Synergistic impacts of habitat loss and fragmentation on model ecosystems

On the History of Ecosystem Dynamical Modeling: The Rise and Promises of Qualitative Models

Ecosystem modeling is a complex and multidisciplinary modeling problem which emerged in the 1950s. It takes advantage of the computational turn in sciences to better understand anthropogenic impacts and improve ecosystem management. For that purpose, ecosystem simulation models based on difference or differential equations were built. These models were relevant for studying dynamical phenomena and still are. However, they face important limitations in data-poor situations. As a response, several formal and non-formal qualitative dynamical modeling approaches were independently developed to overcome some limitations of the existing methods. Qualitative approaches allow studying qualitative dynamics as relevant abstractions of those provided by quantitative models (e.g., response to press perturbations). Each modeling framework can be viewed as a different assemblage of properties (e.g., determinism, stochasticity or synchronous update of variable values) designed to satisfy some scientific objectives. Based on four stated objectives commonly found in complex environmental sciences ((1) grasping qualitative dynamics, (2) making as few assumptions as possible about parameter values, (3) being explanatory and (4) being predictive), our objectives were guided by the wish to model complex and multidisciplinary issues commonly found in ecosystem modeling. We then discussed the relevance of existing modeling approaches and proposed the ecological discrete-event networks (EDEN) modeling framework for this purpose. The EDEN models propose a qualitative, discrete-event, partially synchronous and possibilistic view of ecosystem dynamics. We discussed each of these properties through ecological examples and existing analysis techniques for such models and showed how relevant they are for environmental science studies.

Testing the niche reduction hypothesis for a fossorial rodent (Geomys bursarius) experiencing agricultural intensification

Habitat loss and fragmentation from conversion to agriculture are known threats to grassland species. However, continued agricultural intensification may further reduce a species distribution and realized niche. Here, we create species distribution models (SDMs) for the plains pocket gopher (Geomys bursarius), an ecosystem engineer in grasslands, for historic and contemporary eras in a dynamic agroecosystem and test the "niche reduction hypothesis." We compare SDMs created from gopher occurrences from the historic era (~1950s, pre-agricultural intensification) and the contemporary era (post-agricultural intensification) and assess model transferability. We evaluate shifts in environmental relationships, changes in limiting factors, and an overall decline in niche hypervolume. SDMs were nontransferable between the historic and contemporary eras. Environmental drivers of gopher distribution shifted from elevation, precipitation, and land cover in the 1950s to land cover, soil texture, and soil drainage presently. There also were shifts in environmental associations with gophers now occurring at lower elevations, in sandier soils, and less often in agriculture. Dominant limiting factors of gophers shifted from precipitation to land cover. Gophers were not detected at historic locations during recent resurveys. Contemporary niche hypervolume was reduced compared with the historic niche hypervolume. We found support for the niche reduction hypothesis for a fossorial, grassland species. Further application of the niche reduction hypothesis in landscapes experiencing agricultural intensification is warranted. Understanding niche reduction allows for conservation efforts that promote continued persistence in the contemporary niche while also identifying areas to restore within the historic niche.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Artificial night light and anthropogenic noise interact to influence bird abundance over a continental scale

The extent of artificial night light and anthropogenic noise (i.e., "light" and "noise") impacts is global and has the capacity to threaten species across diverse ecosystems. Existing research involving impacts of light or noise has primarily focused on noise or light alone and single species; however, these stimuli often co-occur and little is known about how co-exposure influences wildlife and if and why species may vary in their responses. Here, we had three aims: (1) to investigate species-specific responses to light, noise, and the interaction between the two using a spatially explicit approach to model changes in abundance of 140 prevalent bird species across North America, (2) to investigate responses to the interaction between light exposure and night length, and (3) to identify functional traits and habitat affiliations that explain variation in species-specific responses to these sensory stimuli with phylogenetically informed models. We found species that responded to noise exposure generally decreased in abundance, and the additional presence of light interacted synergistically with noise to exacerbate its negative effects. Moreover, the interaction revealed negative emergent responses for several species that only reacted when light and noise co-occurred. Additionally, an interaction between light and night length revealed 47 species increased in abundance with light exposure during longer nights. In addition to modifying behavior with optimal temperature and potential foraging opportunities, birds might be attracted to light, yet suffer inadvertent physiological consequences. The trait that most strongly related to avian response to light and noise was habitat affiliation. Specifically, species that occupy closed habitat were less tolerant of both sensory stressors compared to those that occupy open habitat. Further quantifying the contexts and intrinsic traits that explain how species respond to noise and light will be fundamental to understanding the ecological consequences of a world that is ever louder and brighter.

