图1 本研究引种植物种源地(a)与物种分布记录(b)的地理分布。繁殖体来自435个种源地, 由中国科学院西双版纳热带植物园(XTBG)通过野外采集、与其他植物园交换及购买获得。本研究共涵盖1,232个物种, 其3,152,039条分布记录源自全球生物多样性信息网络(GBIF, https://www.gbif.org/) (已剔除异常值)。各物种分布记录数量的中位数为47, 平均值为2,767 (1-500,332)。XTBG的位置(21°41′ N, 101°25′ E)以黑色菱形符号标示, (a)和(b)中的红色实心圆分别代表各种源地和各物种分布地的位置。

Fig. 1 Geographic distribution of provenance (a) and species occurrence records (b) for introduced plants included in this study. Propagules were obtained from 435 provenances through field collection, exchange with other botanical gardens, and purchase, all coordinated by the Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences (XTBG). This study includes 1,232 species, with 3,152,039 occurrence records obtained from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/) and cleaned by removing outliers. The number of occurrence records per species had a median of 47, a mean of 2,767, and ranged from 1 to 500,332. The location of XTBG (21°41′ N, 101°25′ E) is marked by a black diamond. Red circles indicate provenance in (a) and species occurrence records in (b).

图2 基于物种整体分布点计算气候条件较中国科学院西双版纳热带植物园(XTBG)更为极端的比例。以研究物种黄葛树(Ficus viren)为例进行图示说明: 红色长虚线表示XTBG的气候参数值, 蓝色线表示种源地气候, 绿色短虚线表示物种分布地气候中位数, 灰色阴影曲线为采用核密度估计构建的物种气候分布密度(分布点气候数据基于GBIF分布记录与WorldClim 2.1气候数据匹配提取)。该指标通过计算物种分布区中较XTBG气候更为极端一侧的累积比例获得。具体计算规则如下: 最冷月最低温(mtcm)取分布点中较XTBG更温暖者所占比例; 最热月最高温(mtwm)取较XTBG更寒冷者所占比例; 年均温(mat)依据物种分布区气候中位数与XTBG的相对关系确定, 若中位数低于XTBG, 则计算低于XTBG的分布点比例, 反之则计算高于XTBG的分布点比例。降水变量统一计算低于XTBG降水值的分布点比例。该方法以物种气候生态位整体为参照, 区别于基于单一繁殖体来源地的局域适应假设, 适用于气候耐受性由广适基因型主导的情形。

Fig. 2 Climatic differences between Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences (XTBG) and the distribution of plants in the wild based on the proportion of the climatic distribution of each species that was more extreme than the climate at XTBG, illustrated with one of the study species: Ficus virens. Red long-dashed vertical lines indicate the climatic parameter values at XTBG; blue lines indicate the climate at the provenance; green short-dashed vertical lines indicate the median climate across occurrence records of the species; and gray shaded curves represent the density of the species climatic distribution constructed using kernel density estimation, with climatic data for occurrence records extracted by matching GBIF distribution records with WorldClim 2.1. This metric was derived by calculating the cumulative proportion of species occurrence records falling on the side more extreme than the XTBG threshold. Specifically, for minimum temperature of coldest month (mtcm), we calculated the proportion of occurrence records with temperatures warmer than that at XTBG; for maximum temperature of warmest month (mtwm), we calculated the proportion of occurrence records with temperatures colder than that at XTBG. For mean annual temperature (mat), the metric depended on the relative position between the species median and XTBG: When the species median was lower, we calculated the proportion of occurrence records with temperatures lower than that at XTBG; otherwise, we calculated the proportion of occurrence records with temperature higher than that at XTBG. For precipitation variables—annual precipitation (map), precipitation of driest month (pdm), and precipitation of wettest month (pwm)—we consistently calculated the proportion of occurrence records with precipitation lower than that at XTBG. This approach emphasizes climatic properties of the whole species distribution, as opposed to propagule provenance under the assumption of local adaptation, and may be useful when species’ climatic niches are largely determined by the tolerance of generalist genotypes.

