How do habitat amount and habitat fragmentation drive time-delayed responses of biodiversity to land-use change?

Accounting for imperfect detection when estimating species-area relationships and beta-diversity

Ecologists have historically quantified fundamental biodiversity patterns, including species-area relationships (SARs) and beta diversity, using observed species counts. However, imperfect detection may often bias derived community metrics and subsequent community models. Although several statistical methods claim to correct for imperfect detection, their performance in species-area and β-diversity research remains unproven. We examine inaccuracies in the estimation of SARs and β-diversity parameters that emerge from imperfect detection, and whether such errors can be mitigated using a non-parametric diversity estimator (iNEXT.3D) and Multi-Species Occupancy Models (MSOMs). We simulated 28,350 sampling regimes of 2835 fragmented communities, varying the mean and standard deviation of species detection probabilities, and the number of sampling repetitions. We then quantified the bias, accuracy, and precision of derived estimates of model coefficients for SARs and the effects of patch area on β-diversity (pairwise Sørensen similarity). Imperfect detection biased estimates of all evaluated parameters, particularly when mean detection probabilities were low, and there were few sampling repetitions. Observed counts consistently underestimated species richness and SAR z-values, and overestimated SAR c-values; iNEXT.3D and MSOMs only partially resolved these biases. iNEXT.3D provided the best estimates of SAR z-values, although MSOM estimates were generally comparable. All three methods accurately estimated pairwise Sørensen similarity in most circumstances, but only MSOMs provided unbiased estimates of the coefficients of models examining covariate effects on β-diversity. Even when using iNEXT.3D or MSOMs, imperfect detection consistently caused biases in SAR coefficient estimates, calling into question the robustness of previous SAR studies. Furthermore, the inability of observed counts and iNEXT.3D to estimate β-diversity model coefficients resulted from a systematic, area-related bias in Sørensen similarity estimates. Importantly, MSOMs corrected for these biases in β-diversity assessments, even in suboptimal scenarios. Nonetheless, as estimator performance consistently improved with increasing sampling repetitions, the importance of appropriate sampling effort cannot be understated.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Stepping stones to extirpation: Puma patch occupancy thresholds in an urban-wildland matrix

Habitat loss and fragmentation are the leading causes of species range contraction and extirpation, worldwide. Factors that predict sensitivity to fragmentation include high trophic level, large body size, and extensive spatial requirements. Pumas (Puma concolor) exemplify these qualities, making them particularly susceptible to fragmentation and subsequent reductions in demographic connectivity. The chaparral-dominated ecosystems surrounding the greater San Francisco Bay Area encompass over 10,000 km2 of suitable puma habitat, but inland waterways, croplands, urban land uses, and extensive transportation infrastructure have resulted in widespread habitat fragmentation. Pumas in this region now exist as a metapopulation marked by loss of genetic diversity, collisions with vehicles, and extensive human-puma conflict. Given these trends, we conducted a photo survey from 2017 to 2021 across 19 patches of predicted habitat and compiled a dataset of >6584 puma images. We used a logistic regression analytical framework to evaluate the hypothesis that puma patch occupancy would exhibit a threshold response explained by patch size, isolation, and habitat quality. Contrary to predictions, only variables related to patch size demonstrated any power to explain occupancy. On average, occupied patches were 18× larger than those where they were not detected (825 ± 1238 vs. 46 ± 101 km2). Although we observed pumas in patches as small as 1 km2, logistic regression models indicated a threshold occupancy probability between 300 and 400 km2, which is remarkably close to the mean male puma home range size in coastal California (~381 km2). Puma populations dependent on habitats below this value may be susceptible to inbreeding depression and human-wildlife conflict, and therefore vulnerable to extirpation. For species conservation, we suggest conflicts might be ameliorated by identifying the largest, isolated patches for public education campaigns with respect to management of domestic animals, and remaining connective parcels be identified, mapped, and prioritized for targeted mitigation.

