Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics

Abstract

Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples – one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics – to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation.

The sociality–health–fitness nexus: synthesis, conclusions and future directions

“…Habitat loss, climate change, overexploitation, invasive species and environmental pollution are among the important drivers of species extinctions; the compounding effects of health, specifically infectious disease, are now considered as an additional driver [120]. Infectious diseases threaten conservation of biodiversity through both local and global extinctions [121][122][123], and health and disease should be considered in population viability analysis and species conservation planning [124,125], as well as in reintroductions of species of conservation concern [126].…”

Species‐level persistence probabilities for recovery and conservation status assessment

“…; Lacy et al. ) to nondemographic approaches (e.g., Hanski ; Hanski & Ovaskainen ; He ) that would be particularly appropriate in data‐poor situations. However, to our knowledge, the latter approaches have not been applied to recovery planning.…”

“…Incorporating standardized assessments of viability and clearly linking recovery criteria to species viability would improve transparency and provide a scientific basis for recovery plans. Such assessments can be made through a range of analytical methods, from complex spatially explicit, individual-based models (e.g., Carroll et al 2006;Bonnot et al 2013;Lacy et al 2013) to nondemographic approaches (e.g., Hanski 1994;Hanski & Ovaskainen 2000;He 2012) that would be particularly appropriate in data-poor situations. However, to our knowledge, the latter approaches have not been applied to recovery planning.…”

A Novel Tool for Making Policy Recommendations Based on PVA: Helping Theory Become Practice

“…Although increasingly complex modeling methods are becoming more widely used (e.g., Lacy et al . ), a very common form of PVA uses a population projection matrix consisting of vital rates to detail life histories of target species (e.g., age‐specific growth, survival and fecundity rates). PVA users mimic possible management actions by perturbing vital rates in the model via a global (e.g., McCarthy et al .…”

“…Although increasingly complex modeling methods are becoming more widely used (e.g., Lacy et al 2013), a very common form of PVA uses a population projection matrix consisting of vital rates to detail life histories of target species (e.g., age-specific growth, survival and fecundity rates). PVA users mimic possible management actions by perturbing vital rates in the model via a global (e.g., McCarthy et al 1995;Wisdom et al 2000;Cross & Beissinger 2001) or a local (one-at-a-time) sensitivity analysis, with a global approach considered superior (Naujokaitis-Lewis et al 2008;Saltelli & Annoni 2010).…”