Existence, persistence and inference of demersal biodiversity hotspots in the BCLME

Transcription

1 Existence, persistence and inference of demersal biodiversity hotspots in the BCLME Stephen P Kirkman 1, Dawit Yemane 2, John Kathena 3, Samuel K. Mafwila 4, Silvi E. Nsiangango 5, Toufiek Samaai 1, Bjorn Axelsen 6, Larvika Singh 2 1 Department of Environmental Affairs, Oceans and Coasts Research, P.O.Box 52126, Victoria & Alfred Waterfront, Cape Town 8000; 3 Fisheries Management, Department of Agriculture, Forestry and Fisheries, Foretrust Building, Foreshore, Rogge Bay 8012, Cape Town, South Africa; 3 NaZonal Marine InformaZon and Research Center, P.O.Box 912, Swakopmund, Namibia; 4 6 Department of Fisheries and AquaZc Sciences, University of Namibia, Mandume Ndemufayo Avenue, Windhoek, Namibia; 5 InsZtuto Nacional de InvesZgação Pesqueira, Ministério da Agricultura e Desenvolvimento Rural e Pescas, Angola, Ilha de Luanda, CP 2601 Luanda, Angola; 6 InsZtute for Marine Research, P. O.Box 1870 Nordnes, N Bergen, Norway

2 INTRODUCTION Biological hotspots in the marine realm generally refer to localised areas of biological importance, associated with key processes e.g. spawning, nursery or feeding areas, or with significantly elevated levels of produccvity/biodiversity relacve to surrounding seascape Given their potencal (supporcng human livelihoods / conservacon of biodiversity and natural processes), idencfying biological hotspots and determining which factors govern and maintain them is a growing area of research In this study, biodiversity characterisccs were used to define hotspots

3 Climate change regarded as major threat to global biodiversity: - localized/regional reduccons in biodiversity levels, and distribuconal shirs, range expansions/contraccons of many organisms associated with climacc stress

4 High levels of species richness typically associated with more efficient use of resources and greater capacity to ensure ecosystem stability under disturbance/stress caused by climacc or other variability Considering this (and also to maximise biodiversity conserved within minimum areas), marine biodiversity hotspots may be obvious locacons for conservacon efforts e.g. spacal proteccon In this case understanding of hotspots including spacal and temporal determinants is potencally key for biodiversity conservacon especially in light of climate and other global changes A key requirement of hotspots with regard to conservacon relevance and in terms of their capacity for sustaining representacve levels of biodiversity, is that they should be persistent over Cme

5 In the BCLME, studies show changes in temperature in large areas in recent decades Since 1950s, wide variety of changes in populacons, ecosystem structure and funcconing, related to changes in environmental condicons or fishing ~ Includes changes in distribucon of several marine organisms that occur/feed in pelagic waters, and structure of pelagic communices Examples: - Jellyfish in northern Benguela - Eastward shir of pelagics in Southern Benguela - Effects on distribucon and abundance of top predators Mathieu Rouault (pers. comm)

6 ~ Such changes not limited to pelagic environment: - DistribuConal shirs of west coast rock lobster, inshore southern Benguela - Changes in the structure of demersal fish assemblages in southern Benguela - In a parallel NansClim study mulcvariate analyses have indicated disconcnuices in demersal community structure and populacon changes, throughout the BCLME - Another parallel NansClim study has shown changes in range size and distribucon of several demersal spp throughout BCLME It is of interest to determine whether such shirs will be reflected in spaco- temporal changes in species richness pa`erns including hotspots

7 AIMS To determine the most appropriate techniques for idenzfying hotspots based on species distribuzonal data (methodological aim) To assess the persistence of demersal biodiversity hotspots in space and Zme To characterise hotspots in terms of physical/environmental associazons Based on results of the above, to discuss the relevance of demersal biodiversity hotspots in the BCLME with regard to effects of climate variability and other changes

8 Data colleccon Fishery- independent data from swept- area demersal biomass assessment surveys Nansen Programme and related programmes in region summer surveys only Presence/absence per species per stazon and boiom temperature of interest to this study Summer surveys only - consistency Angola: (RV Dr Fridtjof Nansen): 2768 stazons Namibia: (RV Dr Fridtjof Nansen), (Blue Sea II) : 4219 stazons South Africa: (RV Africana): 2528 stazons Angola and Namibia: boiom trawls at m, locazons followed semi- random distribuzon based on 10x10 min grid SA: boiom trawls at m, locazons followed pseudo- random distribuzon based on 5x5 min grid Depth Ang. Nam. S.A.

