Data from: Which landscape size best predicts the influence of forest cover on restoration success? – A global meta-analysis on the scale of effect

Landscape context is a strong predictor of species persistence, abundance and distribution, yet its influence on the success of ecological restoration remains unclear. Thus, a primary question arises: which landscape size best predicts the effects of forest cover on restoration success? To answer this question, we conducted a global meta-analysis for biodiversity (mammals, birds, invertebrates, herpetofauna and plants) and measures of vegetation structure (cover, density, height, biomass and litter). Response ratios were calculated for comparisons between reference (e.g. old-growth forest) and disturbed sites (degraded or restored). Using an information-theoretic approach, mean response ratio (restoration success) and response ratio variance (restoration predictability) within each study landscape were regressed against the percentage of overall (summed forest cover) and contiguous (summed pixels of ≥60% forest cover) forest within eight different buffer sizes of radius 5–200 km (at 1-km resolution). We included 247 studies encompassing 196 study landscapes and 4360 quantitative comparisons. The best buffer (landscape) size varied for the following: (i) overall and contiguous forest cover, (ii) biodiversity and vegetation structure and (iii) mean response ratio and response ratio variance. Only plant biodiversity was influenced by overall forest cover (buffer size of 5, 10 and 200 km radii), while plants (10 and 200 km radii), mammals (5, 10 and 50–200 km radii), invertebrates (5 and 10 km radii), cover (5 km radii), height (5 km radii) and litter (100 km radii) were influenced by contiguous forest cover. Overall, mean response ratio and response ratio variance were positively and negatively nonlinearly related with both overall and contiguous forest cover, respectively. We reveal for the first time a clear pattern of increasing restoration success and decreasing uncertainty as contiguous forest cover increases. We also indicate preliminary recommended buffer sizes for investigating landscape restoration effects on biodiversity and vegetation structure. However, the coarse grain and variability in the data mean the optimal landscape size may not have been detected; thus, further research is needed. Synthesis and applications. When setting targets for ecological restoration, policymakers and restoration practitioners should account for the following: (i) the landscape context, particularly the amount of contiguous habitat up to 10 km around a disturbed site, and (ii) the uncertainty in restoration success, as it increases when contiguous forest cover falls below about 50%.

景观背景（landscape context）是物种存续、多度与分布的强预测因子，但其对生态修复成效的影响仍未明确。据此提出核心研究问题：何种尺度的景观最能预测森林覆盖对修复成效的影响？
为解答该问题，我们针对生物多样性（哺乳类、鸟类、无脊椎动物、爬行动物与两栖类（herpetofauna）以及植物）以及植被结构指标（盖度、密度、高度、生物量与枯落物（litter））开展了一项全球荟萃分析（meta-analysis）。我们以参照样地（如原始老龄林（old-growth forest））与受干扰样地（退化或修复样地）为对照，计算了响应比（response ratio）。
我们采用信息论方法（information-theoretic approach），将每个研究景观内的平均响应比（即修复成效）与响应比方差（即修复可预测性），分别与8种不同半径（5~200 km，分辨率为1 km）缓冲区（buffer size）范围内的总森林覆盖占比（即总森林面积占比）以及连片森林覆盖占比（即森林覆盖≥60%的像素总和占比）进行回归分析。本研究共纳入247项研究，涵盖196个研究景观与4360组定量对照。
最优缓冲区尺度因以下三类因素存在差异：(i) 总森林覆盖与连片森林覆盖；(ii) 生物多样性与植被结构；(iii) 平均响应比与响应比方差。仅植物生物多样性受总森林覆盖的影响（对应缓冲区半径为5、10与200 km）；而植物（缓冲区半径10与200 km）、哺乳类（5、10与50~200 km）、无脊椎动物（5与10 km）、植被盖度（5 km）、植被高度（5 km）以及枯落物（100 km）均受连片森林覆盖的影响。
总体而言，平均响应比与总森林覆盖、连片森林覆盖分别呈正非线性相关，而响应比方差则分别呈负非线性相关。本研究首次揭示了清晰的规律：随着连片森林覆盖度提升，修复成效逐步提高，而修复结果的不确定性随之降低。本研究同时为探究景观背景对生物多样性与植被结构的修复效应，提供了初步推荐的缓冲区尺度。
但受限于数据的粗分辨率与变异性，本研究可能未检测到最优景观尺度，因此仍需开展进一步研究。
综合与应用：在制定生态修复目标时，政策制定者与修复从业者应考虑以下两点：(i) 景观背景，尤其是受干扰样地周边10 km范围内的连片栖息地面积；(ii) 修复成效的不确定性——当连片森林覆盖度低于约50%时，该不确定性会显著升高。