Do small-grain processes matter for landscape scale questions? Sensitivity of a forest landscape model to the formulation of tree growth rate

Process-based forest landscape models are valuable tools for testing basic ecological theory and for projecting how forest landscapes may respond to climate change and other environmental shifts. However, the ability of these models to accurately predict environmentally-induced shifts in species distributions as well as changes in forest composition and structure is often contingent on the phenomenological representation of individual-level processes accurately scaling-up to landscape-level community dynamics. We use a spatially explicit landscape forest model (LandClim) to examine how three alternative formulations of individual tree growth (logistic, Gompertz, and von Bertalanffy) influence model results. Interactions between growth models and landscape characteristics (landscape heterogeneity and disturbance intensity) were tested to determine in what type of landscape simulation results were most sensitive to growth model structure. We found that simulation results were robust to growth function formulation when the results were assessed at a large spatial extent (landscape) and when coarse response variables, such as total forest biomass, were examined. However, results diverged when more detailed response variables, such as species composition within elevation bands, were considered. These differences were particularly prevalent in regions that included environmental transition zones where forest composition is strongly driven by growth-dependent competition. We found that neither landscape heterogeneity nor the intensity of landscape disturbances accentuated simulation sensitivity to growth model formulation. Our results indicate that at the landscape extent, simulation results are robust, but the reliability of model results at a finer resolution depends critically on accurate tree growth function

Sensitivity of ecosystem goods and services projections of a forest landscape model to initialization data

Projections of indicators of forest ecosystem goods and services (EGS) based on process-based landscape models are critical for adapting forest management to climate change. However, the scarcity of fine-grained, spatially explicit forest data means that initializing these models is both a challenge and a source of uncertainty. To test how different initialization approaches influence the simulation of forest dynamics and EGS indicators we initialized the forest landscape model LandClim with fine resolution empirical data, coarse empirical data, and simulation-derived data, and evaluated the results at three spatial scales (stand, management area and landscape). Simulations were performed for a spruce (Picea abies) dominated landscape in the Black Forest, Germany, under current climate and a climate change scenario. We found that long-term (>150years) projections are robust to initialization uncertainty. In contrast, shorter-term projections are sensitive to initialization uncertainty, with sensitivity increasing when EGS are assessed at smaller spatial scales, and when the EGS indicators depend on the spatial distribution of individual species. EGS dynamics are strongly influenced by interactions between the density, species composition, and age structure of initialized forests and simulated forest management. If EGS dynamics are strongly influenced by climate change, such as when climate change induces mortality in drought-sensitive species, some of the initialization uncertainty can be masked. We advocate for initializing landscape models with fine-grained data in applications that focus on spatial management problems in heterogeneous landscapes, and stress that the scale of analysis must be in accordance with the accuracy that is warranted by the initialization dat

Impacts of changing climate and land use on vegetation dynamics in a Mediterranean ecosystem: insights from paleoecology and dynamic modeling

Forests near the Mediterranean coast have been shaped by millennia of human disturbance. Consequently, ecological studies relying on modern observations or historical records may have difficulty assessing natural vegetation dynamics under current and future climate. We combined a sedimentary pollen record from Lago di Massacciucoli, Tuscany, Italy with simulations from the LandClim dynamic vegetation model to determine what vegetation preceded intense human disturbance, how past changes in vegetation relate to fire and browsing, and the potential of an extinct vegetation type under present climate. We simulated vegetation dynamics near Lago di Massaciucoli for the last 7,000years using a local chironomid-inferred temperature reconstruction with combinations of three fire regimes (small infrequent, large infrequent, small frequent) and three browsing intensities (no browsing, light browsing, and moderate browsing), and compared model output to pollen data. Simulations with low disturbance support pollen-inferred evidence for a mixed forest dominated by Quercus ilex (a Mediterranean species) and Abies alba (a montane species). Whereas pollen data record the collapse of A. alba after 6000calyr bp, simulated populations expanded with declining summer temperatures during the late Holocene. Simulations with increased fire and browsing are consistent with evidence for expansion by deciduous species after A. alba collapsed. According to our combined paleo-environmental and modeling evidence, mixed Q. ilex and A. alba forests remain possible with current climate and limited disturbance, and provide a viable management objective for ecosystems near the Mediterranean coast and in regions that are expected to experience a mediterranean-type climate in the futur

Modeling abundance using N-mixture models: the importance of considering ecological mechanisms

Predicting abundance across a species' distribution is useful for studies of ecology and biodiversity management. Modeling of survey data in relation to environmental variables can be a powerful method for extrapolating abundances across a species' distribution and, consequently, calculating total abundances and ultimately trends. Research in this area has demonstrated that models of abundance are often unstable and produce spurious estimates, and until recently our ability to remove detection error limited the development of accurate models. The N-mixture model accounts for detection and abundance simultaneously and has been a significant advance in abundance modeling. Case studies that have tested these new models have demonstrated success for some species, but doubt remains over the appropriateness of standard N-mixture models for many species. Here we develop the N-mixture model to accommodate zero-inflated data, a common occurrence in ecology, by employing zero-inflated count models. To our knowledge, this is the first application of this method to modeling count data. We use four variants of the N-mixture model (Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial) to model abundance, occupancy (zero-inflated models only) and detection probability of six birds in South Australia. We assess models by their statistical fit and the ecological realism of the parameter estimates. Specifically, we assess the statistical fit with AIC and assess the ecological realism by comparing the parameter estimates with expected values derived from literature, ecological theory, and expert opinion. We demonstrate that, despite being frequently ranked the “best model” according to AIC, the negative binomial variants of the N-mixture often produce ecologically unrealistic parameter estimates. The zero-inflated Poisson variant is preferable to the negative binomial variants of the N-mixture, as it models an ecological mechanism rather than a statistical phenomenon and generates reasonable parameter estimates. Our results emphasize the need to include ecological reasoning when choosing appropriate models and highlight the dangers of modeling statistical properties of the data. We demonstrate that, to obtain ecologically realistic estimates of abundance, occupancy and detection probability, it is essential to understand the sources of variation in the data and then use this information to choose appropriate error distributions. Copyright ESA. All rights reserved

Catastrophic Floods May Pave the Way for Increased Genetic Diversity in Endemic Artesian Spring Snail Populations

The role of disturbance in the promotion of biological heterogeneity is widely recognised and occurs at a variety of ecological and evolutionary scales. However, within species, the impact of disturbances that decimate populations are neither predicted nor known to result in conditions that promote genetic diversity. Directly examining the population genetic consequences of catastrophic disturbances however, is rarely possible, as it requires both longitudinal genetic data sets and serendipitous timing. Our long-term study of the endemic aquatic invertebrates of the artesian spring ecosystem of arid central Australia has presented such an opportunity. Here we show a catastrophic flood event, which caused a near total population crash in an aquatic snail species (Fonscochlea accepta) endemic to this ecosystem, may have led to enhanced levels of within species genetic diversity. Analyses of individuals sampled and genotyped from the same springs sampled both pre (1988–1990) and post (1995, 2002–2006) a devastating flood event in 1992, revealed significantly higher allelic richness, reduced temporal population structuring and greater effective population sizes in nearly all post flood populations. Our results suggest that the response of individual species to disturbance and severe population bottlenecks is likely to be highly idiosyncratic and may depend on both their ecology (whether they are resilient or resistant to disturbance) and the stability of the environmental conditions (i.e. frequency and intensity of disturbances) in which they have evolved

Cross-scale Interactions Among Bark Beetles, Climate Change, and Wind Disturbances: a Landscape Modeling Approach

Bark beetles are a key forest disturbance agent worldwide, with their impact shaped by climate, forest susceptibility, and interactions with other disturbances such as windthrow and fire. There is ample evidence of the interactions among these factors at small spatial and temporal scales, but projecting their long‐term and landscape‐scale impacts remains a challenge. We developed a spatially explicit model of European spruce bark beetle (Ips typographus) dynamics that incorporates beetle phenology and forest susceptibility and integrated it in a climate‐sensitive landscape model (LandClim). We first corroborated model outputs at various spatial and temporal scales and then applied the model in three case studies (in the Black Forest, Germany, and Davos, Switzerland) that cover an extended climatic gradient. We used this model and case study framework to examine the mechanisms and feedbacks that are driving short‐term and long‐term interactions among beetle disturbance, climate change, and windthrow, and how they may shift in the future. At the current cold‐wet end of the Norway spruce (Picea abies) distribution, climate change is projected to increase temperature and drought, such that beetles become a more dominant disturbance agent. At the warm‐dry end of the spruce distribution, where, under current climate, beetle outbreaks are confined to the simultaneous occurrence of drought and windthrow, the simulated level of drought alone sufficed for triggering beetle outbreaks, such that elevated drought‐ and beetle‐induced spruce mortality would negatively feed back on beetle disturbance in the long term. This would lead to receding beetle populations due to the local extinction of Norway spruce. These results suggest that, depending on initial environmental conditions, climate change may shift the importance of direct and indirect drivers of disturbances. These shifts may affect the sign and strength of cross‐scale disturbance interactions and may impact the cost–benefit trade‐off between beetle suppression and preventive management strategies

Do small-grain processes matter for landscape scale questions?: Sensitivity of a forest landscape model to the formulation of tree growth rate

ISSN:0921-2973ISSN:1572-976

Aggregated LandClim simulation output data

Data are on tree species biomass (Norway spruce and others, Mg/ha), disturbance damage (bark beetles and wind, Mg/ha) and forest susceptibility indices (drought-, age- and spruce share-induced susceptibility and all combined, dimensionless) for three case studies (Black Forest low and high elevation and Davos), three climate and wind scenarios and with and without beetle disturbance for decades within AD 2000-2200. The data were generated using the forest landscape model LandClim and are averages of cell-specific data over the three study areas. Fig. 8 and Fig. B1-4 in Appendix B of the publication visualize the data