Tropical forest degradation emits carbon at a rate of~0.5 Pg·y −1 , reduces biodiversity, and facilitates forest clearance. Understanding degradation drivers and patterns is therefore crucial to managing forests to mitigate climate change and reduce biodiversity loss. Putative patterns of degradation affecting forest stocks, carbon, and biodiversity have variously been described previously, but these have not been quantitatively assessed together or tested systematically. Economic theory predicts a systematic allocation of land to its highest use value in response to distance from centers of demand. We tested this theory to see if forest exploitation would expand through time and space as concentric waves, with each wave targeting lower value products. We used forest data along a transect from 10 to 220 km from Dar es Salaam (DES), Tanzania, collected at two points in time (1991 and 2005). Our predictions were confirmed: high-value logging expanded 9 km·y −1 , and an inner wave of lower value charcoal production 2 km·y ; 0.1 species per sample area (0.4 ha)]. Our study suggests that tropical forest degradation can be modeled and predicted, with its attendant loss of some public goods. In sub-Saharan Africa, an area experiencing the highest rate of urban migration worldwide, coupled with a high dependence on forestbased resources, predicting the spatiotemporal patterns of degradation can inform policies designed to extract resources without unsustainably reducing carbon storage and biodiversity. biodiversity conservation | carbon emissions | reducing emissions from deforestation and forest degradation | sustainability | tropical forest degradation

1. The need to assess population size and change is central to any population monitoring programme. A range of monitoring techniques is available for deer, but few studies have addressed the performance of these techniques in terms of their accuracy and their power to detect population changes reliably. This study compares the performance of three commonly used techniques to monitor woodland deer populations in terms of their accuracy, precision and statistical power using field data and simulation modelling. 2. Faecal Standing Crop (FSC) was found to provide the most accurate estimates at all deer densities studied, but was outperformed by distance sampling using thermal imaging in terms of precision and statistical power. Faecal Accumulation Rate (FAR) performed worst in terms of precision and statistical power, and was inferior in terms of accuracy to FSC. The accuracy of FAR and distance sampling using thermal imaging was similar. 3. Annual surveying is not the best use of resources, since 10% per annum increases or decreases in a population could not be detected by any of the techniques within a 3‐year time period. Even with the best technique under the most favourable conditions of deer density simulated in this study, a population change could only be detected reliably once the population had increased by 33% or decreased by 27%. In the least favourable conditions, the respective figures were a 380% increase or extinction. 4. All the techniques studied are relatively poor at detecting population change, so their use in practical deer management needs to be treated with caution. The consequences of relying on similar techniques to detect population change for rare species could be severe.

We present diving data for four whale sharks in relation to a predictable food pulse (reef fish spawn) and an analysis of the longest continuous fine-resolution diving record for a planktivorous shark. Fine-resolution pressure data from a recovered pop-up archival satellite tag deployed for 206 days on a whale shark were analysed using the fast Fourier Transform method for frequency domain analysis of time-series. The results demonstrated that a free-ranging whale shark displays ultradian, diel and circa-lunar rhythmicity of diving behaviour. Whale sharks dive to over 979.5 m and can tolerate a temperature range of 26.4 degrees C. The whale sharks made primarily diurnal deep dives and remained in relatively shallow waters at night. Whale shark diving patterns are influenced by a seasonally predictable food source, with shallower dives made during fish spawning periods.

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