Ecosystem stability at the landscape scale is primarily associated with climatic history

There is an increasing interest in landscape-scale perspectives of ecosystem functioning to inform policy and conservation decisions. However, we need a better understanding of the stability of ecosystem functioning (e.g. plant productivity) at the landscape scale to inform policy around topics such as global food security. We investigate the role of the ecological and environmental context on landscape-scale stability of plant productivity in agricultural pasture using remotely sensed enhanced vegetation index data. We determine whether four measures of stability (variability, magnitude of extreme anomalies, recovery time and recovery rate) are predicted by (a) species richness of vascular plants, (b) regional land cover heterogeneity and (c) climatic history. Stability of plant productivity was primarily associated with climatic history, particularly a history of extreme events. These effects outweighed any positive effects of species richness in the agricultural landscape. A history of variable and extreme climates both increased and decreased contemporary ecosystem stability, suggesting both cumulative and legacy effects, whereas land cover heterogeneity had no effect on stability. The landscape scale is a relevant spatial scale for the management of an ecosystem's stability. At this scale, we find that past climate is a stronger driver of stability in plant productivity than species richness, differing from results at finer field scales. Management should take an integrated approach by incorporating the environmental context of the landscape, such as its climatic history, and consider multiple components of stability to maintain functioning in landscapes that are particularly vulnerable to environmental change

Monitoring migration timing in remote habitats: assessing the value of extended duration audio recording

Because birds are frequently detected by sound, autonomous audio recorders (called automated recording units or ARUs) are now an established tool in addition to in-person observations for monitoring the status and trends of bird populations. ARUs have been evaluated and applied during breeding seasons, and to monitor the nocturnal flight calls of migrating birds. However, birds behave differently during migration than during the breeding season. Here we present a method for using ARUs to monitor land birds during the migration period in remote habitats. We conducted in-person point counts next to continuously recording ARUs, and compared estimates of the number of species detected and focal species relative abundance from point counts and ARUs. We used a desk-based audio bird survey method for processing audio recordings, which does not require automated species identification algorithms. We tested two methods of using extended duration ARU recording: surveying consecutive minutes and surveying randomly selected minutes. Desk-based surveys using randomly selected minutes from extended duration ARU recordings performed similarly to point counts, and better than desk-based surveys using consecutive minutes from ARU recordings. Surveying randomly selected minutes from ARUs provided estimates of relative abundance that were strongly correlated with estimates from point counts and successfully showed the increase in abundance associated with migration timing. Randomly selected minutes also provided estimates of the number of species present that were comparable to estimates from point counts. Our results suggest that ARUs are an effective way to track migration timing and intensity in remote or seasonally inaccessible habitat during spring migration. Additional testing is needed to determine the efficacy of our methods during fall migration, and at more southerly latitudes. We recommend that desk-based surveys use randomly sampled minutes from extended duration ARU recordings, rather than using consecutive minutes from recordings. Our methods can be immediately applied by researchers with the skills to conduct point counts, with no additional expertise necessary in automated species identification algorithms

Land cover drives large scale productivity-diversity relationships in Irish vascular plants

The impact of productivity on species diversity is often studied at small spatial scales and without taking additional environmental factors into account. Focusing on small spatial scales removes important regional scale effects, such as the role of land cover heterogeneity. Here, we use a regional spatial scale (10 km square) to establish the relationship between productivity and vascular plant species richness across the island of Ireland that takes into account variation in land cover. We used generalized additive mixed effects models to relate species richness, estimated from biological records, to plant productivity. Productivity was quantified by the satellite-derived enhanced vegetation index. The productivity-diversity relationship was fitted for three land cover types: pasture-dominated, heterogeneous, and non-pasture-dominated landscapes. We find that species richness decreases with increasing productivity, especially at higher productivity levels. This decreasing relationship appears to be driven by pasture-dominated areas. The relationship between species richness and heterogeneity in productivity (both spatial and temporal) varies with land cover. Our results suggest that the impact of pasture on species richness extends beyond field level. The effect of human modified landscapes, therefore, is important to consider when investigating classical ecological relationships, particularly at the wider landscape scale.Science Foundation Irelan

What have biological records ever done for us? A systematic scoping review

Biological records provide biodiversity information over large spatial and temporal scales.  Our systematic scoping review of biological records from the well-recorded region of the United Kingdom (UK) and Ireland revealed that over half of all studies using biological records were studying species distributions (134 of 253 studies) and/or temporal trends (139 of 253 studies).  A minority of studies (61 of 253) focused on methodological questions, while most studies used biological records with existing methods as tools for answering biological and ecological questions.  However, only 31 of 253 studies tested models using independent data.  Most studies (154 of 253) integrated multiple biological records datasets, showing that biological records hold a largely untapped potential for independently testing conclusions by withholding some of those datasets for use as independent test data.  Our results provide guidance for data providers and researchers interested in more effectively collecting and using biological records

What have biological records ever done for us? A systematic scoping review

Biological records provide biodiversity information over large spatial and temporal scales.  Our systematic scoping review of biological records from the well-recorded region of the United Kingdom (UK) and Ireland revealed that over half of all studies using biological records were studying species distributions (134 of 253 studies) and/or temporal trends (139 of 253 studies).  A minority of studies (61 of 253) focused on methodological questions, while most studies used biological records with existing methods as tools for answering biological and ecological questions.  However, only 31 of 253 studies tested models using independent data.  Most studies (154 of 253) integrated multiple biological records datasets, showing that biological records hold a largely untapped potential for independently testing conclusions by withholding some of those datasets for use as independent test data.  Our results provide guidance for data providers and researchers interested in more effectively collecting and using biological records

Modelling the distribution of rare invertebrates by correcting class imbalance and spatial bias

Aim Soil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (Diversity and Distributions, 24, 460) proposed a method for under-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatially stratified under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. Location Island of Ireland. Methods We tested the spatially stratified under-sampling method of Robinson et al. (Diversity and Distributions, 24, 460) by using biological records to train species distribution models of rare millipedes. Results Using spatially stratified under-sampled data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The spatial pattern of under-sampling affected model performance. Training data that was under-sampled in a spatially stratified way sometimes produced worse models than did data that was under-sampled in an unstratified way. Geographic coordinates were as good as or better than environmental variables for predicting distributions of one out of six species. Main Conclusions Spatially stratified under-sampling improved prediction performance of species distribution models for rare millipedes. Spatially stratified under-sampling was most effective for rarer species, although unstratified under-sampling was sometimes more effective. The good prediction performance of models using geographic coordinates is promising for modelling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence