In the face of rapid global change and an uncertain fate for biodiversity, it is vital to quantify trends in wild populations. These trends are typically estimated from abundance time series for suites of species across large geographic and temporal scales. Such data implicitly contain phylogenetic, spatial, and temporal structure which, if not properly accounted for, may obscure the true magnitude and direction of biodiversity change. Here, using a novel statistical framework to simultaneously account for all three of these structures, we show that the majority of current abundance trends estimates among 10 high-profile datasets, representing millions of abundance observations, are likely unreliable or incorrect. Our new approach suggests that previous models are too simplistic, incorrectly estimating global abundance trends and often dramatically underestimating uncertainty, an aspect that is critical when translating global assessments into policy outcomes. Further, our approach also results in substantial improvements in abundance forecasting accuracy. Whilst our results do not improve the outlook for biodiversity, our framework does allow us to make more robust estimates of global wildlife abundance trends, which is critical for developing policy to protect our biosphere.

The parasitic weed genus Striga causes huge losses to crop production in sub-Saharan Africa, estimated to be in excess of $7 billion per year, affecting subsistence farmers who frequently lack access to novel technologies proposed for control. Effective Striga management therefore requires the development of strategies utilising existing cultural and management practices. We report a multi-year, landscape-scale monitoring project for Striga asiatica in the mid-west of Madagascar, undertaken over 2019-2020 with the aims of examining cultural, climatic and edaphic factors currently driving abundance and distribution. Long-distance transects were established across the middle-west region of Madagascar, over which Striga asiatica abundance in fields was estimated. Analysis of the data highlights the importance of crop variety and legumes in driving Striga density. Moreover, the dataset revealed significant effect of precipitation seasonality, mean temperature and altitude in determining abundance. A composite management index indicated the effect of a range of cultural practices on changes in Striga abundance. The findings support the assertion that single measures are not sufficient for the effective, long-term management of Striga. Furthermore, the composite score has potential as a significant guide of ISM control beyond the geographic range of this study.

A recent paper by Mori [ 1 Mori A.S. Resilience in the studies of biodiversity-ecosystem functioning. Trends Ecol. Evol. 2016; 31: 87-89 Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar ] states the need for a unification of studies of 'engineering' and 'ecological' frameworks of resilience. Engineering resilience focuses on the capacity of a system to recover to equilibrium following some kind of perturbation, while ecological resilience (ER) explicitly recognizes multiple stable states and the capacity for systems to resist 'regime shifts' between alternative states. We find Mori's argument somewhat surprising given the number of recent biodiversity–ecosystem functioning (B-EF) studies that incorporate aspects of both resistance and recovery (e.g., see references in [ 2 Nimmo D.G. et al. Vive la résistance: reviving resistance for 21st century conservation. Trends Ecol. Evol. 2015; 30: 516-523 Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar , 3 Oliver T.H. et al. Biodiversity and the resilience of ecosystem services. Trends Ecol. Evol. 2015; 30: 673-684 Abstract Full Text Full Text PDF PubMed Scopus (693) Google Scholar ]). We would argue that a synthesis is well underway and that apparent discrepancies are more due to differences in the spatial, temporal, and systems scale of focus and ambiguities in defining this study context rather than any fundamental incompatibilities in conceptual frameworks.

Accelerating rates of environmental change and the continued loss of global biodiversity threaten functions and services delivered by ecosystems. Much ecosystem monitoring and management is focused on the provision of ecosystem functions and services under current environmental conditions, yet this could lead to inappropriate management guidance and undervaluation of the importance of biodiversity. The maintenance of ecosystem functions and services under substantial predicted future environmental change (i.e., their 'resilience') is crucial. Here we identify a range of mechanisms underpinning the resilience of ecosystem functions across three ecological scales. Although potentially less important in the short term, biodiversity, encompassing variation from within species to across landscapes, may be crucial for the longer-term resilience of ecosystem functions and the services that they underpin.

1. Species distribution models are static models for the distribution of a species, based on Hutchinson's niche concept. They make probabilistic predictions about the distribution of a species, but do not have a temporal interpretation. In contrast, density-structured models based on categorical abundance data make it possible to incorporate population dynamics into species distribution modelling. 2. Using dynamic species distribution models, temporal aspects of a species' distribution can be investigated, including the predictability of future abundance categories and the expected persistence times of local populations, and how these may respond to environmental or anthropogenic drivers. 3. We built density-structured models for two intertidal marine invertebrates, the Lusitanian trochid gastropods Phorcus lineatus and Gibbula umbilicalis, based on 9 years of field data from around the United Kingdom. Abundances were recorded on a categorical scale, and stochastic models for year-to-year changes in abundance category were constructed with winter mean sea surface temperature (SST) and wave fetch (a measure of the exposure of a shore) as explanatory variables. 4. Both species were more likely to be present at sites with high SST, but differed in their responses to wave fetch. Phorcus lineatus had more predictable future abundance and longer expected persistence times than G. umbilicalis. This is consistent with the longer lifespan of P. lineatus. 5. Where data from multiple time points are available, dynamic species distribution models of the kind described here have many applications in population and conservation biology. These include allowing for changes over time when combining historical and contemporary data, and predicting how climate change might alter future abundance conditional on current distributions.