Alternative stable states and the sustainability of forests, grasslands, and agriculture

Abstract

Endangered forest-grassland mosaics interspersed with expanding agriculture and silviculture occur across many parts of the world, including the southern Brazilian highlands. This natural mosaic ecosystem is thought to reflect alternative stable states driven by threshold responses of recruitment to fire and moisture regimes. The role of adaptive human behavior in such systems remains understudied, despite its pervasiveness and the fact that such ecosystems can exhibit complex dynamics. We develop a nonlinear mathematical model of coupled human-environment dynamics in mosaic systems and social processes regarding conservation and economic land valuation. Our objective is to better understand how the coupled dynamics respond to changes in ecological and social conditions. The model is parameterized with southern Brazilian data on mosaic ecology, land-use profits, and questionnaire results concerning landowner preferences and conservation values. We find that the mosaic presently resides at a crucial juncture where relatively small changes in social conditions can generate a wide variety of possible outcomes, including complete loss of mosaics; large-amplitude, long-term oscillations between land states that preclude ecosystem stability; and conservation of the mosaic even to the exclusion of agriculture/silviculture. In general, increasing the time horizon used for conservation decision making is more likely to maintain mosaic stability. In contrast, increasing the inherent conservation value of either forests or grasslands is more likely to induce large oscillations-especially for forests-due to feedback from rarity-based conservation decisions. Given the potential for complex dynamics, empirically grounded nonlinear dynamical models should play a larger role in policy formulation for humanenvironment mosaic ecosystems.

The influence of social behaviour on competition between virulent pathogen strains

“…We did not study the influence of the social learning parameter κ in this paper, but previous research on other socio-ecological and socio-epidemiological systems suggests that the social learning rate can destabilize interior equilibria Henderson et al, 2016 ). A model that accounts for multiple rounds of mutation would enable studying how pathogen evolutionary timescales interact with social learning dynamics.…”

“…where k is the sampling rate and U is the payoff difference between the two strategies. This equation is derived elsewhere and is used in other socio-ecological and socioepidemiological models ( Bauch, 2005;Bauch et al, 2016;Henderson et al, 2016;Oraby et al, 2014 ). The augmented system of differential equations representing the coupled social-epidemiological SI 1 I 2 RX model with adaptive human behaviour is therefore given by:…”

The Influence Of Social Behavior On Competition Between Virulent Pathogen Strains

“…These assumptions give rise to a differential equation of the form dx/dt = kx (1 − x )Δ U where k is the sampling rate and Δ U is the payoff difference between the two strategies. This equation is derived elsewhere and is used in other socio-ecological and socio-epidemiological models [35, 37, 38, 39]. The augmented system of differential equations representing the coupled social-epidemiological SI 1 I 2 RX model with adaptive human behaviour is therefore given by: …”

Human–environment interactions in population and ecosystem health

“…They stress the importance of compensating farmers for the ecosystem services provided by their lands, another example where the architecture of incentives is crucial to the sustenance of ecosystem health and viability in coupled human-environment systems. Similarly, Henderson et al (26) examine a region in southern Brazil where natural mosaics of forests interspersed with grasslands are gradually converted to agricultural and silvicultural use. The authors study long-term trajectories of these mosaics by coupling the ecological dynamics to a model of human behavior, calibrated with ecological and sociological data.…”