The effect of population model choice and network topologies on extinction time patterns in dendritic ecological networks.

Models of populations in habitat networks are vital for understanding and linking processes and patterns across individuals, environments, ecological interactions, and population structures. River ecosystem models combine the physical structure of the networks with the biological processes of the organisms using structural and functional models, respectively. Previous studies on dendritic river networks have employed different functional (population) models and either directly claimed or implied that the results illustrate general properties of actual river systems. However, these studies have used different approaches and assumptions when modeling population characteristics and behavior, and it is possible that inferences regarding a system may vary based on the combination of functional model and the spatial structure of a network. This study aims to understand if different functional models in river systems produce substantially different model results and, therefore, whether conclusions are model-dependent. We compare variation in extinction time and occupancy proportion of river networks with linear, trellis, dendritic and ring-lattice topologies, using three population models (uniform, age-class and individual based) and one metapopulation-based (patch-occupancy) model. Dendritic, linear, and trellis structures did not show notable differences among extinction times for any of the four models. The difference between topologies was higher for the patch-occupancy model compared to the three population models. There were significant differences in the variations of patch-occupancy between the metapopulation and the population models, but the three population models of differing complexity produced broadly similar results. Therefore, if the occupancy data is obtained based on local subpopulations, spatial arrangement and connectivity does not appear to be the sole predictor of single-species metapopulation responses. We conclude that the outputs from functional models are robust to assumptions and varying levels of detail as long as they contain at least some detail at the level of individuals within habitat nodes. Also, if we are modeling network-scale populations, models that include at least some detailed information on individuals are a far better choice than considering populations implicitly.

Small-scale temporal variation and the effect of urbanisation on extracellular enzyme activity in streams.

The activity of six extracellular enzymes involved in the degradation of dissolved organic carbon compounds was measured in two highly urbanised and two minimally impacted streams east of Melbourne, Australia, using 4-methylumbelliferyl-substrates. Small-scale temporal variation in enzyme activity was determined by repeatedly sampling the same point in the water column, while the effect of flow was determined by sampling in regions of higher and lower flow in both stream types. Replicate samples showed that enzyme activity was not significantly different over small (minutes) time scales. On five of six sampling occasions the enzyme activity was unaffected by flow. On one sampling occasion in a minimally disturbed stream, the difference between the high- and low-flow regions was statistically significant (ANOSIM, Global R= 0.78, P= 0.03). Enzyme activity profiles (activities of the suite of enzymes) of the streams in urbanised catchments were different to those in minimally disturbed catchments. The measurements made in four different streams showed high reproducibility over short time periods (minutes) which lends greater credibility to analogous spatial studies. Although these results determined that small-scale temporal variability was not significant, and that the effects of flow were generally minimal, it is recommended that spatial and temporal variability in the stream be at least considered before any studies measuring extracellular enzyme activity in stream waters are carried out. Such an approach will lead to conclusions from measurements that are not likely to be confounded by variables such as flow rate or time.