Amanda E. Martin

Measuring and selecting scales of effect for landscape predictors in species–habitat models

Wildlife managers often use habitat models to determine species habitat requirements and to identify locations for conservation efforts, uses which depend on accurate specification of species-habitat relationships. Prior study suggests that model performance may be influenced by the way we measure environmental predictors. We hypothesized that species responses to landscape predictors are best represented by landscape composition-based measurements, rather than distance-based measurements. We also hypothesized that models using empirical data to select an appropriate scale of effect for each habitat predictor (multi-scale models) should perform better than models that assume a common scale of effect for all predictors (single-scale models). To test these hypotheses we constructed habitat models for three mammal species, Mephitis mephitis, Mustela erminea, and Procyon lotor, based on surveys conducted in 80 landscapes in southeastern Ontario, Canada. For each species we compared the performance of distance- and composition-based measurements, and we compared the performance of single- and multi-scale models. The composition-based measurement, measured at its empirically determined scale of effect, had greater explanatory power than the distance-based measurement of a given predictor more often than expected by chance, supporting our first hypothesis. Contrary to expectation, multi-scale models did not have better explanatory power or predictive performance relative to single-scale models. We identified and evaluated four potential mechanisms to explain this, and, depending on the species, we found that the best explanation was either that predictors have significant effects at a common scale or that, although the modeled effects were at multiple scales, they were of similar magnitude and direction at the scales modeled in single- and multi-scale models. Our results suggest that habitat modeling based on distance-based measurements could be improved by including composition-based measurements of landscape predictor variables, but that inclusion of predictor-specific scales of effect for composition-based measurements does not necessarily improve performance over models including composition-based measurements at a single scale. Conservation and wildlife management may be simplified when single-scale models perform as well as multi-scale models, as this suggests actions conducted at a single scale may address management objectives as well as actions taken at different scales for different landscape features.

What determines the spatial extent of landscape effects on species

ContextLandscape ecologists are often interested in measuring the effects of an environmental variable on a biological response; however, the strength and direction of effect depend on the size of the area within which the environmental variable is measured. Thus a central objective is to identify the optimal spatial extent within which to measure the environmental variable, i.e. the “scale of effect”.ObjectivesOur objectives are (1) to provide a comprehensive summary of the hypotheses concerning what determines the scale of effect, (2) to provide predictions that can be tested in empirical studies, and (3) to show, with a review of the literature, that most of these predictions have so far been inadequately tested.MethodsWe propose 14 predictions derived from five hypotheses explaining what determines the scale of effect, and review the literature (if any) supporting each prediction. These predictions involve five types of factors: (A) species traits, (B) landscape variables, (C) biological responses (e.g. abundance vs. occurrence), (D) indirect influences, and (E) regional context of the study. We identify methodological issues that hinder estimation of the scale of effect.ResultsOf the 14 predictions, only nine have been tested empirically and only five have received some empirical support. Most support is from simulation studies. Empirical evidence usually does not support predictions.Conclusions The study of the spatial scale at which landscape variables influence biological outcomes is in its infancy. We provide directions for future research by clarifying predictions concerning the determinants of the scale of effect.

From forest and agro-ecosystems to the microecosystems of the human body: what can landscape ecology tell us about tumor growth, metastasis, and treatment options?

Cancer is now understood to be a process that follows Darwinian evolution. Heterogeneous populations of cancerous cells that make up the tumor inhabit the tissue ‘microenvironment’, where ecological interactions analogous to predation and competition for resources drive the somatic evolution of cancer. The tumor microenvironment plays a crucial role in the tumor genesis, development, and metastasis processes, as it creates the microenvironmental selection forces that ultimately determine the cellular characteristics that result in the greatest fitness. Here, we explore and offer new insights into the spatial aspects of tumor–microenvironment interactions through the application of landscape ecology theory to tumor growth and metastasis within the tissue microhabitat. We argue that small tissue microhabitats in combination with the spatial distribution of resources within these habitats could be important selective forces driving tumor invasiveness. We also contend that the compositional and configurational heterogeneity of components in the tissue microhabitat do not only influence resource availability and functional connectivity but also play a crucial role in facilitating metastasis and may serve to explain, at least in part, tissue tropism in certain cancers. This novel work provides a compelling argument for the necessity of taking into account the structure of the tissue microhabitat when investigating tumor progression.

