The persistence of biodiversity in southern California under future land-use scenarios
Collaborative research with Dr Helen Regan, UC-Riverside, Principal Investigator
Sponsored by the National Science Foundation (2008-2012)
and by the Department of Energy National Institute for Climate Change Research (NICCR)
We investigated the combined impacts of habitat loss and fragmentation, altered fire regime and invasive species on functional groups of plants found in Mediterranean-type and some other fire-prone ecosystems. In particular we addressed the questions: a) How does projected habitat loss and fragmentation due to urbanization, in conjunction with indirect or synergistic effects (altered fire regimes, competition from invasive species), affect the risk of decline or extinction for plant populations in southern California? b) Which plant species traits are the best predictors of extinction or decline due to habitat loss, altered fire regimes and competition with invasive species?
We linked four different modeling approaches to address this: 1) a spatially explicit simulation model of urban growth to project land use and land cover change over the next 50-100 years; 2) potential habitat models for focal species using existing location records and interpolated using statistical and machine learning methods; 3) a landscape model of habitat dynamics including disturbance (such as fire) and succession that incorporates dynamic land-use change from the urban growth model; and 4) stochastic population models for a selected set of focal species to estimate risk of population decline or extinction under the urban growth scenarios coupled with habitat dynamics from the landscape model.
With NICCR supplemental support we also examined the impact of climate change and interactions with land use change and disturbance regimes.
Highlights of Major Findings
Publications (available upon request) and Findings
Concern over rapid global changes and the potential for interactions among multiple threats is prompting scientists to combine multiple modeling approaches to understand impacts on biodiversity. A relatively recent development is the combination of species distribution models, land use change predictions, and dynamic population models to predict the relative and combined impacts of climate change, land use change, and altered disturbance regimes on species’ extinction risk. Each modeling component introduces its own source of uncertainty through different parameters and assumptions, which, when combined, can result in compounded uncertainty that can have major implications for management. Although some uncertainty analyses have been conducted separately on various model components – such as climate predictions, species distribution models, land use change predictions, and population models – a unified sensitivity analysis comparing various sources of uncertainty in combined modeling approaches is needed to identify the most influential and problematic assumptions. We estimated the sensitivities of long-run population predictions to different ecological assumptions and parameter settings for a rare and endangered annual plant species (Acanthomintha ilicifolia, or San Diego thornmint). Uncertainty about habitat suitability predictions, due to the choice of species distribution model, contributed most to variation in predictions about long-run populations.
A species’ response to climate change depends on the interaction of biotic and abiotic factors that define future habitat suitability and species’ ability to migrate or adapt. The interactive effects of processes such as fire, dispersal, and predation have not been thoroughly addressed in the climate change literature. Our objective was to examine how life history traits, short-term global change perturbations, and long-term climate change interact to affect the likely persistence of an oak species - Quercus engelmannii (Engelmann oak). Specifically, we combined dynamic species distribution models, which predict suitable habitat, with stochastic, stage-based metapopulation models, which project population trajectories, to evaluate the effects of three global change factors – climate change, land use change, and altered fire frequency – emphasizing the roles of dispersal and seed predation. Our model predicted dramatic reduction in Q. engelmannii abundance, especially under drier climates and increased fire frequency. When masting lowers seed predation rates, decreased masting frequency leads to large abundance decreases. Current rates of dispersal are not likely to prevent these effects, although increased dispersal could mitigate population declines. The results suggest that habitat suitability predictions by themselves may under-estimate the impact of climate change for other species and locations.
As a clear consensus is emerging that suitable habitat for many species will dramatically reduce and/or shift with climate change, attention is turning to adaptation strategies to address these impacts. Assisted colonization is one such strategy that has been predominantly discussed in terms of the costs of introducing potential competitors into new communities and the benefits of reducing extinction risk. However, the success or failure of assisted colonization will depend on a range of population-level factors on which the climate change literature has been relatively silent—the quality of the recipient habitat, the number and life stages of translocated individuals, the establishment of translocated individuals in their new habitat and whether the recipient habitat is subject to ongoing threats all will play an important role in population persistence. We link a population model with dynamic bioclimate envelopes to investigate expected changes in populations with climate change, the impact of altered fire regimes on population persistence, and how much assisted colonization is necessary to minimize risk of decline in populations of Tecate cypress, a rare endemic tree in the California Floristic Province, a biodiversity hotspot. We show that when there are large source populations that are expected to decline dramatically due to habitat contractions, multiple nearby sites predicted to contain suitable habitat, minimal natural dispersal, high rates of establishment of translocated populations, and the absence of more serious ongoing threats, assisted colonization may be a risk-minimizing adaptation strategy. However, when serious ongoing threats exist, assisted colonization is ineffective
The conversion of natural habitat to urban settlements is a primary driver of biodiversity loss, and species’ persistence is threatened by the extent, location, and spatial pattern of development. Urban growth models are widely used to anticipate future development and to inform conservation manage- ment, but the source of spatial input to these models may contribute to uncertainty in their predictions. We compared two sources of historic urban maps, used as input for model calibration, to determine how differences in definition and scale of urban extent affect the resulting spatial predictions from a widely used urban growth model for San Diego County, CA under three conservation scenarios. The results showed that rate, extent, and spatial pattern of predicted urban development, and associated habitat loss, may vary substantially depending on the source of input data, regardless of how much land is excluded from development. Although the datasets we compared both represented urban land, different types of land use/land cover included in the definition of urban land and different minimum mapping units contributed to the discrepancies. Varying temporal resolution of the input datasets also contributed to differences in projected rates of development. Differential predicted impacts to vegetation types illustrate how the choice of spatial input data may lead to different conclusions relative to conservation. Although the study cannot reveal whether one dataset is better than another, modelers should carefully consider that geographical reality can be represented differently, and should carefully choose the defi- nition and scale of their data to fit their research objectives
We linked a spatially explicit stochastic population model to dynamic bioclimate envelopes to investigate the effects of climate change, habitat loss and fragmentation and altered ﬁre regime on population abundances of a long-lived obligate seeding shrub, Ceanothus verrucosus, a rare endemic species of southern California. We tested a range of ﬁre return intervals under the present and two future climate scenarios. We also assessed the impact of potential anthropogenic land-use change by excluding land identiﬁed as developable by local governments. We found that the 35–50 year ﬁre return interval resulted in the highest population abundances. Expected minimum population abundance (EMA) declined declined dramatically for shorter ﬁre intervals. Simulated future development resulted in a 33% decline in EMA, but relatively stable population trajectories over the time frame modeled. Relative changes in EMA for alternative ﬁre intervals were similar for all climate and habitat loss scenarios, except under the more severe climate scenario which resulted in a change in the relative ranking of the ﬁre scenarios. Our results show climate change to be the most serious threat facing obligate seeding shrubs embedded in urban landscapes, resulting in population decline and increased local extirpation, and that likely interactions with other threats increase risks to these species.
Linking species–environment relationships, landscape dynamics and population dynamics in a multi-modelling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species to be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood. Main conclusions Forecasts of the impacts of global change on species may be improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.