The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

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Pathogenic bacteria in nearshore waters are a public health threat, and many have watershed sources. Hence, understanding direct and indirect causes of bacterial loading can improve awareness and watershed management. Rainfall-driven runoff influences river discharge, affecting pathogen transport to the ocean. This study assessed pathogen loading to nearshore waters under varying weather conditions within Hilo Bay, Hawaii, from 2014 to 2017. Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), and fecal indicator bacteria (FIB) were quantified in the bay, rivers, and potential watershed sources using culturebased methods. Relationships between their concentrations with rainfall, river discharge, and water quality data were examined. Staphylococcus aureus, MRSA, and FIB were present within Hilo Bay and its rivers, as well as road runoff, sewage, and soils; MRSA was less prevalent. Staphylococcus aureus and FIB concentrations increased with rainfall and river discharge. Turbidity and salinity were the best water quality parameters for predicting bacteria concentrations, with positive and negative relationships, respectively. Our results suggest that more intense storms, especially after longer dry periods between events, will increase S. aureus and FIB loads to nearshore waters, as storms comprise >80% of annual river loads. Our models can be used to assess recreational water users' health risks and predict future water quality conditions with changing rainfall patterns.

Potential shifts in rainfall driven by climate change are anticipated to affect watershed processes (e.g., soil moisture, runoff, stream flow), yet few model systems exist in the tropics to test hypotheses about how these processes may respond to these shifts. We used a sequence of nine watersheds on Hawaii Island spanning 3000 mm (7500-4500 mm) of mean annual rainfall (MAR) to investigate the effects of short-term (24-h) and long-term (MAR) rainfall on three fecal indicator bacteria (FIB) (enterococci, total coliforms, and ). All sample sites were in native Ohia dominated forest above 600 m in elevation. Additional samples were collected just above sea level where the predominant land cover is pasture and agriculture, permitting the additional study of interactions between land use across the MAR gradient. We found that declines in MAR significantly amplified concentrations of all three FIB and that FIB yield increased more rapidly with 24-h rainfall in low-MAR watersheds than in high-MAR watersheds. Because storm frequency decreases with declining MAR, the rate of change in water potential affects microbial growth, whereas increased rainfall intensity dislodges more soil and bacteria as runoff compared with water-logged soils of high-MAR watersheds. As expected, declines in % forest cover and increased urbanization increased FIB. Taken together, shifts in rainfall may alter bacterial inputs to tropical streams, with land use change also affecting water quality in streams and near-shore environments.