The MacKinnon Lists Technique: An efficient new method for rapidly assessing biodiversity and species abundance ranks in the marine environment.

Widespread and ever-increasing anthropogenic impacts in the marine environment are driving a need to develop more efficient survey methods for monitoring changes in marine biodiversity. There is a particular urgent need for survey methods that could more rapidly and effectively detect change in species richness, abundance and community composition. Here, test the suitability of the Mackinnon Lists Technique for use in the marine environment by testing its effectiveness for rapid assessment of fish communities. The MacKinnon Lists Technique is a time-efficient and cost-effective sampling method developed for studying avian tropical biodiversity, in which several list samples of species can be collected from a single survey. Using the well-established MaxN approach on data from deployments of a Baited Remote Underwater Video Systems for comparison, we tested the suitability of the MacKinnon Lists Technique for use in marine environments by analysing tropical reef fish communities. Using both methods for each data set, differences in community composition between depths and levels of protection were assessed. Both methods were comparable for diversity and evenness indices with similar ranks for species. Multivariate analysis showed that the MacKinnon Lists Technique and MaxN detected similar differences in community composition at different depths and protection status. However, the MacKinnon Lists Technique detected significant differences between factors when fewer videos (representing reduced survey effort) were used. We conclude that the MacKinnon Lists Technique is at least as effective as the widely used MaxN method for detecting differences between communities in the marine environment and suggest can do so with lower survey effort. The MacKinnon Lists Technique has the potential to be widely used as an effective new tool for rapid conservation monitoring in marine ecosystems.

Predicting population change from models based on habitat availability and utilization.

The need to understand the impacts of land management for conservation, agriculture and disease prevention are driving demand for new predictive ecology approaches that can reliably forecast future changes in population size. Currently, although the link between habitat composition and animal population dynamics is undisputed, its function has not been quantified in a way that enables accurate prediction of population change in nature. Here, using 12 house sparrow colonies as a proof-of-concept, we apply recent theoretical advances to predict population growth or decline from detailed data on habitat composition and habitat selection. We show, for the first time, that statistical population models using derived covariates constructed from parametric descriptions of habitat composition and habitat selection can explain an impressive 92% of observed population variation. More importantly, they provide excellent predictive power under cross-validation, anticipating 81% of variability in population change. These models may be embedded in readily available generalized linear modelling frameworks, allowing their rapid application to field systems. Furthermore, we use optimization on our sample of sparrow colonies to demonstrate how such models, linking populations to their habitats, permit the design of practical and environmentally sound habitat manipulations for managing populations.

Past Human Disturbance Effects upon Biodiversity are Greatest in the Canopy; A Case Study on Rainforest Butterflies.

A key part of tropical forest spatial complexity is the vertical stratification of biodiversity, with widely differing communities found in higher rainforest strata compared to terrestrial levels. Despite this, our understanding of how human disturbance may differentially affect biodiversity across vertical strata of tropical forests has been slow to develop. For the first time, how the patterns of current biodiversity vary between three vertical strata within a single forest, subject to three different types of historic anthropogenic disturbance, was directly assessed. In total, 229 species of butterfly were detected, with a total of 5219 individual records. Butterfly species richness, species diversity, abundance and community evenness differed markedly between vertical strata. We show for the first time, for any group of rainforest biodiversity, that different vertical strata within the same rainforest, responded differently in areas with different historic human disturbance. Differences were most notable within the canopy. Regenerating forest following complete clearance had 47% lower canopy species richness than regenerating forest that was once selectively logged, while the reduction in the mid-storey was 33% and at ground level, 30%. These results also show for the first time that even long term regeneration (over the course of 30 years) may be insufficient to erase differences in biodiversity linked to different types of human disturbance. We argue, along with other studies, that ignoring the potential for more pronounced effects of disturbance on canopy fauna, could lead to the underestimation of the effects of habitat disturbance on biodiversity, and thus the overestimation of the conservation value of regenerating forests more generally.

Predicting population-level risk effects of predation from the responses of individuals.

Fear of predation produces large effects on prey population dynamics through indirect risk effects that can cause even greater impacts than direct predation mortality. As yet, there is no general theoretical framework for predicting when and how these population risk effects will arise in specific prey populations, meaning that there is often little consideration given to the key role predator risk effects can play in understanding conservation and wildlife management challenges. Here, we propose that population predator risk effects can be predicted through an extension of individual risk trade-off theory and show for the first time that this is the case in a wild vertebrate system. Specifically, we demonstrate that the timing (in specific months of the year), occurrence (at low food availability), cause (reduction in individual energy reserves), and type (starvation mortality) of a population-level predator risk effect can be successfully predicted from individual responses using a widely applicable theoretical framework (individual-based risk trade-off theory). Our results suggest that individual-based risk trade-off frameworks could allow a wide range of population-level predator risk effects to be predicted from existing ecological theory, which would enable risk effects to be more routinely integrated into consideration of population processes and in applied situations such as conservation.

Mass regulation in response to predation risk can indicate population declines.

In theory, survival rates and consequent population status might be predictable from instantaneous behavioural measures of how animals prioritize foraging vs. avoiding predation. We show, for the 30 most common small bird species ringed in the UK, that one quarter respond to higher predation risk as if it is mass-dependent and lose mass. Half respond to predation risk as if it only interrupts their foraging and gain mass thus avoiding consequent increased starvation risk from reduced foraging time. These mass responses to higher predation risk are correlated with population and conservation status both within and between species (and independently of foraging habitat, foraging guild, sociality index and size) over the last 30 years in Britain, with mass loss being associated with declining populations and mass gain with increasing populations. If individuals show an interrupted foraging response to higher predation risk, they are likely to be experiencing a high quality foraging environment that should lead to higher survival. Whereas individuals that show a mass-dependent foraging response are likely to be in lower quality foraging environments, leading to relatively lower survival.

Mass-dependent predation risk as a mechanism for house sparrow declines?

House sparrow (Passer domesticus) numbers have declined rapidly in both rural and urban habitats across Western Europe over the last 30 years, leading to their inclusion on the UK conservation red list. The decline in farmland has been linked to a reduction in winter survival caused by reduced food supply. This reduction in food supply is associated with agricultural intensification that has led to the loss of seed-rich winter stubble and access to spilt grain. However, urban house sparrows have also declined, suggesting that reduced food supply in farmland is not the sole reason for the decline. Here, we show that changes in house sparrow mass and thus fat reserves are not regulated to minimize starvation risk, as would be expected if limited winter food were the only cause of population decline. Instead, the species appears to be responding to mass-dependent predation risk, with starvation risk and predation risk traded-off such that house sparrows may be particularly vulnerable to environmental change that reduces the predictability of the food supply.