Assessing the Economic Resilience of Different Management Systems to Severe Forest Disturbance.

Given the drastic changes in the environment, resilience is a key focus of ecosystem management. Yet, the quantification of the different dimensions of resilience remains challenging, particularly for long-lived systems such as forests. Here we present an analytical framework to study the economic resilience of different forest management systems, focusing on the rate of economic recovery after severe disturbance. Our framework quantifies the post-disturbance gain in the present value of a forest relative to a benchmark system as an indicator of economic resilience. Forest values and silvicultural interventions were determined endogenously from an optimization model and account for risks affecting tree survival. We consider the effects of differences in forest structure and tree growth post disturbance on economic resilience. We demonstrate our approach by comparing the economic resilience of continuous cover forestry against a clear fell system for typical conditions in Central Europe. Continuous cover forestry had both higher economic return and higher economic resilience than the clear fell system. The economic recovery from disturbance in the continuous cover system was between 18.2 and 51.5% faster than in the clear fell system, resulting in present value gains of between 1733 and 4535 € ha-1. The advantage of the continuous cover system increased with discount rate and stand age, and was driven by differences in both stand structure and economic return. We conclude that continuous cover systems can help to address the economic impacts of increasing disturbances in forest management.

Which Socio-economic Conditions Drive the Selection of Agroforestry at the Forest Frontier?

Models are essential to assess the socio-economic credentials of new agroforestry systems. In this study, we showcase robust optimisation as a tool to evaluate agroforestry's potential to meet farmers' multiple goals. Our modelling approach has three parts. First, we use a discrete land-use model to evaluate two agroforestry systems (alley cropping and silvopasture) and conventional land uses against five socio-economic objectives, focusing on the forest frontier in eastern Panama. Next, we couple the land-use model with robust optimisation, to determine the mix of land uses (farm portfolio) that minimises trade-offs between the five objectives. Here we consider uncertainty to simulate the land-use decisions of a risk-averse farmer. Finally, we assess how the type and amount of agroforestry included in the optimal land-use portfolio changes under different environmental, socio-economic and political scenarios, to explore the conditions that may make agroforestry more attractive for farmers. We identify silvopasture as a promising land use for meeting farmers' goals, especially for farms with less productive soils. The additional labour demand compared to conventional pasture, however, may prove an important barrier to adoption for farms facing acute labour shortages. The selection of agroforestry responded strongly to changes in investment costs and timber prices, suggesting that cost-sharing arrangements and tax incentives could be effective strategies to enhance adoption. We found alley cropping to be less compatible with farmers' risk aversion, but this agroforestry system may still be a desirable complement to the land-use portfolio, especially for farmers who are more profit-oriented and tolerant of risk.

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.

A goal programming approach to evaluate agroforestry systems in Eastern Panama.

Agroforestry is hypothesised to increase ecological and economic functions of farms. Yet it is unclear if and how much agroforestry should be embedded in diversified farming systems to satisfy farmers' needs while potentially enhancing environmental services. To address this research gap we use a mathematical programming model to investigate the role of different agroforestry systems in hypothetical farm portfolios that reduce trade-offs between farmers' goals. Our approach is innovative because it simultaneously considers multiple objectives and the effect of land-use diversification within a farm, is based on knowledge and perceptions of local farmers, and accounts for heterogeneity in farmer judgement. We test the model in a forest frontier region in Eastern Panama, using data from farmer interviews. Farmers evaluated conventional land uses and two agroforestry systems (silvopasture and alley cropping) against 10 pre-defined socio-economic and ecological objectives. First we determined the optimal farm land-use composition that reduces trade-offs between the 10 objectives. The model selects the mix of land uses that secures the best worst-case performance across all objectives, when considering uncertainty in the ability of each land use to achieve each objective (which we quantify by the variability in farmer opinion). Agroforestry dominates the optimised farm portfolio, which comprises 60% silvopasture, 39% forest and 1% plantation. This land-use portfolio, however, deviates strongly from the current land use of farmers, which is 59% pasture, 26% crops, 14% forest and 1% plantation. In a second step we explore the implicit objectives driving farmers' current land-use decisions. We find that immediate-term needs related to food security and liquidity best explain farmers' current land-use portfolio; optimising for these objectives produces a land-use portfolio comprising 60% pasture and 40% crops, which is similar to the current land use. This suggests that increasing agroforestry adoption in the study area will require systems that provide early and frequent returns and allow for ongoing crop production, to better satisfy farmers' cash flow and household consumption needs.