图3 气候差异度量与生长型对引种植物存活影响的混合效应Cox比例风险模型支持度比较。横坐标为模型编号(1-63), 按所含气候变量数量从6个递减至1个(由左至右, 灰白条带标识)分组; 纵坐标表示赤池信息准则差值(ΔAIC, 相对于最佳模型的AIC差异)。模型采用3种气候差异量化指标: 种源地气候(蓝色)、物种分布地气候中位数(橙色)以及极端气候比例(红色); 并结合两种随机效应结构: 物种独立随机效应(圆形)和系统发育相关随机效应(三角形)。灰色虚线表示ΔAIC = 3的阈值。图(b)为图(a)中ΔAIC ≤ 10区域的放大视图, 以清晰展示高支持度模型(ΔAIC ≤ 3)的分布。所有模型均以生长型作为固定效应。

Fig. 3 Comparison of empirical support for mixed-effects Cox proportional hazards models assessing effects of climatic difference metrics and growth form on survival of introduced plants. The x-axis indicates candidate model numbers (1-63) grouped by the number of climatic variables included (decreasing from 6 to 1 from left to right, indicated by alternating gray and white bands); the y-axis indicates Akaike information criterion differences (ΔAIC, relative to the best-supported model). Models incorporated three climatic difference metrics: difference from climate at provenance (blue), difference from median climate across occurrence records (orange), and proportion of extreme climat (red); and two random-effect structures: uncorrelated species random effects (circles) and phylogenetically correlated random effects (triangles). The gray dashed line indicates the ΔAIC = 3 threshold. Panel (b) provides a detailed view of the upper range (ΔAIC ≤ 10) in panel (a), highlighting models with highest support (ΔAIC ≤ 3). All models included growth form as a fixed effect.

图4 气候差异与生长型对引种植物存活影响的混合效应Cox比例风险模型比较与效应估计。 (a)各模型包含的气候变量组合。纵坐标为6个气候差异变量: 年均温差异、最冷月最低温差异、最热月最高温差异、年降水量差异、最干月降水量差异以及最湿月降水量差异。横坐标为这6个气候差异变量的非空组合。灰色小圆点表示模型包含的变量, 垂直虚线标记具有最高经验支持度(ΔAIC ≤ 3)的模型, 这些模型所包含的变量以黑色大圆点突出显示。图顶部的数字及灰白相间的垂直条带用于视觉区隔不同模型组。所有模型均将生长型作为解释变量纳入分析。(b)最优模型(ΔAIC ≤ 3)中解释变量的效应估计。图中展示了9个高支持度模型(模型编号130-152)中各变量的回归系数(对数风险比)及其95%置信区间: 红色为温度变量(左轴), 蓝色为降水变量(左轴), 黑色菱形为生长型(右轴)。当95%置信区间不与0 (灰色水平线)重叠时, 认为效应在P = 0.05水平上具有统计学意义。对于气候变量, 回归系数 > 0表示气候差异每增加1个单位, 植株死亡风险相应增加; 对于生长型, 回归系数 < 0表示草本植物死亡风险低于木本植物。

Fig. 4 Comparison of mixed-effects Cox proportional hazards models for introduced plant survival in relation to climatic differences and growth form, and corresponding effect estimates. (a) Combinations of climatic variables included in each model. The ordinate shows six climatic difference variables: difference in mean annual temperature (Δmat), difference in minimum temperature of coldest month (Δmtcm), difference in maximum temperature of warmest month (Δmtwm), difference in annual precipitation (Δmap), difference in precipitation of driest month (Δpdm), difference in precipitation of wettest month (Δpwm). The abscissa shows all non-empty combinations of these variables. Gray dots indicate variable inclusion; vertical dotted lines mark models with the highest empirical support (ΔAIC ≤ 3), with variables in these models highlighted by large black dots. The numbers and alternating gray/white bands at the top serve as visual separators for model groups. All models included growth form as an explanatory variable in the analysis. (b) Effect estimates from the best-supported models (ΔAIC ≤ 3). Point estimates (red for temperature, blue down for precipitation, black diamonds for growth form) and 95% confidence intervals are shown for nine top-ranked models (Model IDs 130-152). Coefficients are log (hazard ratio); statistical significance at P = 0.05 is indicated when 95% confidence intervals do not overlap 0 (gray horizontal line). For climatic variables, regression coefficients > 0 indicate increased mortality risk per unit increase in climatic difference; for growth form, regression coefficients < 0 indicate lower mortality risk for herbal than for woody plants.

