The transparency, reliability and utility of tropical rainforest land-use and land-cover change models

Conflicts and opportunities for commercial tree plantation expansion and biodiversity restoration across Brazil

Substantial global restoration commitments are occurring alongside a rapid expansion in land-hungry tropical commodities, including to supply increasing demand for wood products. Future commercial tree plantations may deliver high timber yields, shrinking the footprint of production forestry, but there is an as-yet unquantified risk that plantations may expand into priority restoration areas, with marked environmental costs. Focusing on Brazil-a country of exceptional restoration importance and one of the largest tropical timber producers-we use random forest models and information on the economic, social, and spatial drivers of historic commercial tree plantation expansion to estimate and map the probability of future monoculture tree plantation expansion between 2020 and 2030. We then evaluate potential plantation-restoration conflicts and opportunities at national and biome-scales and under different future production and restoration pathways. Our simulations show that of 2.8 Mha of future plantation expansion (equivalent to plantation expansion 2010-2020), ~78,000 ha (3%) is forecast to occur in the top 1% of restoration priority areas for terrestrial vertebrates, with ~547,500 ha (20%) and ~1,300,000 ha (46%) in the top 10% and 30% of priority areas, respectively. Just ~459,000 ha (16%) of expansion is forecast within low-restoration areas (bottom 30% restoration priorities), and the first 1 Mha of plantation expansion is likely to have disproportionate impacts, with potential restoration-plantation overlap starkest in the Atlantic Forest but prominent in the Pampas and Cerrado as well. Our findings suggest that robust, coherent land-use policies must be deployed to ensure that significant trade-offs between restoration and production objectives are navigated, and that commodity expansion does not undermine the most tractable conservation gains under emerging global restoration agendas. They also highlight the potentially significant role an engaged forestry sector could play in improving biodiversity outcomes in restoration projects in Brazil, and presumably elsewhere.

Integration of qualitative and quantitative methods for land-use-change modeling in a deforestation frontier

Development and implementation of effective protected area management to reduce deforestation depend in part on identifying factors contributing to forest loss and areas at risk of conversion, but standard land-use-change modeling may not fully capture contextual factors that are not easily quantified. To better understand deforestation and agricultural expansion in Amazonian protected areas, we combined quantitative land-use-change modeling with qualitative discourse analysis in a case study of Brazil's Jamanxim National Forest. We modeled land-use change from 2008 to 2018 and projected deforestation through 2028. We used variables identified in a review of studies that modeled land-use change in the Amazon (e.g., variables related to agricultural suitability and economic accessibility) and from a critical discourse analysis that examined documents produced by different actors (e.g., government agencies and conservation nonprofit organizations) at various spatial scales. As measured by analysis of variance, McFadden's adjusted pseudo R² , and quantity and allocation disagreement, we found that including variables in the model identified as important to deforestation dynamics through the qualitative discourse analysis (e.g., the proportion of unallocated public land, distance to proposed infrastructure developments, and density of recent fires) alongside more traditional variables (e.g., elevation, distance to roads, and protection status) improved the predictive ability of these models. Models that included discourse analysis variables and traditional variables explained up to 19.3% more of the observed variation in deforestation probability than a model that included only traditional variables and 4.1% more variation than a model with only discourse analysis variables. Our approach of integrating qualitative and quantitative methods in land-use-change modeling provides a framework for future interdisciplinary work in land-use change.

Representing responses to climate change in spatial land system models

Modelling future change to land use and land cover is done as part of many local and global scenario environmental assessments. Nevertheless, there are still considerable challenges related to simulating land-use responses to climate change. Mostly, climate change is considered by changing the temperature and precipitation, affecting the spatial distribution and productivity of future land use and land cover as result of differential changes in growing conditions. Other climate change effects, such as changes in the water resources needed to support future cropland expansion and intensification, are often neglected. In this study, we demonstrate how including different types of responses to climate change influences the simulation of future changes to land use and land cover, and land management. We study the influence of including different climate change effects in land system modeling step by step. The results show that land system models need to include numerous simultaneous climate change effects, particularly when looking at adaptation options such as implementing irrigation. Otherwise, there is a risk of biased impact estimates leading either to under- or overestimation of the consequences of land use change, including land degradation. Spatial land system models therefore need to be developed accounting for a multitude of climate change impacts, uncertainties related to climate data, and an assessment of the sensitivity of the outcomes toward the decisions of modellers on representing climate change impacts.

