Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring.

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

Assessing representation of remote sensing derived forest structure and land cover across a network of protected areas.

Protected areas (PA) are an effective means of conserving biodiversity and protecting suites of valuable ecosystem services. Currently, many nations and international governments use proportional area protected as a critical metric for assessing progress towards biodiversity conservation. However, the areal and other common metrics do not assess the effectiveness of PA networks, nor do they assess how representative PA are of the ecosystems they aim to protect. Topography, stand structure, and land cover are all key drivers of biodiversity within forest environments, and are well-suited as indicators to assess the representation of PA. Here, we examine the PA network in British Columbia, Canada, through drivers derived from freely-available data and remote sensing products across the provincial biogeoclimatic ecosystem classification system. We examine biases in the PA network by elevation, forest disturbances, and forest structural attributes, including height, cover, and biomass by comparing a random sample of protected and unprotected pixels. Results indicate that PA are commonly biased towards high-elevation and alpine land covers, and that forest structural attributes of the park network are often significantly different in protected versus unprotected areas (426 out of 496 forest structural attributes found to be different; p < 0.01). Analysis of forest structural attributes suggests that establishing additional PA could ensure representation of various forest structure regimes across British Columbia's ecosystems. We conclude that these approaches using free and open remote sensing data are highly transferable and can be accomplished using consistent datasets to assess PA representations globally.

Outstanding Challenges in the Transferability of Ecological Models.

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.

Assessing the Effectiveness of Local Management of Coral Reefs Using Expert Opinion and Spatial Bayesian Modeling.

Multiple stressors are an increasing concern in the management and conservation of ecosystems, and have been identified as a key gap in research. Coral reefs are one example of an ecosystem where management of local stressors may be a way of mitigating or delaying the effects of climate change. Predicting how multiple stressors interact, particularly in a spatially explicit fashion, is a difficult challenge. Here we use a combination of an expert-elicited Bayesian network (BN) and spatial environmental data to examine how hypothetical scenarios of climate change and local management would result in different outcomes for coral reefs on the Great Barrier Reef (GBR), Australia. Parameterizing our BN using the mean responses from our experts resulted in predictions of limited efficacy of local management in combating the effects of climate change. However, there was considerable variability in expert responses and uncertainty was high. Many reefs within the central GBR appear to be at risk of further decline based on the pessimistic opinions of our expert pool. Further parameterization of the model as more data and knowledge become available could improve predictive power. Our approach serves as a starting point for subsequent work that can fine-tune parameters and explore uncertainties in predictions of responses to management.

Evidence for multiple stressor interactions and effects on coral reefs.

Concern is growing about the potential effects of interacting multiple stressors, especially as the global climate changes. We provide a comprehensive review of multiple stressor interactions in coral reef ecosystems, which are widely considered to be one of the most sensitive ecosystems to global change. First, we synthesized coral reef studies that examined interactions of two or more stressors, highlighting stressor interactions (where one stressor directly influences another) and potentially synergistic effects on response variables (where two stressors interact to produce an effect that is greater than purely additive). For stressor-stressor interactions, we found 176 studies that examined at least 2 of the 13 stressors of interest. Applying network analysis to analyze relationships between stressors, we found that pathogens were exacerbated by more costressors than any other stressor, with ca. 78% of studies reporting an enhancing effect by another stressor. Sedimentation, storms, and water temperature directly affected the largest number of other stressors. Pathogens, nutrients, and crown-of-thorns starfish were the most-influenced stressors. We found 187 studies that examined the effects of two or more stressors on a third dependent variable. The interaction of irradiance and temperature on corals has been the subject of more research (62 studies, 33% of the total) than any other combination of stressors, with many studies reporting a synergistic effect on coral symbiont photosynthetic performance (n = 19). Second, we performed a quantitative meta-analysis of existing literature on this most-studied interaction (irradiance and temperature). We found that the mean effect size of combined treatments was statistically indistinguishable from a purely additive interaction, although it should be noted that the sample size was relatively small (n = 26). Overall, although in aggregate a large body of literature examines stressor effects on coral reefs and coral organisms, considerable gaps remain for numerous stressor interactions and effects, and insufficient quantitative evidence exists to suggest that the prevailing type of stressor interaction is synergistic.