Habitat specialist birds disperse farther and are more migratory than habitat generalist birds.

Some theories predict habitat specialists should be less dispersive and migratory than generalists, while other theories predict the opposite. We evaluated the cross-species relationship between the degree of habitat specialization and dispersal and migration status in 101 bird species breeding in North America and the United Kingdom, using empirical estimates of the degree of habitat specialization from breeding bird surveys and mean dispersal distance estimates from large-scale mark-recapture studies. We found that habitat specialists dispersed farther than habitat generalists, and full migrants had more specialized habitat than partial migrants or resident species. To our knowledge this is the first large-scale, multi-species study to demonstrate a positive relationship between the degree of habitat specialization and dispersal, and it is opposite to the pattern found for invertebrates. This finding is particularly interesting because it suggests that trade-offs between the degree of habitat specialization and dispersal ability are not conserved across taxonomic groups. This cautions against extrapolation of trait co-occurrence from one species group to another. In particular, it suggests that efforts aimed at conserving the most habitat-specialist temperate-breeding birds will not lead to conservation of the most dispersal-limited species.

When to monitor and when to act: Value of information theory for multiple management units and limited budgets

The question of when to monitor and when to act is fundamental to applied ecology and notoriously difficult to answer. Value of information (VOI) theory holds great promise to help answer this question for many management problems. However, VOI theory in applied ecology has only been demonstrated in single-decision problems and has lacked explicit links between monitoring and management costs. Here, we present an extension of VOI theory for solving multi-unit decisions of whether to monitor before managing, while explicitly accounting for monitoring costs. Our formulation helps to choose the optimal monitoring/management strategy among groups of management units (e.g. species, habitat patches) and can be used to examine the benefits of partial and repeat monitoring. To demonstrate our approach, we use case-simulated studies of single-species protection that must choose among potential habitat areas, and classification and management of multiple species threatened with extinction. We provide spreadsheets and code to illustrate the calculations and facilitate application. Our case studies demonstrate the utility of predicting the number of units with a given outcome for problems with probabilities of discrete states and the efficiency of having a flexible approach to manage according to monitoring outcomes. Synthesis and applications. The decision to act or gather more information can have serious consequences for management. No decision, including the decision to monitor, is risk-free. Our multi-unit expansion of Value of Information theory can reduce the risk in monitoring/acting decisions for many applied ecology problems. While our approach cannot account for the potential value of discovering previously unknown threats or ecological processes via monitoring programmes, it can provide quantitative guidance on whether to monitor before acting, and which monitoring/management actions are most likely to meet management objectives.

Flying insect abundance declines with increasing road traffic

One potentially important but underappreciated threat to insects is road mortality. Road kill studies clearly show that insects are killed on roads, leading to the hypothesis that road mortality causes declines in local insect population sizes. In this study we used custom‐made sticky traps attached to a vehicle to target diurnal flying insects that interact with roads, sampling along 10 high‐traffic and 10 low‐traffic rural roads in southeastern Ontario, Canada. We used a paired sampling design to control for potentially confounding differences in the road characteristics (e.g. road width) and surrounding land covers (e.g. housing density) between high‐traffic and low‐traffic roads. We then used these data to test the prediction that fewer flying insects collide with vehicles, per vehicle (i.e. insect abundance is lower), on high‐traffic than low‐traffic roads. We found significantly fewer insects at the high‐traffic roads than at the low‐traffic roads as predicted. There was a 23.5% decline in the number of insects/km/vehicle on high‐traffic relative to low‐traffic roads. Given the high rates of insect mortality observed in previous studies, it is likely that road mortality contributes to these observed negative effects of traffic intensity. Thus the growing global road network is a concern for conservationists and land managers, not only because insect population declines contribute to the ongoing global losses of biodiversity but also because insects play a vital role in food webs and provide important ecosystem services.