Optimizing forest landscape composition for multiple ecosystem services based on uncertain stakeholder preferences.

Determining the desirable composition of a forested landscape and its associated ecosystem services (ES) is challenging because the solutions must reconcile the preferences of various forest stakeholders and account for uncertain data. By combining multi-objective robust optimization with an online survey of forest professionals in Slovenia (n = 130) and forest professionals, forest scientists, nature conservationists and forest owners in Germany (n = 649) about optimal forest landscape composition, we derived compromise portfolios of forest types. These portfolios minimize the trade-offs between five ES (stopping avalanches, carbon storage, recreation, timber production and regulating flows of water), and account for the varying capacity of eight forest types to supply ES. The resulting optimized forest landscape compositions always comprised at least two forest types. In both countries, uneven-aged native deciduous and conifer mixed stands were prominent in the optimized portfolios. In Germany, however, the optimized portfolio also contained exotic species in mixtures, whereas forest stands without active management were notable for several ES in Slovenia. Unmanaged forest stands were also selected in the forest composition optimized for nature conservationists in Germany: the nature conservationists' portfolio diverged strongly from those of the other stakeholders. Our results illustrate that diversified forested landscapes provide multiple ES, but also secure the provision of a single ES when accounting for uncertainty. The optimal forest compositions obtained by multi-objective robust optimization are a starting point for participatory planning approaches to identify the most socially acceptable strategies for adapting forest management to an uncertain future.

Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors.

At a proximal level, the physiological impacts of global climate change on ectothermic organisms are manifest as changes in body temperatures. Especially for plants and animals exposed to direct solar radiation, body temperatures can be substantially different from air temperatures. We deployed biomimetic sensors that approximate the thermal characteristics of intertidal mussels at 71 sites worldwide, from 1998-present. Loggers recorded temperatures at 10-30 min intervals nearly continuously at multiple intertidal elevations. Comparisons against direct measurements of mussel tissue temperature indicated errors of ~2.0-2.5 °C, during daily fluctuations that often exceeded 15°-20 °C. Geographic patterns in thermal stress based on biomimetic logger measurements were generally far more complex than anticipated based only on 'habitat-level' measurements of air or sea surface temperature. This unique data set provides an opportunity to link physiological measurements with spatially- and temporally-explicit field observations of body temperature.

Genetic variability and phylogeography of the invasive zebra mussel, Dreissena polymorpha (Pallas).

The zebra mussel, Dreissena polymorpha (Pallas), a bivalve species originally native to the Black and Caspian seas, has invaded Ireland in the last decade. Five microsatellite loci were used to investigate genetic diversity and population structure in 10 populations across Europe (Ireland, UK, the Netherlands and Romania) and the Great Lakes (Lake Ontario and Lake St Clair). Levels of allelic diversity and mean expected heterozygosity were high for all populations (mean number of alleles/locus and H(E) were 10-15.2 and 0.79-0.89, respectively). High levels of polymorphism observed in Irish populations suggest that the Irish founder population(s) were large and/or several introductions took place after foundation. Significant deficits of heterozygotes were recorded for all populations, and null alleles were the most probable factor contributing to these deficits. Pairwise comparisons using Fisher exact tests and F(ST) values revealed little genetic differentiation between Irish populations. The UK sample was not significantly differentiated from the Irish samples, most probably reflecting an English origin for Irish zebra mussels. No significant differentiation was detected between the two Great Lakes populations. Our data support a northwest rather than a central or east European source for North American zebra mussels.