Occurrence dynamics of mammals in protected tropical forests respond to human presence and activities

Protected areas (PAs) play a vital role in wildlife conservation. Nonetheless there is concern and uncertainty regarding how and at what spatial scales anthropogenic stressors influence the occurrence dynamics of wildlife populations inside PAs. Here we assessed how anthropogenic stressors influence occurrence dynamics of 159 mammal species in 16 tropical PAs from three biogeographic regions. We quantified these relationships for species groups (habitat specialists and generalists) and individual species. We used long-term camera-trap data (1,002 sites) and fitted Bayesian dynamic multispecies occupancy models to estimate local colonization (the probability that a previously empty site is colonized) and local survival (the probability that an occupied site remains occupied). Multiple covariates at both the local scale and landscape scale influenced mammal occurrence dynamics, although responses differed among species groups. Colonization by specialists increased with local-scale forest cover when landscape-scale fragmentation was low. Survival probability of generalists was higher near the edge than in the core of the PA when landscape-scale human population density was low but the opposite occurred when population density was high. We conclude that mammal occurrence dynamics are impacted by anthropogenic stressors acting at multiple scales including outside the PA itself.

Time-lagged effects of habitat fragmentation on terrestrial mammals in Madagascar

Biodiversity is severely threatened by habitat destruction. As a consequence of habitat destruction, the remaining habitat becomes more fragmented. This results in time-lagged population extirpations in remaining fragments when these are too small to support populations in the long term. If these time-lagged effects are ignored, the long-term impacts of habitat loss and fragmentation will be underestimated. We quantified the magnitude of time-lagged effects of habitat fragmentation for 157 nonvolant terrestrial mammal species in Madagascar, one of the biodiversity hotspots with the highest rates of habitat loss and fragmentation. We refined species' geographic ranges based on habitat preferences and elevation limits and then estimated which habitat fragments were too small to support a population for at least 100 years given stochastic population fluctuations. We also evaluated whether time-lagged effects would change the threat status of species according to the International Union for the Conservation of Nature (IUCN) Red List assessment framework. We used allometric relationships to obtain the population parameters required to simulate the population dynamics of each species, and we quantified the consequences of uncertainty in these parameter estimates by repeating the analyses with a range of plausible parameter values. Based on the median outcomes, we found that for 34 species (22% of the 157 species) at least 10% of their current habitat contained unviable populations. Eight species (5%) had a higher threat status when accounting for time-lagged effects. Based on 0.95-quantile values, following a precautionary principle, for 108 species (69%) at least 10% of their habitat contained unviable populations, and 51 species (32%) had a higher threat status. Our results highlight the need to preserve continuous habitat and improve connectivity between habitat fragments. Moreover, our findings may help to identify species for which time-lagged effects are most severe and which may thus benefit the most from conservation actions.

Occupancy winners in tropical protected forests: a pantropical analysis

The structure of forest mammal communities appears surprisingly consistent across the continental tropics, presumably due to convergent evolution in similar environments. Whether such consistency extends to mammal occupancy, despite variation in species characteristics and context, remains unclear. Here we ask whether we can predict occupancy patterns and, if so, whether these relationships are consistent across biogeographic regions. Specifically, we assessed how mammal feeding guild, body mass and ecological specialization relate to occupancy in protected forests across the tropics. We used standardized camera-trap data (1002 camera-trap locations and 2-10 years of data) and a hierarchical Bayesian occupancy model. We found that occupancy varied by regions, and certain species characteristics explained much of this variation. Herbivores consistently had the highest occupancy. However, only in the Neotropics did we detect a significant effect of body mass on occupancy: large mammals had lowest occupancy. Importantly, habitat specialists generally had higher occupancy than generalists, though this was reversed in the Indo-Malayan sites. We conclude that habitat specialization is key for understanding variation in mammal occupancy across regions, and that habitat specialists often benefit more from protected areas, than do generalists. The contrasting examples seen in the Indo-Malayan region probably reflect distinct anthropogenic pressures.

Why Do Agroforestry Systems Enhance Biodiversity? Evidence From Habitat Amount Hypothesis Predictions

Considering the present ecological crisis, land use-biodiversity relationships have become a major topic in landscape planning, ecosystem management and ecological restoration. In this scope, consistent patterns of outstanding biodiversity have been identified in agroforestry systems within diverse biogeographic regions and types of management. Empirical work has revealed that agroforestry higher structural complexity, when compared with current simplified agricultural systems, might be partially responsible for the observed patterns. The recently developed Habitat Amount Hypothesis predicts diversity for a local habitat patch, from the amount of the same habitat within the local landscape. We have expanded the previous hypothesis to the landscape level, computing the influence of the dominant land uses on the diversity of coexisting guilds. As a case study, we have considered archetypal landscapes dominated (or co-dominated) by crops or trees, which were compared using normalized diversities. The results obtained show that agroforestry systems substantially increase functional diversity and overall biodiversity within landscapes. We highlight that the normalized values should be parametrized to real conditions where the type of crop, tree and agroecological management will make a difference. Most importantly, our findings provide additional evidence that agroforestry has a critical role in enhancing biodiversity in agricultural landscapes and, in this way, should be regarded as a priority measure in European Agri-environmental funding schemes.

Can use of hydroxychloroquine and azithromycin as a treatment of COVID-19 affect aquatic wildlife? A study conducted with neotropical tadpole

The impacts on human health and the economic and social disruption caused by the pandemic COVID-19 have been devastating. However, its environmental consequences are poorly understood. Thus, to assess whether COVID-19 therapy based on the use of azithromycin (AZT) and hydroxychloroquine (HCQ) during the pandemic affects wild aquatic life, we exposed (for 72 h) neotropical tadpoles of the species Physalaemus cuvieri to the water containing these drugs to 12.5 μg/L. We observed that the increase in superoxide dismutase and catalase in tadpoles exposed to AZT (alone or in combination with HCQ) was predominant to keep the production of NO, ROS, TBARS and H₂O₂ equitable between the experimental groups. In addition, the uptake of AZT and the strong interaction of AZT with acetylcholinesterase (AChE), predicted by the molecular docking analysis, were associated with the anticholinesterase effect observed in the groups exposed to the antibiotic. However, the unexpected increase in butyrylcholinesterase (BChE) in these same groups suggests its constitutive role in maintaining cholinergic homeostasis. Therefore, taken together, our data provide a pioneering evidence that the exposure of P. cuvieri tadpoles to AZT (alone or in combination with HCQ) in a predictably increased environmental concentration (12.5 μg/L) elicits a compensatory adaptive response that can have, in the short period of exposure, guaranteed the survival of the animals. However, the high energy cost for maintaining physiological homeostasis, can compromise the growth and development of animals and, therefore, in the medium-long term, have a general negative effect on the health of animals. Thus, it is possible that COVID-19 therapy, based on the use of AZT, affects wild aquatic life, which requires greater attention to the impacts that this drug may represent.

How do habitat amount and habitat fragmentation drive time-delayed responses of biodiversity to land-use change?

Land-use change is a root cause of the extinction crisis, but links between habitat change and biodiversity loss are not fully understood. While there is evidence that habitat loss is an important extinction driver, the relevance of habitat fragmentation remains debated. Moreover, while time delays of biodiversity responses to habitat transformation are well-documented, time-delayed effects have been ignored in the habitat loss versus fragmentation debate. Here, using a hierarchical Bayesian multi-species occupancy framework, we systematically tested for time-delayed responses of bird and mammal communities to habitat loss and to habitat fragmentation. We focused on the Argentine Chaco, where deforestation has been widespread recently. We used an extensive field dataset on birds and mammals, along with a time series of annual woodland maps from 1985 to 2016 covering recent and historical habitat transformations. Contemporary habitat amount explained bird and mammal occupancy better than past habitat amount. However, occupancy was affected more by the past rather than recent fragmentation, indicating a time-delayed response to fragmentation. Considering past landscape patterns is therefore crucial for understanding current biodiversity patterns. Not accounting for land-use history ignores the possibility of extinction debt and can thus obscure impacts of fragmentation, potentially explaining contrasting findings of habitat loss versus fragmentation studies.