Accounting for multiple ecosystem services in a simulation of land-use decisions: Does it reduce tropical deforestation?

Conversion of tropical forests is among the primary causes of global environmental change. The loss of their important environmental services has prompted calls to integrate ecosystem services (ES) in addition to socio-economic objectives in decision-making. To test the effect of accounting for both ES and socio-economic objectives in land-use decisions, we develop a new dynamic approach to model deforestation scenarios for tropical mountain forests. We integrate multi-objective optimization of land allocation with an innovative approach to consider uncertainty spaces for each objective. These uncertainty spaces account for potential variability among decision-makers, who may have different expectations about the future. When optimizing only socio-economic objectives, the model continues the past trend in deforestation (1975-2015) in the projected land-use allocation (2015-2070). Based on indicators for biomass production, carbon storage, climate and water regulation, and soil quality, we show that considering multiple ES in addition to the socio-economic objectives has heterogeneous effects on land-use allocation. It saves some natural forest if the natural forest share is below 38%, and can stop deforestation once the natural forest share drops below 10%. For landscapes with high shares of forest (38%-80% in our study), accounting for multiple ES under high uncertainty of their indicators may, however, accelerate deforestation. For such multifunctional landscapes, two main effects prevail: (a) accelerated expansion of diversified non-natural areas to elevate the levels of the indicators and (b) increased landscape diversification to maintain multiple ES, reducing the proportion of natural forest. Only when accounting for vascular plant species richness as an explicit objective in the optimization, deforestation was consistently reduced. Aiming for multifunctional landscapes may therefore conflict with the aim of reducing deforestation, which we can quantify here for the first time. Our findings are relevant for identifying types of landscapes where this conflict may arise and to better align respective policies.

Improving the representation of adaptation in climate change impact models

Climate change adaptation is a complex human process, framed by uncertainties and constraints, which is difficult to capture in existing assessment models. Attempts to improve model representations are hampered by a shortage of systematic descriptions of adaptation processes and their relevance to models. This paper reviews the scientific literature to investigate conceptualisations and models of climate change adaptation, and the ways in which representation of adaptation in models can be improved. The review shows that real-world adaptive responses can be differentiated along a number of dimensions including intent or purpose, timescale, spatial scale, beneficiaries and providers, type of action, and sector. However, models of climate change consequences for land use and water management currently provide poor coverage of these dimensions, instead modelling adaptation in an artificial and subjective manner. While different modelling approaches do capture distinct aspects of the adaptive process, they have done so in relative isolation, without producing improved unified representations. Furthermore, adaptation is often assumed to be objective, effective and consistent through time, with only a minority of models taking account of the human decisions underpinning the choice of adaptation measures (14%), the triggers that motivate actions (38%) or the time-lags and constraints that may limit their uptake and effectiveness (14%). No models included adaptation to take advantage of beneficial opportunities of climate change. Based on these insights, transferable recommendations are made on directions for future model development that may enhance realism within models, while also advancing our understanding of the processes and effectiveness of adaptation to a changing climate.

Conservation performance of different conservation governance regimes in the Peruvian Amazon

State-controlled protected areas (PAs) have dominated conservation strategies globally, yet their performance relative to other governance regimes is rarely assessed comprehensively. Furthermore, performance indicators of forest PAs are typically restricted to deforestation, although the extent of forest degradation is greater. We address these shortfalls through an empirical impact evaluation of state PAs, Indigenous Territories (ITs), and civil society and private Conservation Concessions (CCs) on deforestation and degradation throughout the Peruvian Amazon. We integrated remote-sensing data with environmental and socio-economic datasets, and used propensity-score matching to assess: (i) how deforestation and degradation varied across governance regimes between 2006–2011; (ii) their proximate drivers; and (iii) whether state PAs, CCs and ITs avoided deforestation and degradation compared with logging and mining concessions, and the unprotected landscape. CCs, state PAs, and ITs all avoided deforestation and degradation compared to analogous areas in the unprotected landscape. CCs and ITs were on average more effective in this respect than state PAs, showing that local governance can be equally or more effective than centralized state regimes. However, there were no consistent differences between conservation governance regimes when matched to logging and mining concessions. Future impact assessments would therefore benefit from further disentangling governance regimes across unprotected land.

