Lucinda Kirkpatrick

Bio: Lucinda Kirkpatrick is an academic researcher from Imperial College London. The author has contributed to research in topics: Biodiversity & Habitat. The author has an hindex of 2, co-authored 2 publications receiving 1943 citations.

Global effects of land use on local terrestrial biodiversity

Abstract: Human activities, especially conversion and degradation of habitats, are causing global biodiversity declines. How local ecological assemblages are responding is less clear--a concern given their importance for many ecosystem functions and services. We analysed a terrestrial assemblage database of unprecedented geographic and taxonomic coverage to quantify local biodiversity responses to land use and related changes. Here we show that in the worst-affected habitats, these pressures reduce within-sample species richness by an average of 76.5%, total abundance by 39.5% and rarefaction-based richness by 40.3%. We estimate that, globally, these pressures have already slightly reduced average within-sample richness (by 13.6%), total abundance (10.7%) and rarefaction-based richness (8.1%), with changes showing marked spatial variation. Rapid further losses are predicted under a business-as-usual land-use scenario; within-sample richness is projected to fall by a further 3.4% globally by 2100, with losses concentrated in biodiverse but economically poor countries. Strong mitigation can deliver much more positive biodiversity changes (up to a 1.9% average increase) that are less strongly related to countries' socioeconomic status.

A global model of the response of tropical and sub-tropical forest biodiversity to anthropogenic pressures

Abstract: Habitat loss and degradation, driven largely by agricultural expansion and intensification, present the greatest immediate threat to biodiversity. Tropical forests harbour among the highest levels of terrestrial species diversity and are likely to experience rapid land-use change in the coming decades. Synthetic analyses of observed responses of species are useful for quantifying how land use affects biodiversity and for predicting outcomes under land-use scenarios. Previous applications of this approach have typically focused on individual taxonomic groups, analysing the average response of the whole community to changes in land use. Here, we incorporate quantitative remotely sensed data about habitats in, to our knowledge, the first worldwide synthetic analysis of how individual species in four major taxonomic groups—invertebrates, ‘herptiles’ (reptiles and amphibians), mammals and birds—respond to multiple human pressures in tropical and sub-tropical forests. We show significant independent impacts of land use, human vegetation offtake, forest cover and human population density on both occurrence and abundance of species, highlighting the value of analysing multiple explanatory variables simultaneously. Responses differ among the four groups considered, and—within birds and mammals—between habitat specialists and habitat generalists and between narrow-ranged and wide-ranged species.

Sonotype‐level responses of Afrotropical hipposiderid bats to local‐scale effects of rainforest structure

