No abstract

There is a need to improve the quantity and quality of data in biodiversity monitoring projects. We compared an automated digital recording system (ADRS) with traditional methods (point-counts and transects) for the assessment of birds and amphibians. The ADRS proved to produce better quantity and quality of data. This new method has 3 additional advantages: permanent record of a census, 24 h/d data collection and the possibility of automated species identification. (WILDLIFE SOCIETY BULLETIN 34(1): 211-214; 2006)

The virulence–transmission trade‐off hypothesis proposed more than 30 years ago is the cornerstone in the study of host–parasite co‐evolution. This hypothesis rests on the premise that virulence is an unavoidable and increasing cost because the parasite uses host resources to replicate. This cost associated with replication ultimately results in a deceleration in transmission rate because increasing within‐host replication increases host mortality. Empirical tests of predictions of the hypothesis have found mixed support, which cast doubt about its overall generalizability. To quantitatively address this issue, we conducted a meta‐analysis of 29 empirical studies, after reviewing over 6000 published papers, addressing the four core relationships between (1) virulence and recovery rate, (2) within‐host replication rate and virulence, (3) within‐host replication and transmission rate, and (4) virulence and transmission rate. We found strong support for an increasing relationship between replication and virulence, and replication and transmission. Yet, it is still uncertain if these relationships generally decelerate due to high within‐study variability. There was insufficient data to quantitatively test the other two core relationships predicted by the theory. Overall, the results suggest that the current empirical evidence provides partial support for the trade‐off hypothesis, but more work remains to be done.

Predicting connectivity, or how landscapes alter movement, is essential for understanding the scope for species persistence with environmental change. Although it is well known that movement is risky, connectivity modelling often conflates behavioural responses to the matrix through which animals disperse with mortality risk. We derive new connectivity models using random walk theory, based on the concept of spatial absorbing Markov chains. These models decompose the role of matrix on movement behaviour and mortality risk, can incorporate species distribution to predict the amount of flow, and provide both short‐ and long‐term analytical solutions for multiple connectivity metrics. We validate the framework using data on movement of an insect herbivore in 15 experimental landscapes. Our results demonstrate that disentangling the roles of movement behaviour and mortality risk is fundamental to accurately interpreting landscape connectivity, and that spatial absorbing Markov chains provide a generalisable and powerful framework with which to do so.

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.dispersal | graph theory | habitat fragmentation | latent space models | landscape ecology N etwork analysis has recently exploded across scientific disciplines, including the social sciences, physics, cellular biology, and ecology (1-4). Topics as divergent as the stability of the Internet and the structure of metabolic reactions can be depicted through network analysis (1, 3). Such analysis is beneficial because it can facilitate the identification of complex, and often emergent, patterns, and can provide hypotheses for relationships between structure and function in many systems (2, 3). Nonetheless, a growing, widespread concern in the topic of network analysis is the reliability of data used in constructing networks (4-8).In ecology and conservation, network analysis is increasingly being used to assess population connectivity across landscapes (9-13). Because of the importance of connectivity in conservation and its relevance to population and community ecology (14-16), network analysis and the accompanying use of graph theory are often emphasized as powerful approaches that have modest data requirements for assessing connectivity (10,11,13). In this spatial context, resource patches are considered nodes (or vertices) and movements and/or flows between pat...