When does seed limitation matter for scaling up reforestation from patches to landscapes?

Feedback loops drive ecological succession: towards a unified conceptual framework

The core principle shared by most theories and models of succession is that, following a major disturbance, plant-environment feedback dynamics drive a directional change in the plant community. The most commonly studied feedback loops are those in which the regrowth of the plant community causes changes to the abiotic (e.g. soil nutrients) or biotic (e.g. dispersers) environment, which differentially affect species availability or performance. This, in turn, leads to shifts in the species composition of the plant community. However, there are many other PE feedback loops that potentially drive succession, each of which can be considered a model of succession. While plant-environment feedback loops in principle generate predictable successional trajectories, succession is generally observed to be highly variable. Factors contributing to this variability are the stochastic processes involved in feedback dynamics, such as individual mortality and seed dispersal, and extrinsic causes of succession, which are not affected by changes in the plant community but do affect species performance or availability. Both can lead to variation in the identity of dominant species within communities. This, in turn, leads to further contingencies if these species differ in their effect on their environment (priority effects). Predictability and variability are thus intrinsically linked features of ecological succession. We present a new conceptual framework of ecological succession that integrates the propositions discussed above. This framework defines seven general causes: landscape context, disturbance and land-use, biotic factors, abiotic factors, species availability, species performance, and the plant community. When involved in a feedback loop, these general causes drive succession and when not, they are extrinsic causes that create variability in successional trajectories and dynamics. The proposed framework provides a guide for linking these general causes into causal pathways that represent specific models of succession. Our framework represents a systematic approach to identifying the main feedback processes and causes of variation at different successional stages. It can be used for systematic comparisons among study sites and along environmental gradients, to conceptualise studies, and to guide the formulation of research questions and design of field studies. Mapping an extensive field study onto our conceptual framework revealed that the pathways representing the study's empirical outcomes and conceptual model had important differences, underlining the need to move beyond the conceptual models that currently dominate in specific fields and to find ways to examine the importance of and interactions among alternative causal pathways of succession. To further this aim, we argue for integrating long-term studies across environmental and anthropogenic gradients, combined with controlled experiments and dynamic modelling.

Introducing desirable patches to initiate ecosystem transitions and accelerate ecosystem restoration

Meeting restoration targets may require active strategies to accelerate natural regeneration rates or overcome the resilience associated with degraded ecosystem states. Introducing desired ecosystem patches in degraded landscapes constitutes a promising active restoration strategy, with various mechanisms potentially causing these patches to become foci from which desired species can re-establish throughout the landscape. This study considers three mechanisms previously identified as potential drivers of introduced patch dynamics: autocatalytic nucleation, directed dispersal, and resource concentration. These mechanisms reflect qualitatively different positive feedbacks. We developed an ecological model framework that compared how the occurrence of each mechanism was reflected in spatio-temporal patch dynamics. We then analyzed the implications of these relationships for optimal restoration design. We found that patch expansion accelerated over time when driven by the autocatalytic nucleation mechanism, while patch expansion driven by the directed dispersal or resource concentration mechanisms decelerated over time. Additionally, when driven by autocatalytic nucleation, patch expansion was independent of patch position in the landscape. However, the proximity of other patches affected patch expansion either positively or negatively when driven by directed dispersal or resource concentration. For autocatalytic nucleation, introducing many small patches was a favorable strategy, provided that each individual patch exceeded a critical patch size. Introducing a single patch or a few large patches was the most effective restoration strategy to initiate the directed dispersal mechanism. Introducing many small patches was the most effective strategy for reaching restored ecosystem states driven by a resource concentration mechanism. Our model results suggest that introducing desirable patches can substantially accelerate ecosystem restoration, or even induce a critical transition from an otherwise stable degraded state toward a desired ecosystem state. However, the potential of this type of restoration strategy for a particular ecosystem may strongly depend on the mechanism driving patch dynamics. In turn, which mechanism drives patch dynamics may affect the optimal spatial design of an active restoration strategy. Each of the three mechanisms considered reflects distinct spatio-temporal patch dynamics, providing novel opportunities for empirically identifying key mechanisms, and restoration designs that introduce desired patches in degraded landscapes according to these patch dynamics.

The framework species method: harnessing natural regeneration to restore tropical forest ecosystems

As interest in restoring tropical forests surges, so does the need for effective methods to ensure success. The framework species method (FSM) restores forest ecosystems by densely planting open sites, close to natural forest, with woody species, indigenous to the reference ecosystem and selected for their ability to accelerate ecological succession. Criteria for selecting framework species include: (i) representative of the reference forest ecosystem, (ii) tolerant of open conditions, (iii) ability to suppress weeds, (iv) attractiveness to seed-dispersing animals and (v) easily propagated. The method is effective where forest remnants and viable populations of seed dispersers remain. The origins and elements of the FSM are discussed. We review its adoption in 12 countries. Adherence to original principles was mostly high, but some misuse of the term was evident. The need for clearer definitions was identified. We place the FSM on a scale of restoration methods, matched with degradation levels and compare its establishment costs with those of other methods. Obstacles to its wider adoption, both technical and socio-economic, are discussed, along with how these might be overcome. Finally, the FSM is more clearly defined to facilitate its use in contributing towards the goals of the UN Decade on Restoration. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.

Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m^-2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a 'latent' variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95-250 seeds per m² that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.

Uniting niche differentiation and dispersal limitation predicts tropical forest succession

Tropical secondary forests are increasingly important for carbon sequestration and biodiversity conservation worldwide; yet, we still cannot accurately predict community turnover during secondary succession. We propose that integrating niche differentiation and dispersal limitation will generate an improved theoretical explanation of tropical forest succession. The interaction between seed sources and dispersers regulates seed movement throughout succession, and recent technological advances in animal tracking and molecular analyses enable us to accurately monitor seed movement as never before. We propose a framework to bridge the gap between niche differentiation and dispersal limitation. The Source-Disperser Limitation Framework (SDLF) provides a way to better predict secondary tropical forest succession across gradients of landscape disturbance by integrating seed sources and frugivore behavior.

Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state-space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat-derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra-annual phenological variation to a model based on Landsat-derived normalized difference vegetation index (NDVI). The best-performing model accounted for inter- and intra-annual noise in spectral reflectance and translated NDVI to canopy height via Landsat-lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state-space models to quantify ecological dynamics from time series of space-borne imagery. State-space models also provide a statistical approach well-suited to fusing high-resolution data, such as airborne lidar, with lower-resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon

Secondary forests are increasing in the Brazilian Amazon and have been cited as an important mechanism for reducing net carbon emissions. However, our understanding of the contribution of secondary forests to the Amazonian carbon balance is incomplete, and it is unclear to what extent emissions from old-growth deforestation have been offset by secondary forest growth. Using MapBiomas 3.1 and recently refined IPCC carbon sequestration estimates, we mapped the age and extent of secondary forests in the Brazilian Amazon and estimated their role in offsetting old-growth deforestation emissions since 1985. We also assessed whether secondary forests in the Brazilian Amazon are growing in conditions favourable for carbon accumulation in relation to a suite of climatic, landscape and local factors. In 2017, the 129,361 km² of secondary forest in the Brazilian Amazon stored 0.33 ± 0.05 billion Mg of above-ground carbon but had offset just 9.37% of old-growth emissions since 1985. However, we find that the majority of Brazilian secondary forests are situated in contexts that are less favourable for carbon accumulation than the biome average. Our results demonstrate that old-growth forest loss remains the most important factor determining the carbon balance in the Brazilian Amazon. Understanding the implications of these findings will be essential for improving estimates of secondary forest carbon sequestration potential. More accurate quantification of secondary forest carbon stocks will support the production of appropriate management proposals that can efficiently harness the potential of secondary forests as a low-cost, nature-based tool for mitigating climate change.

Integrating anthropogenic factors into regional-scale species distribution models-A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional-scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local-scale anthropogenic variables, including wildfire history, land-use change, invasive species, and ecological restoration practices can override regional-scale variables to drive patterns of species distribution. Incorporating these human-induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human-induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field-sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: -6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: -47.9%, -41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land-use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.

Plant Attributes that Drive Dispersal and Establishment Limitation in Tropical Agricultural Landscapes

Factors that influence tropical-forest regeneration have been of interest across the tropics. We tested the degree of dispersal and establishment limitation of pioneer and non-pioneer tree species with different dispersal modes and seed sizes, using data on both seed fall and seedling establishment in primary forest, secondary forest, and pasture excluded from livestock. The study took place in a lowland tropical rain forest in southeastern Mexico. To calculate dispersal and establishment limitation, we used a density-weighted index that considers: (1) whether a seed or seedling of a given species has arrived in the sample area; and (2) the fraction of seeds or seedlings contributed by a given species relative to the total number of seeds or seedlings arriving at a sampling station. Dispersal limitation of non-pioneer species and animal-dispersed species decreased with succession. The secondary forest had less dispersal limitation for wind-dispersed pioneers than pasture, resulting in a dense aggregation of species with seeds dispersed by wind. Overall, establishment limitation differed between animal-dispersed and wind-dispersed species in the primary forest, and was negatively correlated with seed size. The low capacity of most species to arrive, germinate, and establish as seedlings in pastures slows succession back to forest. To overcome barriers to natural succession in pastures, transplanting seedlings of non-pioneer species is suggested because most of them show high dispersal and establishment limitation.

Positive site selection bias in meta-analyses comparing natural regeneration to active forest restoration

Several recent meta-analyses have aimed to determine whether natural regeneration is more effective at recovering tropical forests than active restoration (for example, tree planting). We reviewed this literature and found that comparisons between strategies are biased by positive site selection. Studies of natural forest regeneration are generally conducted at sites where a secondary forest was already present, whereas tree planting studies are done in a broad range of site conditions, including non-forested sites that may not have regenerated in the absence of planting. Thus, a level of success in forest regeneration is guaranteed for many studies representing natural regeneration, but not for those representing active restoration. The complexity of optimizing forest restoration is best addressed by paired experimentation at the same site, replicated across landscapes. Studies that have taken this approach reach different conclusions than those arising from meta-analyses; the results of paired experimental comparisons emphasize that natural regeneration is a highly variable process and that active restoration and natural regeneration are complementary strategies.

A hyperspectral image can predict tropical tree growth rates in single-species stands

Remote sensing is increasingly needed to meet the critical demand for estimates of forest structure and composition at landscape to continental scales. Hyperspectral images can detect tree canopy properties, including species identity, leaf chemistry and disease. Tree growth rates are related to these measurable canopy properties but whether growth can be directly predicted from hyperspectral data remains unknown. We used a single hyperspectral image and light detection and ranging-derived elevation to predict growth rates for 20 tropical tree species planted in experimental plots. We asked whether a consistent relationship between spectral data and growth rates exists across all species and which spectral regions, associated with different canopy chemical and structural properties, are important for predicting growth rates. We found that a linear combination of narrowband indices and elevation is correlated with standardized growth rates across all 20 tree species (R²  = 53.70%). Although wavelengths from the entire visible-to-shortwave infrared spectrum were involved in our analysis, results point to relatively greater importance of visible and near-infrared regions for relating canopy reflectance to tree growth data. Overall, we demonstrate the potential for hyperspectral data to quantify tree demography over a much larger area than possible with field-based methods in forest inventory plots.