图5 基于具有最高经验支持的模型之一(ΔAIC = 0; 图3中的模型13, 图4中的模型139)解释气候变量对引种植物存活曲线的预测影响。该曲线将存活时间(横坐标)与植物在XTBG的存活概率(纵坐标)相关联。每幅图中, 标记为“XTBG”的曲线描述了物种分布地气候中位数与XTBG气候一致时的存活概率(即气候差异为0时的基线预测); 其余曲线分别对应该气候变量4个代表性取值下的存活状况: 该变量的最大观测差异值以及0与最大值之间3个等间距水平。存活曲线基于Nelson-Altschuler-Aalen风险估计器计算。图(a-b)显示年均温中位数差值(ΔmatM)的影响; 图(c-d)显示最冷月最低温中位数差值(ΔmtcmM)的影响; 图(e-f)显示最湿月降水量中位数差值(ΔpwmM)的影响。

Fig. 5 Effects of varying climatic values on predicted introduced plant survival, according to one of the models with highest empirical support (ΔAIC = 0; Model 13 in Fig. 3, Model 139 in Fig. 4). The curves relate survival time (abscissa) to probability of plant survival at XTBG (ordinate). In each panel, the curve labeled “XTBG” describes the predicted survival when the median climate of the species distribution is identical to the climate of XTBG (i.e., the baseline prediction when climatic difference equals zero); other curves correspond to survival conditions at four representative values: the maximum observed difference, and three equally spaced intermediate levels between zero and the maximum. Survival curves are based on the Nelson-Altschuler-Aalen estimator of the hazards. Panels (a-b) show the effect of differences in median mean annual temperature (ΔmatM); panels (c-d) show the effect of differences in median minimum temperature of coldest month (ΔmtcmM); while panels (e-f) show the effect of differences in median precipitation of wettest month (ΔpwmM).

图6 土壤差异与生长型对引种植物存活影响的混合效应Cox比例风险模型比较。60个模型的经验支持度通过赤池信息准则差值(ΔAIC)衡量。模型通过两种方式量化了XTBG与植物物种分布地之间的土壤差异: XTBG与繁殖体采集地土壤条件的差异(标记为“种源地土壤”)和XTBG与物种所有分布点土壤中位数的差异(标记为“分布地土壤中位数”)。此外, 模型通过物种独立随机效应(标记为“物种随机效应”)或基于系统发育关系的相关随机效应(标记为“系统发育关系随机效应”)来考虑数据的非独立性。横坐标表示候选模型编号(1-15), 纵坐标表示ΔAIC值。不同颜色和形状的符号代表不同土壤差异量化方法与随机效应类型的组合: 蓝色圆形表示种源地土壤与物种独立随机效应, 红色圆形表示分布地土壤中位数与物种独立随机效应; 蓝色三角形表示种源地土壤与系统发育关系随机效应, 红色三角形表示分布地土壤中位数与系统发育关系随机效应。图中的灰色虚线均表示ΔAIC = 3, 用于界定经验支持度最高的模型(ΔAIC ≤ 3)。图(b)为图(a)纵坐标下半部分(0-10)的放大视图, 突出展示具有最高经验支持度(ΔAIC ≤ 3)的模型。所有模型均将生长型作为解释变量纳入分析。

Fig. 6 Comparison of empirical support for mixed-effects Cox proportional hazards models relating introduced plant survival to soil differences and growth form. Empirical support for 60 models was measured as differences in the Akaike information criterion (ΔAIC). Models quantified soil differences between XTBG and species’ wild distributions in two ways: difference from soil at provenances (labeled “Provenance soil”) and difference from the median soil across occurrence records of a species (labeled “Median soil across occurrence records”). Additionally, models accounted for evolutionary relationships among plantings using uncorrelated species random effects (labeled “species random effect”) or phylogenetically correlated species random effects (labeled “phylogenetic random effect”). The abscissa indicates candidate model numbers (1-15), and the ordinate indicates ΔAIC values. Blue circles represent provenance soil with uncorrelated species random effects (s), and red circles represent the median soil across occurrence records with uncorrelated species random effects (s); blue triangles represent provenance soil with phylogenetic random effect, and red triangles represent the median soil across occurrence records with phylogenetic random effect. Gray dashed lines in both panel (a) and panel (b) indicate ΔAIC = 3, serving as the threshold to identify models with highest empirical support (ΔAIC ≤ 3). Panel (b) Magnified view the lower part of the ordinate in panel (a) (0-10), highlighting the models with highest empirical support (ΔAIC ≤ 3). Growth form was included as an explanatory variable in all models.

