Mapping scrub vegetation cover from photogrammetric point clouds: A useful tool in reserve management.

Scrub vegetation is a valuable habitat and resource for wildlife, but if unmanaged can encroach and dominate adjacent habitats, reducing biodiversity value. A primary task in the management of terrestrial nature reserves in the UK is monitoring and controlling scrub. The methods used to monitor and assess scrub cover are often basic, relying on qualitative assessment. Inaccurate assessments may fail to inform appropriate management of the habitats and lead to loss or degradation of important ecological features. Scrub can be monitored using UAV or satellite-derived imagery, but it can be difficult to distinguish between other vegetation types without using high-cost hyperspectral sensors. An alternative method using high-resolution surface models from photogrammetric point clouds enables the isolation of vegetation types based on height. Scrub can be isolated from woodland, hedgerows, and tall ground vegetation. In this study, we calculate scrub cover using a photogrammetric point cloud modeling approach using UAVs. We illustrate the method with two case studies from the UK. The scrub cover at Daneway Banks, a calcareous grassland site in Gloucestershire, was calculated at 21.8% of the site. The scrub cover at Flat Holm Island, a maritime grassland in the Severn Estuary, was calculated at 7%. This approach enabled the scrub layer to be readily measured and if required, modeled to provide a visual guide of what a projected management objective would look like. This approach provides a new tool in reserve management, enabling habitat management strategies to be informed, and progress toward objectives monitored.

Lemurs in Cacao: Presence and Abundance within the Shade Plantations of Northern Madagascar.

The recognition that much biodiversity exists outside protected areas is driving research to understand how animals survive in anthropogenic landscapes. In Madagascar, cacao (Theobroma cacao) is grown under a mix of native and exotic shade trees, and this study sought to understand whether lemurs were present in these agroecosystems. Between November 2016 and March 2017, discussions with farmers, nocturnal reconnaissance surveys and camera traps were used to confirm the presence of lemurs in the Cokafa and Mangabe plantations near Ambanja, north-west Madagascar. Four species of lemur were encountered in nocturnal surveys: Mirza zaza, Phaner parienti, Microcebussp. and Cheirogaleussp. with encounter rates of 1.2, 0.4, 0.4 and 0.3 individuals/km, respectively. The presence of Lepilemur dorsalis was confirmed by camera trap. This is the first time lemurs have been studied in cacao plantations, and understanding how these threatened animals use anthropogenic landscapes is vital for their conservation.

Violations of transitivity under fitness maximization.

We present a novel demonstration that violations of transitive choice can result from decision strategies that maximize fitness. Our results depend on how the available options, including options not currently chosen, influence a decision-maker's expectations about the future. In particular, they depend on how the presence of an option may act as an insurance against a run of bad luck in the future.

Do we expect natural selection to produce rational behaviour?

We expect that natural selection should result in behavioural rules which perform well; however, animals (including humans) sometimes make bad decisions. Researchers account for these with a variety of explanations; we concentrate on two of them. One explanation is that the outcome is a side effect; what matters is how a rule performs (in terms of reproductive success). Several rules may perform well in the environment in which they have evolved, but their performance may differ in a 'new' environment (e.g. the laboratory). Some rules may perform very badly in this environment. We use the debate about whether animals follow the matching law rather than maximizing their gains as an illustration. Another possibility is that we were wrong about what is optimal. Here, the general idea is that the setting in which optimal decisions are investigated is too simple and may not include elements that add extra degrees of freedom to the situation.