Ecological restoration has become an important technique for mitigating the human impacts on natural vegetation. Planting seedlings is the most common approach to regain lost forest cover. However, these activities require a large economic investment. Direct seeding is considered a cheaper and easier alternative technique, in which tree seeds are introduced directly on the site rather than transplanting seedlings from nurseries. To evaluate the effectiveness of direct seeding, we conducted a comprehensive search of the literature using 'restoration', 'direct seeding' and 'sowing' as keywords, and we performed a meta-analysis using 30 papers and 89 species. We used two different measures of restoration success: seed germination probability and success probability (the chance that a seed germinates and survives until the end of the experiment). In general, restoration attempts using direct-seeding techniques were relatively unsuccessful. On average, seed germination and success probability were 0·239 and 0·114, respectively, and were not affected by climate, species successional group or the application of pre-germinative treatments. Germination and success probability increased with seed size, and the use of physical protections resulted in a nearly twofold increase in germination probability, but this effect faded by the end of the experiments. Because of the low rate of seedling success, we suggest the use of direct seeding as a complementary technique to reduce restoration costs, particularly for species with large seeds and known high germination rates, but our results do not support direct seeding as a substitute for seedling planting.

Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R 2 = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information.

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Summary Integral projection models (IPMs) allow us to describe quantitatively the dynamics of a population structured by a continuous variable. They rely on information gathered at the individual level by recording survival, reproduction and changes in some structuring variable over time. This requires the ability to track individuals over the course of their entire life cycle. When this is not feasible, we would like to use alternative information to infer a population's dynamics. Time series of population‐level data are an option. An inverse modelling approach allows inferring the vital rates of a population when only population‐level data, in the form of a time series of the size of a population and the distribution of its individuals along a structuring variable, are available. The approach also allows incorporating estimates obtained through individual‐level data. Here, we explore how inverse modelling performs with simulated data and a relatively simple demographic model. We explore scenarios of data availability in terms of time‐series length, per‐year sample size and availability of independent vital‐rate estimates. We also test model performance in a real system using a 15‐year long data set from a chamaephyte plant, Cryptantha flava. We show that an inverse model can provide accurate reconstructions of the vital rates in a scenario where no individual‐level information is available. Better results can be obtained if independent estimates on any vital rate are provided, as was the case for C. flava where high interannual variation is present. Parameter estimation becomes more difficult with shorter time series, but per‐year sample size can be greatly reduced without significantly affecting parameter accuracy. Inverse modelling of IPMs allow for the estimation of unobserved vital rates, which is important for systems where any or all of the vital rates are hard to quantify. It also helps to determine whether a forward IPM is capturing the population dynamics: if the inverse version produces incorrect reconstructions of the vital rates, the forward IPM can be considered as inadequately describing the system.

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