitations to harvesting agaves during dry years whose frequency most probably will increase. In general, extraction rates from 10% to 30% of mature individuals are viable to maintain λ above 1, and these rates may be higher if new plants are introduced in populations. Depending on levels of extraction, our models suggest that it is necessary to carry out actions of reforestation, and management according to the trends found in each site. This is one indispensable condition to maintain λ close to or greater than 1. Sustainable extraction of wild agaves is possible, some communities are already carrying out a repertoire of goods practices in this direction, but together with ecological criteria and good management techniques, strict regulations and social organization are needed to achieve it.

摘要

在墨西哥,至少有37种龙舌兰从野生种群中被采摘用于生产蒸馏酒。这项活动包括在龙舌兰长出花茎之前采摘成熟的植株,这使得所用植株无法进行有性繁殖。国内外市场对龙舌兰酒需求的不断增加,给野生种群带来了巨大压力,其中大多数缺乏适当的管理。此外,龙舌兰种群动态可能会受到自然现象的影响,比如降雨模式的波动会影响龙舌兰幼苗的补充,或者传粉者的稀缺可能会影响种子产量和总体种群动态。我们研究了野生龙舌兰种群的种群统计学,以分析种群恢复的关键条件,模拟降雨趋势对该物种种群动态表现的影响,并探索种群对假设的采摘模式和重新造林努力的反应。我们的研究在墨西哥中西部四个保护良好的野生种群中进行,每个种群在一个约一公顷的样地中采样,该样地由10个50×5米(2500平方米)的子样地组成。在2011年至2013年期间每年对种群进行监测,测量植株大小、繁殖个体和繁殖力。通过使用R语言的IPMpack软件包,利用积分投影模型对数据进行分析,以进行前瞻性分析。我们还构建了随机模型,利用研究期间干旱年份和湿润年份的数据,探索降雨变化对物种种群统计学的可能影响。不同种群和年份的种群增长率在λ=1.003至λ=0.899之间变化,在一次火灾事件后异常低至λ=0.559。低降雨量会降低λ值,这表明在干旱年份采摘龙舌兰存在特殊限制,而干旱年份的频率很可能会增加。一般来说,采摘10%至30%的成熟个体的比率对于维持λ大于1是可行的,如果在种群中引入新植株,这些比率可能会更高。根据采摘水平,我们的模型表明有必要根据每个地点发现的趋势开展重新造林和管理行动。这是使λ接近或大于1的一个不可或缺的条件。野生龙舌兰的可持续采摘是可能的,一些社区已经在朝着这个方向开展一系列良好实践,但要实现这一目标,还需要结合生态标准、良好的管理技术、严格的法规和社会组织。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/e614d7dbf740/fpls-11-01224-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/e68c5befff8a/fpls-11-01224-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/362a8ed66853/fpls-11-01224-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/748660902b07/fpls-11-01224-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/c1789040daeb/fpls-11-01224-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/ece070a1307e/fpls-11-01224-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/e614d7dbf740/fpls-11-01224-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/e68c5befff8a/fpls-11-01224-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/362a8ed66853/fpls-11-01224-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/748660902b07/fpls-11-01224-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/c1789040daeb/fpls-11-01224-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/445a/7438764/e614d7dbf740/fpls-11-01224-g006.jpg

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