Increasing human dominance of tropical forests

Tropical forests house over half of Earth's biodiversity and are an important influence on the climate system. These forests are experiencing escalating human influence, altering their health and the provision of important ecosystem functions and services. Impacts started with hunting and millennia-old megafaunal extinctions (phase I), continuing via low-intensity shifting cultivation (phase II), to today's global integration, dominated by intensive permanent agriculture, industrial logging, and attendant fires and fragmentation (phase III). Such ongoing pressures, together with an intensification of global environmental change, may severely degrade forests in the future (phase IV, global simplification) unless new "development without destruction" pathways are established alongside climate change-resilient landscape designs.

Variable gene dispersal conditions and spatial deforestation patterns can interact to affect tropical tree conservation outcomes

Tropical lowland rain forest (TLRF) biodiversity is under threat from anthropogenic factors including deforestation which creates forest fragments of different sizes that can further undergo various internal patterns of logging. Such interventions can modify previous equilibrium abundance and spatial distribution patterns of offspring recruitment and/or pollen dispersal. Little is known about how these aspects of deforestation and fragmentation might synergistically affect TLRF tree recovery demographics and population genetics in newly formed forest fragments. To investigate these TLRF anthropogenic disturbance processes we used the computer program NEWGARDEN (NG), which models spatially-explicit, individual-based plant populations, to simulate 10% deforestation in six different spatial logging patterns for the plant functional type of a long-lived TLRF canopy tree species. Further, each logging pattern was analyzed under nine varying patterns of offspring versus pollen dispersal distances that could have arisen post-fragmentation. Results indicated that gene dispersal condition (especially via offspring) had a greater effect on population growth and genetic diversity retention (explaining 98.5% and 88.8% of the variance respectively) than spatial logging pattern (0.2% and 4.7% respectively), with ‘Near’ distance dispersal maximizing population growth and genetic diversity relative to distant dispersal. Within logged regions of the fragment, deforestation patterns closer to fragment borders more often exhibited lower population recovery rates and founding genetic diversity retention relative to more centrally located logging. These results suggest newly isolated fragments have populations that are more sensitive to the way in which their offspring and pollen dispersers are affected than the spatial pattern in which subsequent logging occurs, and that large variation in the recovery rates of different TLRF tree species attributable to altered gene dispersal regimens will be a likely outcome of fragmentation. Conservation implications include possible manual interventions (manual manipulations of offspring dispersers and/or pollinators) in forest fragments to increase population recovery and genetic diversity retention.

Modelling land cover change in the Brazilian Amazon: temporal changes in drivers and calibration issues

Land cover change (LCC) models are used in many studies of human impacts on the environment, but knowing how well these models predict observed changes in the landscape is a challenge. We used nearly three decades of LCC maps to run several LCC simulations to: (1) determine which parameters associated with drivers of LCC (e.g. roads) get selected for which transition (forest to deforested, regeneration to deforested or deforested to regeneration); (2) investigate how the parameter values vary through time with respect to the different activities (e.g. farming); and (3) quantify the influence of choosing a particular time period for model calibration and validation on the performance of LCC models. We found that deforestation of primary forests tends to occur along roads (included in 95 % of models) and outside protected areas (included in all models), reflecting farming establishment. Regeneration tends to occur far from roads (included in 78 % of the models) and inside protected areas (included in 38 % of the models), reflecting the processes of land abandonment. Our temporal analysis of model parameters revealed a degree of variation through time (e.g. effectiveness of protected areas rose by 73 %, p < 0.001), but for the majority of parameters there was no significant trend. The degree to which model predictions agreed with observed change was heavily dependent on the year used for calibration (p < 0.001). The next generation of LCC models may need to embed trends in parameter values to allow the processes determining LCC to change through time and exert their influence on model predictions.