With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise. Species Distribution Models in EcologyLarge-scale ecological models of how species distributions and abundances vary over space and time are a critical tool in macroecology, biogeography, and conservation biology. They underpin our understanding of how biodiversity is shaped, how it is responding to anthropogenic activities, and how it might change in the future [1][2][3]. There is now a substantial literature on statistical tools for building species distribution models (SDMs) (see Glossary) and best practice in how to fit them [4][5][6][7]. SDMs also form a building block upon which more complex models, incorporating occupancy and/or abundance in space and time, can be built [8,9].

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence-only dataset with a structured, presence-absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence-only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence-absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources.

Climate warming has caused the seasonal timing of many components of ecological food chains to advance (Thackeray et al. 2010(Thackeray et al. , 2016. In the context of trophic interactions the match-mismatch hypothesis (MMH) postulates that differential shifts can lead to phenological asynchrony with negative impacts for consumers (Cushing 1990). However, it is still largely unresolved whether unequal trophic shifts are adaptive or constrained by

Climate change has been shown to induce shifts in the timing of life‐history events. As a result, interactions between species can become disrupted, with potentially detrimental effects. Predicting these consequences has proven challenging. We apply structured population models to a well‐characterised great tit‐caterpillar model system and identify thresholds of temporal asynchrony, beyond which the predator population will rapidly go extinct. Our model suggests that phenotypic plasticity in predator breeding timing initially maintains temporal synchrony in the face of environmental change. However, under projections of climate change, predator plasticity was insufficient to keep pace with prey phenology. Directional evolution then accelerated, but could not prevent mismatch. Once predator phenology lagged behind prey by more than 24 days, rapid extinction was inevitable, despite previously stable population dynamics. Our projections suggest that current population stability could be masking a route to population collapse, if high greenhouse gas emissions continue.

For organisms living in seasonal environments, synchronizing the peak energetic demands of reproduction with peak food availability is a key challenge. Understanding the extent to which animals can adjust behavior to optimize reproductive timing, and the cues they use to do this, is essential for predicting how they will respond to future climate change. In birds, the timing of peak energetic demand is largely determined by the timing of clutch initiation; however, considerable alterations can still occur once egg laying has begun. Here, we use a wild population of great tits (Parus major) to quantify individual variation in different aspects of incubation behavior (onset, duration, and daily intensity) and conduct a comprehensive assessment of the causes and consequences of this variation. Using a 54‐year dataset, we demonstrate that timing of hatching relative to peak prey abundance (synchrony) is a better predictor of reproductive success than clutch initiation or clutch completion timing, suggesting adjustments to reproductive timing via incubation are adaptive in this species. Using detailed in‐nest temperature recordings, we found that postlaying, birds improved their synchrony with the food peak primarily by varying the onset of incubation, with duration changes playing a lesser role. We then used a sliding time window approach to explore which spring temperature cues best predict variance in each aspect of incubation behavior. Variation in the onset of incubation correlated with mean temperatures just prior to laying; however, incubation duration could not be explained by any of our temperature variables. Daily incubation intensity varied in response to daily maximum temperatures throughout incubation, suggesting female great tits respond to temperature cues even in late stages of incubation. Our results suggest that multiple aspects of the breeding cycle influence the final timing of peak energetic demand. Such adjustments could compensate, in part, for poor initial timing, which has significant fitness impacts.

Changes in the timing of life‐history events (phenology) are a widespread consequence of climate change. Predicting population resilience requires knowledge of how phenology is likely to change over time, which can be gained by identifying the specific environmental cues that drive phenological events. Cue identification is often achieved with statistical testing of candidate cues. As the number of methods used to generate predictions increases, assessing the predictive accuracy of different approaches has become necessary. This study aims to (a) provide an empirical illustration of the predictive ability of five commonly applied statistical methods for cue identification (absolute and relative sliding time window analyses, penalized signal regression, climate sensitivity profiles and a growing degree‐day model) and (b) discuss approaches for implementing cue identification methods in different systems. Using a dataset of mean clutch initiation timing in wild great tits (Parus major), we explored how the days of the year identified as most important, and the aggregate statistic identified as a cue, differed between statistical methods and with respect to the time span of data used. Each method's predictive capacity was tested using cross‐validation and assessed for robustness to varying sample size. We show that the identified critical time window of cue sensitivity was consistent across four of the five methods. The accuracy and precision of predictions differed by method with penalized signal regression resulting in the most accurate and most precise predictions in our case. Accuracy was maximal for near‐future predictions and showed a relationship with time. The difference between predictions and observations systematically shifted across the study from preceding observations to lagging. This temporal trend in prediction error suggests that the current statistical tools either fail to capture a key component of the cue–phenology relationship, or the relationship itself is changing through time in our system. These two influences need to be teased apart if we are to generate realistic predictions of phenological change. We recommend future phenological studies to challenge the idea of a static cue–phenology relationship and should cross‐validate results across multiple time periods.

Global climate change is altering the timing of life history events for species living in seasonal environments. These shifts in phenology can lead to the disruption of interspecific relationships with implications for individual fitness. Predicting phenological change and its population level consequences can provide insights into population persistence. Achieving this is challenging for labile traits as current structured population models do not explicitly distinguish between the roles of phenotypic plasticity and micro‐evolution, hindering realistic predictions of trait change. In this study we present the first empirical test of a new integral projection model (IPM) framework, which allows phenotypic plasticity and micro‐evolution to be teased apart by incorporating a quantitative genetic inheritance function. We parameterise this model for a population of wild great tits Parus major and test its predictive capabilities through K‐fold cross validation. We test the predictive accuracy of the quantitative genetic IPM in comparison to the standard IPM. We demonstrate that adding genetic inheritance rules maintains high accuracy of projections of phenological change, relative to the standard IPM. In addition, we find almost identical projections of population dynamics in this population for both IPMs, demonstrating that this model formulation allows researchers to investigate the contributions of phenotypic plasticity and micro‐evolution to trait change, without sacrificing predictive accuracy. Modelling in this way reveals that, under directional environmental change, both micro‐evolution and plasticity contribute to an advance of phenology, although the effect of plasticity is an order of magnitude higher than evolution. Despite this, synchrony between great tits and their caterpillar prey was reduced and population declines occurred. Our approach demonstrates that this model framework provides a promising avenue through which to explore the roles of phenotypic plasticity and evolution in trait changes and population dynamics.

Inferences drawn from long-term field studies are vulnerable to biases in observability of different classes of individuals, which may lead to biases in the estimates of selection, or fitness. Population surveys that monitor breeding individuals can introduce such biases by not identifying individuals that fail early in their reproductive attempts. Here, we quantify how the standard protocol for detecting breeding females introduces bias in a long-term population study of the great tit, Parus major. We do so by identifying females whose breeding attempts fail before they would normally be censused and explore whether this early failure can be predicted by a number of intrinsic and extrinsic factors. We investigate the effect of these biases on estimates of reproductive performance and selection. We show that females that go undetected by standard censusing because they fail early in their breeding attempt were less likely to have been previously trapped within our study site and were more likely to breed in poor-quality habitats. Furthermore, we demonstrate that this bias sampling had lead previous studies on this population to overestimate the reproductive performance of unringed females, which are likely to be immigrants to the population. Finally, we show that these biases in detectability influence estimates of selection on a key life-history trait. While these conclusions are specific to this study, we suggest that such effects are likely to be widespread and that more attention should be given to whether or not methods for surveying natural populations introduce systematic bias that will influence conclusions about ecological and evolutionary processes.