Habitat loss, predation pressure and episodic heat-shocks interact to impact arthropods and photosynthetic functioning of microecosystems

Ecosystems face multiple, potentially interacting, anthropogenic pressures that can modify biodiversity and ecosystem functioning. Using a bryophyte-microarthropod microecosystem we tested the combined effects of habitat loss, episodic heat-shocks and an introduced non-native apex predator on ecosystem function (chlorophyll fluorescence as an indicator of photosystem II function) and microarthropod communities (abundance and body size). The photosynthetic function was degraded by the sequence of heat-shock episodes, but unaffected by microecosystem patch size or top-down pressure from the introduced predator. In small microecosystem patches without the non-native predator, Acari abundance decreased with heat-shock frequency, while Collembola abundance increased. These trends disappeared in larger microecosystem patches or when predators were introduced, although Acari abundance was lower in large patches that underwent heat-shocks and were exposed to the predator. Mean assemblage body length (Collembola) was reduced independently in small microecosystem patches and with greater heat-shock frequency. Our experimental simulation of episodic heatwaves, habitat loss and non-native predation pressure in microecosystems produced evidence of individual and potentially synergistic and antagonistic effects on ecosystem function and microarthropod communities. Such complex outcomes of interactions between multiple stressors need to be considered when assessing anthropogenic risks for biota and ecosystem functioning.

Relative genetic diversity of the rare and endangered Agave shawii ssp. shawii and associated soil microbes within a southern California ecological preserve

Shaw's Agave (Agave shawii ssp. shawii) is an endangered maritime succulent growing along the coast of California and northern Baja California. The population inhabiting Point Loma Peninsula has a complicated history of transplantation without documentation. The low effective population size in California prompted agave transplanting from the U.S. Naval Base site (NB) to Cabrillo National Monument (CNM). Since 2008, there are no agave sprouts identified on the CNM site, and concerns have been raised about the genetic diversity of this population. We sequenced two barcoding loci, rbcL and matK, of 27 individual plants from 5 geographically distinct populations, including 12 individuals from California (NB and CNM). Phylogenetic analysis revealed the three US and two Mexican agave populations are closely related and have similar genetic variation at the two barcoding regions, suggesting the Point Loma agave population is not clonal. Agave-associated soil microbes used significantly more carbon sources in CNM soil samples than in NB soil likely due to higher pH and moisture content; meanwhile, soil type and soil chemistry analysis including phosphorus, nitrate nitrogen, organic matter, and metals revealed significant correlations between microbial diversity and base saturation (p < 0.05, r 2 = 0.3676), lime buffer capacity (p < 0.01, r 2 = 0.7055), equilibrium lime buffer capacity (p < 0.01, r 2 = 0.7142), and zinc (p < 0.01, r 2 = 0.7136). Soil microbiome analysis within the CNM population revealed overall expected richness (H' = 5.647-6.982) for Agave species, while the diversity range (1 - D = 0.003392-0.014108) suggests relatively low diversity marked by high individual variation. The most prominent remaining US population of this rare species is not clonal and does not seem to be threatened by a lack of genetic and microbial diversity. These results prompt further efforts to investigate factors affecting Agave's reproduction and fitness.

Making ecological models adequate

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.