Abstract: Bats exhibit a variety of life-history traits that can serve as valuable surrogate metrics of terrestrial ecosystem health. Here, we investigate how sonotype activity of hipposiderid bats covaries with habitat structure at finer spatial scales. We recorded passive echolocation calls and measured key habitat attributes in six rainforests in the Lomami and Yangambi landscapes, Democratic Republic of the Congo. Using bat passes as a measure of sonotype activity, we clustered echolocation calls based on call structure similarity to control for within-sonotype variation in activity. Over 432 h of recording, we detected 370 passes matching a hipposiderid sonotype in three subgroups, recovering eight potential species. Open habitats negatively affected sonotype activity in the Hipposideros subgroup, which was associated with higher echolocation frequencies. Indeed, activity peaked in the early evening when mean post-sunset temperature was above the nocturnal average and declined until early morning when mean temperatures dropped below the nightly average. All habitat variables were marginally correlated with the activity of the Doryrhina subgroup, whereas Macronycteris was more active in open habitats. Our findings indicate a probable flexibility of habitat use in lower echolocating bats and point to three possible foraging guilds that modulate hipposiderid bat responses to habitat structure. Les chauves-souris présentent une variété de traits d'histoire de vie qui peuvent servir de métriques de substitution précieuses pour la santé des écosystèmes terrestres. Ici, nous étudions comment l'activité des sonotypes de chauves-souris Hipposideridae covarie avec la structure de l'habitat à des échelles spatiales plus fines. Nous avons enregistré des appels d'écholocalisation passive et mesuré les principaux attributs de l'habitat dans six forêts pluviales des paysages de Lomami et de Yangambi, en République démocratique du Congo. En utilisant les passages de chauves-souris comme mesure de l'activité du sonotype, nous avons regroupé les appels d'écholocalisation sur la base de la similarité de la structure des appels afin de contrôler la variation de l'activité au sein du sonotype. Au cours de 432 heures d'enregistrement, nous avons détecté 370 passages correspondant à un sonotype d'Hipposideridae en trois sous-groupes, composés de huit espèces potentielles. Les habitats ouverts ont eu un impact négatif sur l'activité des sonotypes dans le sous-groupe desHipposiderosqui était associé à des fréquences d'écholocalisation plus élevées. En effet, l'activité a été maximale en début de soirée, lorsque la température moyenneaprès le coucher du soleil était supérieure à la moyenne nocturne et a diminué jusqu'au début de la matinée, lorsque les températures moyennes ont chuté en dessous de la moyenne nocturne. Toutes les variables relatives à l'habitat étaient en corrélation marginale avec l'activité du sous-groupe de Doryrhina , tandis que les Macronycteris étaient plus actifs dans des habitats ouverts. Nos résultats indiquent une flexibilité probable de l'utilisation de l'habitat chez les chauves-souris écholocatrices inférieures et soulignent trois guildes de recherche de nourriture possibles qui modulent les réponses des chauves-souris Hipposideridae à la structure de l'habitat. The authors declare no conflict of interest. The data supporting the findings of this study are available from the corresponding author upon reasonable request. Additional supporting information is available online in the Supporting Information section at the end of the article. Data S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Incorporating space in hierarchical capture mark recapture models: can we better capture variance?

Abstract: Capture mark recapture (CMR) models allow the estimation of various components of animal populations, such as survival and recapture probabilities. In recent years, incorporating the spatial distribution of the devices used to detect an animals’ presence has become possible. By incorporating spatial information, we explicitly acknowledge the fact that there will be spatial structuring in the ecological processes which give rise to the capture data. Individual detection probability is not heterogeneous for a range of different reasons, for example the location of traps within an individual’s home range, the environmental context around the trap or the individual characteristics of an animal such as its age. Spatial capture recapture models incorporate this heterogeneity by including the spatial coordinates of traps, data which is often already collected in standard CMR approaches. Here, we compared how the inclusion of spatial data changed estimations of survival, detection probability, and to some extent the probability of seroconversion to a common arenavirus, using the multimammate mouse as our model system. We used a Bayesian framework to develop non spatial, partially spatial and fully spatial models alongside multievent CMR models. First, we used simulations to test whether certain parameters were sensitive to starting parameters, and whether models were able to return the expected values. Then we applied the non-spatial, partially spatial and fully spatial models to a real dataset. We found that bias and precision were similar for the three different model types, with simulations always returning estimates within the 95% credible intervals. When applying our models to the real data set, we found that the non-spatial model predicted a lower survival of individuals exposed to Morogoro virus (MORV) compared to unexposed individuals, yet in the spatial model survival between exposed and non-exposed individuals was the same. This suggests that the non-spatial model underestimated the survival of seropositive individuals, most likely due to an age effect. We suggest that spatial coordinates of traps should always be recorded when carrying out CMR and spatially explicit methods of analysis should be used whenever possible, particularly as incorporating spatial variation may more easily capture ecological processes without the need for additional data collection that can be challenging to acquire with wild animals.