图7 土壤差异与生长型对引种植物存活影响的混合效应Cox比例风险模型比较与效应估计。(a)各模型包含的土壤变量组合。纵坐标为4个土壤差异变量: 表层沙含量差异(ΔT_SAND)、表层有机碳含量差异(ΔT_OC)、表层碎石体积百分比差异(ΔT_GRAVEL)和底层土壤容重差异(ΔS_BULK_DENSITY); 横坐标为15个备选模型(编号1-15), 代表上述变量的所有可能组合。圆点表示变量纳入情况, 黑色大圆点标识具有最强经验支持度的模型(ΔAIC ≤ 3, 垂直虚线所示)中包含的变量。图(a)顶部的数字表示各模型纳入的土壤变量数量(1-4个)。所有模型均将生长型作为解释变量纳入。图(b)表示最高经验支持模型(ΔAIC ≤ 3)中各解释变量的效应估计。横坐标为模型编号(14、8、15、11), 纵坐标为对数风险比。图中以点估计值和95%置信区间(垂线)展示各变量的回归系数。当95%置信区间不与0线(灰色水平线)重叠时, 效应具有统计学意义(P ≤ 0.05)。土壤变量回归系数 > 0表示该土壤差异每增加1个单位, 植株死亡风险增加; 生长型回归系数 > 0表示草本植物的死亡风险高于木本植物。

Fig. 7 Comparison of mixed-effects Cox proportional hazards models for introduced plant survival in relation to soil differences and growth form, with effect estimates. (a) Combinations of soil variables included in each model. The ordinate (y-axis) displays four soil difference variables: difference in topsoil sand content (ΔT_SAND), difference in topsoil organic carbon content (ΔT_OC), difference in topsoil gravel content (ΔT_GRAVEL), and difference in subsoil bulk density (ΔS_BULK_DENSITY); the abscissa (x-axis) shows 15 candidate models (numbered 1-15) representing all possible combinations of these variables. Dots indicate variable inclusion; large black dots mark variables present in models with the highest empirical support (ΔAIC ≤ 3, indicated by vertical dotted lines). Numbers at the top of panel (a) denote the number of soil variables included in each model (1-4). All models included growth form as an explanatory variable. (b) Effect estimates from the highest empirical support models (ΔAIC ≤ 3). The abscissa (x-axis) indicates model numbers (14, 8, 15, 11), and the ordinate (y-axis) represents the natural logarithm of the hazard ratio (log hazard ratio). Point estimates and 95% confidence intervals (vertical lines) show regression coefficients. Statistical significance (P ≤ 0.05) is indicated when confidence intervals do not overlap zero (gray horizontal line). regression coefficients > 0 for soil variables indicate increased mortality risk per unit increase in soil difference; for growth form, regression coefficients > 0 indicate higher mortality risk for herbal compared to woody plants.

图8 基于具有最高经验支持度的土壤模型(图6中模型14)预测土壤变量对引种植物存活曲线的影响。图中显示存活概率(纵坐标)随存活天数(横坐标)的变化关系。左列为木本植物预测结果, 右列为草本植物预测结果; (a-b)展示表层有机碳含量差异(ΔT_OCL)的效应, (c-d)展示表层碎石体积百分比差异(ΔT_GRAVELL)的效应。每个小图中, 标注为“XTBG”的曲线代表种源地与版纳植物园土壤条件无差异(差异值 = 0)的参照情景; 其余4条曲线对应该土壤变量在0至最大观测差异范围内的4个等间距梯度水平(含最大值)。生存曲线基于Nelson-Aalen累积风险估计得出。

Fig. 8 Predicted effects of soil variable differences on introduced plant survival curves based on the model with the highest empirical support (Model 14 in Fig. 6). The panels display predicted survival probability (ordinate) over time since survival time (abscissa). The left column presents predictions for woody plants and the right column for herbal plants; the upper row (a-b) shows the effect of topsoil organic carbon content difference (ΔT_OCL), and the lower row (c-d) shows the effect of topsoil gravel volume percentage difference (ΔT_GRAVELL). In each panel, the curve labeled “XTBG” represents the reference scenario with zero soil difference from XTBG; the other four curves correspond to four equally-spaced gradient levels of that soil variable across the range from 0 to the maximum observed difference (including the maximum). Survival curves were derived from Nelson-Aalen cumulative hazard estimates.