9 Data analysis Data for the three countries were analysed separately - Differences in sampling design between countries - Differing lengths of Cme series Used demersal fish data only - Most reliable data - The heterogeneous nature of fish distribucons can make them relacvely effeccve surrogates for other marine taxa Determined species richness (SR) per trawl stacon For each country, horizontal maps of SR generated for the encre Cme series and for smaller periods (5 years, unless there less than two complete surveys within 5 years)

10 Three approaches to generate hotspot maps applied and compared: - Inverse Distance WeighCng (IDW) - a non- geospacal technique where interpolacon is performed without taking the spacal structure of the data into account - Kriging with External DriR (KED), a geospacal method to model large- scale trends in spacal data - StaCsCcal models GLM/GAM using longitude, lactude and bathymetric data (GEBCO) for the encre study grid as predictors. Final Model seleccon using AIC SpaCal areas were considered hotspots if their diversity was in the top 5% (diversity >=95% quancle)

11 Leave- One- Out- Cross- ValidaCon (LOOCV) performed for each technique 3 performance stacsccs were used to cross- compare the residuals (true value - escmated value) obtained from LOOCV between techniques - Mean error (ME) of the residuals - Root mean squared error (RMSE) of the residuals - Inter- quarcle range (IQR) of residuals [3 quarcle 1 st quarcle] Analyses in R (R Development Core team) R packages: gstat (Pebesma 2004) SoDA (Chambers 2012) larce (Sarkar 2008) mgcv (Wood 2004; Wood 2006)

12 Cross-validation table RESULTS and DISCUSSION

13 GAMs spp. richness response curve for depth In general, higher SR associated with deeper water, but depth- dependent SR varied among the 3 countries: - Angola: SR showed 2 peaks with depth - SA: similar to Angola but peaking at different depths - Namibia: SR consistently increased up to a depth of 400m aker which it declined slightly

14 Box- Whisker plots of bo`om temperature associated with different quancles of SR according to GAMs prediccons. Hotspot areas generally associated with cooler temperatures and coldspot areas with warmer temperatures Only in Namibia was there a clear increase in SR with decreasing temperature. This was most likely related to the stronger effect of depth (which is inversely related to boiom temperature) in Namibia In Angola and especially South Africa, the relazonship between SR quanzles and boiom temperature was less straighlorward

15 Horizontal maps showing quancles of spp. richness for encre study period according to GAMs Similar prediczons between approaches Most hotspot areas corresponded with furthest offshore extent of sampling (± shelf break) Most coldspot areas with the inshore areas RelaZonship not consistent throughout study domain: reversal of trend in central Angola, and presence of hotspot areas at intermediate depths in south of Namibia and north of South Africa Horizontal maps showing quancles of spp. richness for encre study period according to KED

16 Horizontal maps showing quancles of spp. richness for the smaller Cme periods in Namibia according to GAMs Persistence in the locazons of hotspots over Zme Horizontal maps showing quancles of spp. richness for the smaller Cme periods in Namibia according to KED

17 Horizontal maps showing quancles of spp. richness for the smaller Cme periods in (a) Angola, (b) South Africa according to GAMs

18 SUMMARY and CONCLUSIONS CauZon when interpolazng/prediczng biodiversity indices for un- sampled areas in a study grid, especially if using methods that do not take the spazal structure of data into account Coherence between different model prediczons (GAMs and Kriging) added confidence to prediczons The relazonship between species richness and physical/ environmental variables was inconsistent between countries, although generally hotspots of species richness were associated with greater depths and cooler boiom temperatures Despite changes in community structure and geographical shiks of species that have been documented during the study period, and which may be associated with climazc changes, this study showed the presence of consistently predictable hotspot areas over a year study period

19 ACKNOWLEDGEMENTS All co- authors especially Dawit Yemane The Norwegian Agency for Development CooperaZon is thanked for supporzng the NANSCLIM project and facilitazng meezngs of the demersal task team South Africa s Department of Agriculture Forestry and Fisheries, Namibia s Ministry of Fisheries and Marine Resources and Angola s InsZtuto Nacional de InvesZgação Pesqueira for data South Africa s Department of Environmental Affairs for supporzng SPKs parzcipazon here