Global effects of land use on local terrestrial biodiversity

Abstract: Human activities, especially conversion and degradation of habitats, are causing global biodiversity declines. How local ecological assemblages are responding is less clear--a concern given their importance for many ecosystem functions and services. We analysed a terrestrial assemblage database of unprecedented geographic and taxonomic coverage to quantify local biodiversity responses to land use and related changes. Here we show that in the worst-affected habitats, these pressures reduce within-sample species richness by an average of 76.5%, total abundance by 39.5% and rarefaction-based richness by 40.3%. We estimate that, globally, these pressures have already slightly reduced average within-sample richness (by 13.6%), total abundance (10.7%) and rarefaction-based richness (8.1%), with changes showing marked spatial variation. Rapid further losses are predicted under a business-as-usual land-use scenario; within-sample richness is projected to fall by a further 3.4% globally by 2100, with losses concentrated in biodiverse but economically poor countries. Strong mitigation can deliver much more positive biodiversity changes (up to a 1.9% average increase) that are less strongly related to countries' socioeconomic status.

The biomass distribution on Earth

Abstract: A census of the biomass on Earth is key for understanding the structure and dynamics of the biosphere. However, a global, quantitative view of how the biomass of different taxa compare with one another is still lacking. Here, we assemble the overall biomass composition of the biosphere, establishing a census of the ≈550 gigatons of carbon (Gt C) of biomass distributed among all of the kingdoms of life. We find that the kingdoms of life concentrate at different locations on the planet; plants (≈450 Gt C, the dominant kingdom) are primarily terrestrial, whereas animals (≈2 Gt C) are mainly marine, and bacteria (≈70 Gt C) and archaea (≈7 Gt C) are predominantly located in deep subsurface environments. We show that terrestrial biomass is about two orders of magnitude higher than marine biomass and estimate a total of ≈6 Gt C of marine biota, doubling the previous estimated quantity. Our analysis reveals that the global marine biomass pyramid contains more consumers than producers, thus increasing the scope of previous observations on inverse food pyramids. Finally, we highlight that the mass of humans is an order of magnitude higher than that of all wild mammals combined and report the historical impact of humanity on the global biomass of prominent taxa, including mammals, fish, and plants.

Options for keeping the food system within environmental limits

Abstract: The food system is a major driver of climate change, changes in land use, depletion of freshwater resources, and pollution of aquatic and terrestrial ecosystems through excessive nitrogen and phosphorus inputs. Here we show that between 2010 and 2050, as a result of expected changes in population and income levels, the environmental effects of the food system could increase by 50–90% in the absence of technological changes and dedicated mitigation measures, reaching levels that are beyond the planetary boundaries that define a safe operating space for humanity. We analyse several options for reducing the environmental effects of the food system, including dietary changes towards healthier, more plant-based diets, improvements in technologies and management, and reductions in food loss and waste. We find that no single measure is enough to keep these effects within all planetary boundaries simultaneously, and that a synergistic combination of measures will be needed to sufficiently mitigate the projected increase in environmental pressures.

Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation

Abstract: Human pressures on the environment are changing spatially and temporally, with profound implications for the planet’s biodiversity and human economies. Here we use recently available data on infrastructure, land cover and human access into natural areas to construct a globally standardized measure of the cumulative human footprint on the terrestrial environment at 1 km2 resolution from 1993 to 2009. We note that while the human population has increased by 23% and the world economy has grown 153%, the human footprint has increased by just 9%. Still, 75% the planet’s land surface is experiencing measurable human pressures. Moreover, pressures are perversely intense, widespread and rapidly intensifying in places with high biodiversity. Encouragingly, we discover decreases in environmental pressures in the wealthiest countries and those with strong control of corruption. Clearly the human footprint on Earth is changing, yet there are still opportunities for conservation gains. Habitat loss and urbanization are primary components of human impact on the environment. Here, Venter et al.use global data on infrastructure, agriculture, and urbanization to show that the human footprint is growing slower than the human population, but footprints are increasing in biodiverse regions.

Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

Abstract: Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.